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EP/G060045/1 Final Report
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Final report Title of Research Project: Thermal Management of Industrial Processes Grant Reference: EP/G060045/1 Programme: Energy Multidisciplinary Applications Call: Thermal Management in the Process Industries Organisation: The University of Machester
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Part I: Advanced Process Integration for Low Grade Heat Recovery
Ankur Kapil, Igor Bulatov, Robin Smith, Jin-Kuk Kim
Centre for Process Integration School of Chemical Engineering and Analytical Science
The University of Manchester Manchester, M13 9PL, UK
Part II: Environmental and Socio-Economic
Issues
Patricia Thornley, Conor Walsh, Paul Upham
The Tyndall Centre for Climate Change Research The University of Manchester
Manchester, M13 9PL, UK
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Abstract
A large amount of low-grade heat in the temperature range of 30 oC and 250 oC
are readily available in process industries, and wide range of technologies can be
employed to recover and utilize low-grade heat. However, engineering and
practical limitations associated with the integration of these technologies with the
site has not been fully addressed so far in academic and industrial communities.
Also, the integration of non-conventional sources of energy with the total site can
be a cost-effective and promising option for retrofit, however, carrying out its
design and techno-economic analysis is not straightforward, due to variable
energy demands. One of the key performance indicators for the evaluation and
screening of the performance of various energy saving technologies within the
total site is the potential of cogeneration for the site. A new method has been
developed by Centre for Process Integration (CPI), School of Chemical
Engineering and Analytical Science, to estimate cogeneration potential by a
combination of bottom-up and top-down procedures. In this work, the
optimization of steam levels of site utility systems, based on a new cogeneration
targeting model, has been carried out and the case study illustrates the benefits
of optimising steam levels for reducing the overall energy consumption of the
site.
There are wide range of low-grade recovery technologies and design options for
the recovery of low grade heat, including heat pump, organic Rankine cycle,
energy recovery from exhaust gas, absorption refrigeration and boiler feed water
heating. Simulation models have been developed for techno-economic analysis
of the design options for each technology and to evaluate the performance of
each with respect to quantity and quality of low grade heat produced on the site.
Integration of heat upgrading technologies with the total site has been studied
and its benefits have been illustrated with a case study for the retrofit design.
Over-the-fence heat integration for district heating (DH) can be suggested to
utilize the low grade waste heat and therefore alleviate the carbon footprint of the
integrated energy system. The economic performance of the over-the-fence
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process integration depends on the cost of fuel, electricity and distance for the
transfer of waste heat to DH network. A new design methodology has been
developed to systematically evaluate economic benefit of such the integration of
low grade heat with local district heating networks. A site-wide analysis tool using
site composite profiles is incorporated in the developed design method in order to
identify the quality and quantity of low grade heat available from the site. The
developed optimisation framework identifies economically acceptable distance
for the over-the-fence heat recovery from the industrial site to local community,
subject to economic parameters and engineering constraints. A case study has
been carried out to demonstrate the developed design methodology, and the
results from the case study illustrates techno-economic and engineering barriers
in practice for the implementation of low grade heat recovery beyond the site.
The Tyndall Centre for Climate Change Research undertook in task 7 of this
project to quantify the benefits of different process efficiency options and analyze
barriers to their practical implementation. This was achieved by completion of the
following research:
Evaluation of the barriers to process efficiency improvements generally
and utilisation of lwo grade heat particularly - described in section 9
Assessment of the environmental and economic performance of different
low grade heat recovery options – described in section 10
Assessment of the social aspects of low grade heat recovery, including
responses of potential heat users – described in section 11
An important part of this task has also been engagement with key stakeholders and
dissemination of the findings of the work. This is therefore detailed separately in
section 12. The key research outputs published in peer reviewed journals are
provided in the report.
The report provides final results of research carried out by The University of
Manchester team during the project duration from 15/09/2009 to 14/12/2011. The
CPI research group included Dr Jin-Kuk Kim (Principal Investigator, later moved
to a university in Republic of Korea), Professor Robin Smith (Principal
Investigator, initially Co-Investigator), Dr Ankur Kapil (researcher, 100% of full
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time) and Dr Igor Bulatov (project officer, 25% of full time). The Tyndall Centre
research group included Dr Patricia Thornley (Co-Investigator), Dr Conor Walsh
(researcher 100%), Dr Paul Upham (researcher 10%). Grant total £ 336,803.
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List of contents
Part I System-wide Modelling and Optimisation with Advanced Process
Integration for Low Grade Heat Recovery
1 Introduction ................................................................................................................. 9 2 Cogeneration potential .............................................................................................. 11 3 Optimization of steam levels .................................................................................... 18
Case Study Cogeneration potential ............................................................................... 21
4 Low Grade heat upgrade ........................................................................................... 28 4.1 Available technologies ....................................................................................... 28
4.1.1 Vapour compression heat pump.................................................................. 28 4.1.2 Absorption Systems .................................................................................... 29 4.1.3 Boiler feed water (BFW) heating ................................................................ 33 4.1.4 Organic Rankine Cycle (ORC) ................................................................... 33
4.1.5 Thermo-compressor .................................................................................... 34 4.1.6 Drying ......................................................................................................... 35
4.2 Algorithm ........................................................................................................... 35 4.3 Case study .......................................................................................................... 36 4.4 Results and discussions ...................................................................................... 39
4.4.1 Integration of heat pump ............................................................................. 39
4.4.2 Integration of Organic Rankine Cycle (ORC) ............................................ 43 4.4.3 Integration of Absorption refrigeration ....................................................... 46 4.4.4 Boiler feed water heating Integration ......................................................... 48
4.4.5 Comparison of design options .................................................................... 49 5 Over the fence process Integration ........................................................................... 50
5.1 Design Methodology .......................................................................................... 52 5.2 Modelling of energy equipment ......................................................................... 54 5.3 Optimization formulation ................................................................................... 55
5.4 Case Study 1: Integration of industrial waste heat with district heating (DH)
systems .......................................................................................................................... 59
5.4.1 Waste heat available in an industrial site .................................................... 60
5.4.2 District heating (DH) systems ..................................................................... 62 5.4.3 Feasible distance of heat transfer ................................................................ 65 5.4.4 Optimization Results ................................................................................... 65
5.5 Case Study 2: Integration of waste heat with a local energy systems ................ 70 6 Conclusions & future work ....................................................................................... 72 7 References ................................................................................................................. 74 8 Appendix A ............................................................................................................... 77
8.1 Optimization framework .................................................................................... 77
8.1.1 Objective function ....................................................................................... 77 Optimization constraints ........................................................................................... 79 8.1.2 Electric balances ......................................................................................... 79
8.1.3 Mass balances ............................................................................................. 79 8.1.4 Heat balance ................................................................................................ 81
8.2 Equipments ......................................................................................................... 82
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8.2.1 Multi-fuel boilers ........................................................................................ 82 8.2.2 Gas turbines (GT) ....................................................................................... 83 8.2.3 Heat recovery steam generators (HRSG) .................................................... 84 8.2.4 Electric motors (EM) .................................................................................. 85
8.2.5 Steam turbines (ST) .................................................................................... 85
Part II Environmental and Socio-Economic Issues
9 Barriers to Process Efficiency Improvements and Low Grade Heat Utilisation ...... 90 10 Environmental and Economic Analysis .................................................................... 92
10.1 Organic Rankine cycle integrated into a coke oven. ...................................... 93 10.2 Condensing boiler applied to woodchip combustion. .................................... 95
10.3 Heat pump for desalination ............................................................................. 97 10.4 District Heating............................................................................................... 99
11 Social aspects: perceptions of heat users ................................................................ 100
12 Conclusions ............................................................................................................. 103 13 Appendix B: List of Published Outputs .................................................................. 105
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Part I
System-wide Modelling and Optimisation with Advanced Process Integration for Low Grade Heat Recovery
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1 Introduction The typical sources of low grade heat are listed in Table 1. The opportunity
includes the waste heat recovery from liquids and gases, CHP (combined heat
and power), drying, steam generation and distribution and waste heat utilization.
The industrial application of low grade heat recovery is relevant to process
industries, including chemical, petroleum, pulp and power, food and drink,
manufacturing, iron and steel, and cement industries.
Table 1: Sources of low grade heat[1]
Opportunity Areas Industry
Waste heat recovery from gases and
liquids
chemicals, petroleum, forest products
Combined heat and power systems chemicals, food, metals, machinery,
forest products
Heat recovery from drying processes chemicals, forest products, food
processing
Steam (improved generation,
distribution and recovery)
all manufacturing
Energy system integration chemicals, petroleum, forest products,
iron and steel, food, aluminium
Improved process heating/heat transfer
systems (improved heat exchangers, new
materials, improved heat transport)
petroleum, chemicals
Waste heat recovery from gases in metals
and non-metallic minerals manufacture
iron and steel, cement
To avoid unnecessary capital expenditure for oversized equipment and to
enhance controllability of the energy systems, dynamic feature of the energy
supply and demand along with integration with energy recovery technology must
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be incorporated into the energy study in a systematic and holistic manner. The
implementation of these integrated energy saving projects within or beyond the
plant may not be favoured, due to practical constraints, for example,
considerable civil and piping works required, legislative limitations, different
energy utilisation patterns between sources and sinks, etc. Therefore, it is vital to
quantify the economic benefits of employing low grade energy recovery and its
impacts on the industrial site.
The integration of waste heat from the site utility system in a process industry
with a DH network is schematically shown in Figure 1. The integration of waste
heat with an existing DH network is evaluated in this work. However, a new DH
system can also be designed taking into account the waste heat from industry.
The barriers to the design and installation of new DH network were discussed in
the work of Davies et al.[2]. The regulatory framework, for the installation of a
new DH network, a financial barrier for raising money for new DH design, and a
commercial barrier related to the competitiveness of DH in regards to other
technologies are the three main factors that heavily influence on the installation
of the DH network in UK[2]. The impact of low grade heat transfer with an
existing DH network is dependent on the distance between the DH network and
the process industry, part load performance of the energy production equipment
in DH, etc.
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Figure 1: Process integration
2 Cogeneration potential The extent of heat recovery and cogeneration potential is closely related to the
configuration of site energy distribution systems in an industrial site, in which
multiple levels of steam pressure are introduced, for example, VHP (very high
pressure), HP (high pressure), MP (medium pressure) and LP (low pressure).
Steam levels and its corresponding pressure is an important design variable as
they can be adjusted to either minimize the fuel requirement or maximise profits
by exploiting site-wide trade-off of heat recovery and power generation.
Optimization of levels of steam mains is based on the manipulation of targeting
models for the cogeneration potential for the site utility systems.
Fuel
Fuel
FuelPROCESS A PROCESS B
Fuel
POWER
HIGH PRESS
MED. PRESS
LOW PRESS
PROCESS C
COOLING WATER
COND
Air
W
REFRIGERATION
District heating
Waste heat integration with District
heating
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The performance of the system can be either optimized to obtain the best design,
or to obtain the optimum operating conditions for an existing design, considering
the part load performance of the equipment based on the optimum number of
steam levels and their pressure. The simulation and optimization of the utility
systems require an accurate and yet simpler model for each element of the
system. Accurate estimation of the cogeneration potential is vital for the total site
analysis as it aids in the evaluation of performance and profitability of the energy
systems. The overall cost-effectiveness of power and heat from the site is heavily
influenced by the optimum management and distribution of steam between
various steam levels. Furthermore, optimum import and export targets for
electricity can be obtained from steam levels, load and price of fuel and
electricity. Also, energy efficiency for the utilisation of low grade heat will be
strongly influenced by operating and design conditions of existing energy
systems. Therefore, the accurate estimation of cogeneration potential is essential
for performing a meaningful economic evaluation of the design options
considered for heat upgrading and/or waste heat recovery.
A number of methods are available in the literature for estimating the
cogeneration potential of utility systems. The ideal shaftpower is calculated as
the exergy change of the steam passing the turbine[3]. The exergetic efficiency is
considered to be independent of the load and inlet-outlet conditions, and is
assumed to be a constant value. The steam conditions are approximated by the
saturated conditions, but the superheat in the inlet and outlet steam conditions
are neglected[4]. There is a difference of up to 30% in cogeneration potential in
comparison with simulations based on THM (turbine hardware model) developed
by Mavromatis and Kokossis[5].
Salisbury[6] observed that the specific enthalpy of steam (i.e. enthalpy per unit
mass flow) is approximately constant for all exhaust pressure values[7]. There is
a linear correlation between specific power w (power per unit mass flowrate of
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steam) produced in the turbine and the outlet saturation temperatures. The
specific power corresponds to the area of the rectangle on a graphical
representation of the inlet and outlet saturation temperatures of the turbine with
respect to the heat loads of steam. This methodology is based on the following
assumptions: specific load (q) of steam is constant with variation in exhaust
pressure and specific power is linearly proportional to the difference of inlet and
outlet saturation temperatures.
Mavromatis and Kokossis[5] proposed a new shaftpower targeting tool called the
turbine hardware model (THM) based on the principle of Willans line. Willans line
approximates a linear relationship between steam flowrate and the power output.
THM has limitations as Varbanov addressed[8]: the effect of back pressure is not
taken into account, and modelling assumptions for part-load performance are too
simplistic, such that the model assumes a linear relationship over the entire
range of operation.
Sorin and Hammache[9] introduced a different targeting method based on
thermodynamic insights and Rankine cycle. The ideal shaftpower is a function of
outlet heat loads and the difference in Carnot factor between the heat source and
heat sink. The deviation of the actual expansion from the ideal expansion is
defined in terms of isentropic efficiency.
New Method
Cogeneration targeting in utility systems is used to determine fuel consumptions,
shaftpower production and cooling requirements before the actual design of the
utility systems[9]. The previous methods available in literature have the following
drawbacks. TH model does not consider the contribution of superheat in the inlet
and the outlet stream in the power generation. THM parameters are based on
regression parameters derived from a small sample of steam turbines, and
consequently are not applicable for all the possible sizes of turbines.
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In order to overcome shortcomings of previous methods, new method for
cogeneration targeting has been proposed in this work, and isentropic efficiency
is used in the new targeting method.
TH model for targeting does not include the superheat conditions at each level
which results in significant error for estimating cogeneration potential. THM
model uses an iterative procedure based on specific heat loads to calculate the
mass flowrate for the turbines. The calculation of flowrates in Sorin’s
methodology is based on the flow of energy. Power produced by the system is
estimated with the isentropic efficiency, available heat for power generation and
inlet and outlet temperatures of Rankine cycle. However, there is no justification
for the assumption that thermodynamic behaviour of all the steam turbines to be
used acts as that of the Rankine cycle.
The new algorithm calculates the minimum required flowrate from steam
generation unit (e.g. boiler) and the levels of superheat at each steam main
based on the heat loads specified by site profiles of heat sources and sinks.
The algorithm for the new procedure is given in Figure 2. The superheat
temperature calculation at each steam level is made, starting with a certain
superheat temperature of the steam from the boiler. The procedure is based on
the assumption that steam supplied to the site utility systems from a boiler is at
the superheated conditions required as VHP steam level. Figure 3 shows the
temperature entropy diagram for the process. The initial conditions of
superheated steam at higher pressure and temperature level are represented by
point 1. The steam at lower pressure level for an isentropic expansion is shown
as Point 2’ on the curve. Isentrotpic expansion with an efficiency of x% is used to
determine the enthalpy at point 2. It is assumed during targeting stage that all the
steam turbines are operating at their full load. The cogeneration potential of the
system is dependent on the expansion efficiency of x. This parameter is
dependent on the capacity of the turbine and detailed calculation is given below.
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Steam properties are calculated for the given entropy and pressure at the lower
steam level. If the degree of superheat in the resulting LP steam main is less
than required, then operating conditions of VHP is updated and then re-iterates
the procedure above until the acceptable superheated conditions for LP steam
main is met.
Figure 2: Algorithm for new method based on isentropic expansion
Given steam levels, inlet superheat of VHP steam, process load, BFW, Condensate temperature
Isentropic efficiency
Calculate superheat temperatures at subsequent lower steam level using isentropic efficiencies (Equation 2)
Starting from the lowest level, calculate the mass flow rates using Equation 1.
Add flow rates to determine the overall flow rates through each level (bottom up)
LP superheat temperature > LP
saturation temperature + T*
YES
STOP
Increase Boiler VHP superheat
NO
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Figure 3: Temperature Entropy diagram for change in level
In the bottom up procedure, the temperature of the lowest steam level pressure
is first used to calculate the steam mass flowrate for the expansion of steam
between the lowest steam level and the higher pressure next to the lowest one.
This procedure is sequentially repeated until the interval for highest steam
pressure level. Flowrates at the higher levels are determined from the flowrate in
the lower levels. The flowrate of steam for each expansion interval is a function
of the heat load at that level and the enthalpy change to the condensate
temperature at the given level. Superheated steam is condensed and supplied to
downstream processes at condensate temperature of the steam.
H
Qm
1
Where,
m = mass flow rate
Q = heat load for a given level
H = Enthalpy change from superheat conditions at the given level to
condensate conditions at that pressure
T
S
1
2’
2
P1
P2 Real
Isentropic
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Isentropic efficiency calculation
It is designer’s discretion to use the most appropriate value of isentropic
efficiency for the developed cogeneration targeting method presented in this
paper. On the other hand, information of isentropic efficiency available in the
literature can be also used. Mavromatis and Kokossis[5] developed a
thermodynamic model to estimate the isentropic efficiency of single and multiple
extraction turbines. Varbanov et al.[10] presented equations to determine the
parameters in terms of saturation temperature. Medina-Flores and Picón-
Núñez[11] modified the correlations of Varbanov et al.[10] to obtain the
regression parameters as a function of inlet pressure. The regression parameters
obtained by Varbanov et al.[10] from the turbine data of Peterson and Mann[12]
are shown in Table 2.
max,
max
is
isW
W
B
AWW is max,
max
satTbbA 10
satTbbB 32
2
Where,
is = isentropic efficiency
isH = isentropic enthalpy change
3210 ,,,,, bbbbBA = Regression coefficients
satT = Inlet pressure of the steam
Table 2: Regression coefficients for single extraction turbines[13]
Single extraction back pressure turbines
Wmax< 2 MW Wmax >2 MW
0b (MW) 0 0
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The results are investigated with STAR®, which is Process Integration software
for the design of utility systems for a single process or a group of processes
involving power (electricity) and heat (steam) generation, and associated heat
exchanging and distributing units. The design procedure of utility systems in
STAR® requires information about steam flowrates, heat supply and loads, VHP
(very high pressure) steam specification (e.g. VHP steam generation capacity
and temperature at the outlet of the boiler). At the initial targeting stage, some of
these design parameters are not known. The parameters, such as flowrate from
the boiler, steam level conditions, have to be specified for the detailed design in
STAR®. The information required for the calculation of cogeneration potential
from the utility systems is current flowrate of steam generated, maximum and
minimum flow rates of equipment, thermodynamic model and efficiency of steam
turbines, steam demand and surplus for each steam main, superheat condition of
steam generated from the boiler, etc. STAR® has two models isentropic and THM
model for the calculation of power generation of steam turbine in the detailed
design, while it uses TH and THM model for cogeneration targeting.
3 Optimization of steam levels As explained before, the choice of steam level in the design of site utility systems
are critical to ensure cost-effective generation of heat and power, and its
distribution in the site. In a new design, pressures of steam level can be readily
optimized. However, for the retrofitting of existing systems, opportunities for the
change of steam level conditions are limited. The mechanical limitation for the
steam mains limits a significant increase in steam pressure. However, long term
investment with a proper optimization of the steam levels may be economically
1b (MWoC-1) 0.00108 0.00423
2b 1.097 1.155
3b (oC-1) 0.00172 0.000538
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viable in spite of the fact that the short term investment can not be justified[14].
VHP steam generation in the boiler and hence the fuel costs in the utility boilers
can be decreased by increasing number of steam mains which increases the
heat recovery potential. Number of steam mains has a significant impact on the
cogeneration potential. Therefore, to minimise fuel cost with maintaining high
cogeneration potential, the design should be thoroughly investigated.
Optimization model
In this study, the optimisation framework for determining the cost-effective
conditions of steam mains for the site utility systems had been proposed with
incorporating new cogeneration targeting method proposed in the work. The
optimisation model is formulated in an NLP (non-linear programming) problem
and the details of models are as follows:
Objective Function
The objective function is to minimize the amount of hot utility to be supplied from
the steam generation unit (e.g. boiler). It should be noted that the method
presented in this paper is generic for taking different objective functions, for
example, overall fuel cost, operating profit, etc, as long as the relevant cost
parameters are available.
minimise VHPsourceheatVHPkshifted HH ,,sin
VHPkshiftedH ,sin Enthalpy of shifted heat sink for VHP
VHPsourceheatH , Enthalpy of heat source for VHP
Optimization Variables
iP Pressure at i Steam levels (VHP, HP, MP, LP)
Four steam mains are used in the current optimisation model, as this is most
common in the large-scale industrial plant, while different number of steam
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mains, for example, three levels (HP, MP and LP), can be considered based on
needs and operating characteristics on the plant.
Constraints
Total source and sink profiles are generated from stream data of the site. Design
procedure for manipulating stream data to generate the site profiles is not a part
of this study and those details can be found from Smith [14] and Klemes et al,
[15]. In order to maintain feasibility of heat recovery across steam mains,
constraint between sink and source site profiles is needed. First, the sink is
shifted until the enthalpy of heat source at either of steam levels is the same as
the enthalpy of heat source corresponding to the pinch point, and then enthalpy
difference at each steam levels is always greater than zero.
0,,sin isourceheatikshifted HH i Steam levels (VHP, HP, MP, LP)
Mass balance
The mass flow rate of steam between steam levels is given:
j
j
kjjiH
Qmm
Where,
jim Mass flow rate of steam through turbine between i and j steam levels
kjm Mass flow rate of steam through turbine between j and k steam levels
jQ Heat duty at j steam level
jH Enthalpy extracted by process from superheated steam at j level to reach
condensate conditions
Power is calculated base on the new design algorithm as shown in Figure 2.
Figure 4 shows the model for the determination of optimal steam pressure levels
for a site utility system. The change in the steam pressure levels shifts the site
sink and surplus profiles along with heat demand and supply. Cogeneration
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potential for the site composite is calculated from the new algorithm. The process
is repeated until optimum pressure levels corresponding to minimum value of
objective function are found for the site.
Figure 4: Flowchart to determine optimum steam pressure level
Case Study Cogeneration potential
An illustrative case study is used to test the different methodologies. The four
steam levels considered in this example are very high pressure (VHP), high
pressure (HP), medium pressure (MP), low pressure (LP) at 120, 50, 14 and 3
bar(a) respectively. The heat demand at HP, MP and LP steam levels is 50, 40
and 85 MW respectively. The efficiency of the boiler is assumed to be 100% for
the simplicity, which can be updated, according to boiler data available, and it is
supplying steam at a temperature of 575oC. Water supplied to the boiler and the
condensate returns are both assumed to be at a temperature of 105oC.
In this work, cogeneration targeting methods have been applied to the case
studies with only back pressure turbines. However, it can be easily extended to
condensing turbines. One of the additional constraints on condensing turbine is a
maximum wetness permitted at the exhaust. Wetness factor in the condensing
turbine can be controlled by adjusting the superheat in the steam mains, as
similary treated in the consideration of degree of superheat in LP steam.
New steam level pressure
Calculate shifted sink and source profiles & heat surplus or
deficit at each steam level
Cogeneration potential calculation from new algorithm
Minimum Utility requirement Optimum
Pressure
NO YES
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Table 3: Problem Data Parameters
VHP HP MP LP
Pressure (bara) 120 50 14 3
Saturation
Temperature (°C) 324.7 264 195.1 133.6
Heat Demand
(MW) 0 50 40 85
The isentropic efficiency was calculated as given in Equation 2, while the
mechanical efficiency was assumed to be 100%.
TH Model: The shaftpower targets from TH method are shown in Table 4. The
overall shaftpower calculated from TH model is 33.02 MW. The value of
conversion factor (CF) is assumed to be 0.00135.
THM Model: The targets for the three sections VHP-HP, HP-MP and MP-LP for
THM model are 9.4, 4.7 and 0 MW respectively (Table 4). The overall shaftpower
target from THM model was 14.2 MW.
Sorin’s Methodology: The work in the bottom section is used to calculate the
heat load in subsequent top section as described in the methodology in the
previous section. Shaftpower targets for VHP-HP, HP-MP and MP-LP of 18.2,
14.46 and 8.77 MW are shown in Table 4.
New Method
Table 4 shows the shaftpower targets for VHP-HP, HP-MP and MP-LP sections
of 14.99, 14.37 and 9.75 MW respectively. The main difference between the new
method and existing TH and THM model is the calculation of superheat
temperature for each steam main, as explained previously. Superheat
temperature of the outlet LP steam should be greater than saturation
temperature of LP steam to avoid condensation of vapour at the outlet of turbine
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and thereby reduced performance and efficiency. The amount of superheat in
VHP steam determines the superheat in LP steam. In the new algorithm, the
superheat in VHP steam from the boiler is a variable and is adjusted by trial and
error to ensure the superheat in LP steam.
Figure 5: Results of the new method
STAR® Simulation – Constant Isentropic Efficiency
Once the steam levels and the heat surplus and deficit are known, a detailed
design procedure is used for the optimal design of the utility systems or to find
out the optimum operating conditions for an existing design. However, as
discussed before, the detailed design requires some additional parameters such
as flowrates and superheat steam temperatures. These additional parameters
are specified by trial and error. STAR® was used to test the targeting potential
against the actual production from the steam turbine. The shaftpower was
calculated by the isentropic model with isentropic efficiency calculated as shown
in Equation 2. The utility systems consist of a boiler supplying VHP steam at
575oC. The steam is passed from the boiler to the higher pressure steam main to
lower pressure steam main, via a steam turbine. Any unused steam can be
passed through the vent. The process cooling and heating duty at each steam
VHP Supply
Qusage = 85 MW
Qusage = 40 MW
Qusage = 50 MW
Heat Demand (MW)
VHP
HP
MP
LP
Satu
rati
on
tem
per
atu
re (
C)
14.99 MW
14.37 MW
9.75 MW
248.29 t/h
185.89 t/h
130.7 t/h
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main level is specified as given in Figure 4. The overall turbine shaftpower is
39.12 MW.
Comparison of Cogeneration Targeting Results
Table 4 shows a comparison of cogeneration targeting results from Sorin’s
methodology, new method, TH and THM model in STAR®. A detailed design
simulation in STAR® with the constant isentropic method is used to compare the
shaftpower targets from the different methodologies. As shown in Table 4 the
total power target of 41.43 MW from Sorin’s methodology is significantly different
from the detailed design procedure of 39.0 MW with an error of 6.2%. The
shaftpower target obtained from TH model of 33.02 MW is 15.3% different from
the shaftpower obtained from the detailed design procedure. Similarly, THM
model target is 63.85% different from the actual shaftpower from the detailed
design procedure. These discrepancies in the shaftpower targets are due to the
assumptions used in these models. The shaftpower target obtained from the new
method of 39.12 MW is only 0.31% different from the detailed design procedure
in STAR®.
Figure 6: STAR® simulation isentropic efficiency
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Table 4: Comparison of cogeneration targeting results
Optimization of steam levels
Site data was taken from an example available in the literature [16]. Site sink and
source profile is shown in Figure 7. Four steam mains are available at very high
pressure (VHP), high pressure (HP), low pressure (LP) and medium pressure
(MP) respectively. Sink profile is shifted by the minimum of the enthalpy
difference between the source and sink, which identifies site pinch point for the
utility system.
0
50
100
150
200
250
300
-500 -400 -300 -200 -100 0 100 200 300 400
Enthalpy (MW)
Tem
pera
ture
(o
C)
Sink
Source
Shifted Sink Profile
Figure 7: Sink and source profiles for a given site
The site utility grand composite curve (SUGCC) plots the difference between the
hot and the cold composite curves as shown in Figure 8. The heat generation
and use at individual steam level is shown in Figure 8-Figure 11. Figure 9 plot the
Methodology Total
(MW)
VHP-HP
(MW)
HP-MP
(MW)
MP-LP
(MW)
Sorin’s methodology 41.43 18.2 14.46 8.77
New Method 39.12 14.99 14.37 9.75
TH Model in STAR® 33.02 14.35 11.62 7.06
THM Model in STAR® 14.1 9.4 4.7 0
STAR® Simulation – Constant
Isentropic Efficiency
39.0 14..85 14.78 9.37
EP/G060045/1 Final Report
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cogeneration potential between different steam levels as expansion zones for
steam turbines. The power output for these zones for the optimized case, based
on the new algorithm, is found to be 7.69 MW.
0
50
100
150
200
250
300
350
400
0 20 40 60 80 100 120 140 160 180 200
Enthalpy (MW)
Tem
pera
ture
(o
C)
Figure 8: Site Utility Grand Composite Curve with the optimum steam levels
0
50
100
150
200
250
300
350
400
0 10 20 30 40 50 60 70 80
Enthalpy (MW)
Tem
pera
ture
(o
C)
Figure 9: Site Utility Grand Composite Curve with cogeneration areas
0
50
100
150
200
250
300
350
400
-500 -400 -300 -200 -100 0 100 200 300 400
Enthalpy (MW)
Tem
pera
ture
(o
C)
Sink
Source
Figure 10: Site profile targets for steam generation and steam usage
EP/G060045/1 Final Report
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0
50
100
150
200
250
300
350
400
-450 -400 -350 -300 -250 -200 -150 -100 -50 0 50 100
Enthalpy (MW)
Tem
pera
ture
(o
C)
Figure 11: Site profile with cogeneration potential area
The objective function is the minimization of the utility cost. The hot utility is
supplied as VHP steam from the boiler. The optimization framework described in
previous section and the model calculations are performed in Microsoft Excel®.
The size of the model and the optimization problem is small and therefore solver
function in Microsoft Excel® can be effectively used for the minimization of the
utility cost. The number of steam levels has been assumed constant as four
corresponding to VHP, HP, MP and LP respectively. Steam pressures at each
level are the design variables. They affect both the level of heat recovery and the
cogeneration potential, via the steam turbine network[14].
Table 5 shows the base case conditions for the four steam levels. Optimum
steam level pressure and temperature along with heat load at each level is
shown in Table 6. The optimum pressure in the steam mains for the lowest utility
cost are 180, 46.55, 12.26 and 2.25 bar in the VHP, HP, MP and LP steam loads
respectively. The minimum VHP steam generation required from the boiler is
70.22 MW, while the VHP steam flowrate requirement from the boiler is 88.16
t/hr. Steam generation required at VHP mains has been reduced from 105.20
MW to 70.22 MW for the optimized case. However, the cogeneration potential
reduced from 8.8 MW for base case to 7.67 MW for the optimized case.
Therefore, increasing the heat recovery reduces the steam generation from the
boiler as well as the cogeneration potential for this particular example. If power
generation in the site should be increased, then additional VHP steam is
generated to pass through steam mains.
EP/G060045/1 Final Report
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Table 5: Base case steam levels[16]
Pressure (bar) Temperature (oC) Heat Load (MW) Saturation temperature (
oC)
180 625 105.20 357.14
50 458.74 137.01 264.09
10 322.1 125.29 180.04
2 143.63 81.98 120.36
Table 6: Optimized steam levels
Pressure (bar) Temperature (oC) Heat Load (MW) Saturation temperature (
oC)
180 625 70.22 357.14
46.65 449.21 113.45 259.79
12.26 308.08 107.57 189.09
2.25 214.48 55.34 124.10
This optimisation framework can be extended to accommodate other economic
scenarios (e.g. to minimise the fuel costs with maintaining the same cogeneration
potential) or practical constraints (e.g. the number of steam levels allowed).
4 Low Grade heat upgrade
4.1 Available technologies
Low grade heat source can be very useful to provide energy to the heat sink by
upgrading low-grade energy (e.g. low pressure steam). The upgrade of low grade
heat can be carried out by heat pump, absorption refrigeration, thermo
compressor, etc, by recovering and/or upgrading waste heat from various
sources (e.g. gas turbine exhaust) and utilising them with the wide range of
applications (e.g. drying and boiler feedwater heating).
4.1.1 Vapour compression heat pump
Heat pump transfers the low grade heat at the lower temperature to higher
temperature heat by the compressor. Heat pump has been used in petroleum
refining, and petrochemicals, wood products, pharmaceuticals, utility system etc.
[17]. Figure 12 shows a typical closed cycle heat pump. The heat from lower
temperature source is transferred to the working refrigerant in the evaporator.
Electric or mechanical energy is used in the compressor to increase the pressure
of the vapour from the evaporator. High grade heat at higher temperature is
EP/G060045/1 Final Report
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released from the condenser. Pressure of the vapour is reduced by throttle valve
to lower its temperature and convert it to liquid to exchange heat with low grade
heat source. The main issue with the utilization of the heat pump is that it uses
expensive external energy to convert low grade heat into high grade heat. In
general, one unit of high grade electrical energy can produce 2-4 units of high
grade thermal energy.
Figure 12: Heat pump cycle [18]
Co
E
Q
QCOP
3
Where,
COP = coefficient of performance
EQ = Heat received at low temperature by the evaporator
CoQ = Electric power supplied in the compressor
4.1.2 Absorption Systems
Low grade heat can be recovered by absorption with three different types of
equipments absorption refrigeration, absorption heat pump and absorption
transformers respectively. Iyoki and Uemura [19] compared the performance of
Condenser
Compressor Prime
Mover
Evaporator
Heat from lower temperature source
Throttle
valve
Mechanical
work input
EP/G060045/1 Final Report
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absorption refrigeration, absorption heat pump, and absorption transformer for
water-lithium bromide zinc chloride calcium bromide system.
a) Absorption refrigeration – There has been extensive work in literature on
absorption refrigeration system, with both experimental [20] and simulation
studies [21, 22] to determine the performance of absorption refrigeration. A
schematic diagram of ammonia-water absorption refrigeration cycle is shown in
Figure 13. Ammonia vapour at high pressure transfers heat to neighbourhood in
the condenser. Liquid ammonia from the condenser is passed through an
expansion valve to reach the evaporator pressure. Heat is transferred from the
low temperature heat source to convert liquid ammonia to vapour state.
Ammonia vapour is absorbed by a weak solution of water and ammonia to form a
concentrated solution of ammonia-water at the bottom of absorber. This
concentrated solution is passed to the generator for the production of ammonia
vapour while the lean solution from the generator is passed back to the absorber
unit. Low grade heat is used in the generator for the production of ammonia
vapour. Lean ammonia solution from the generator exchanges heat with the high
concentration ammonia solution from the absorber.
Figure 13: Ammonia water absorption refrigeration cycle [20]
EP/G060045/1 Final Report
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The coefficient of performance for an absorption refrigeration system is defined
as the ratio of heat removed from the evaporator to heat supplied in the
generator.
G
E
Q
QCOP
4
Where,
COP = coefficient of performance
EQ = Heat received at low temperature by the evaporator
GQ = High temperature heat used in the generator
b) Absorption heat pump – A single stage absorption heat pump consists of a
generator, absorber, evaporator, condenser and heat exchanger. High grade
heat is supplied at higher temperature to the generator to separate the refrigerant
from the solution. Low grade waste heat is supplied to the evaporator, while
medium temperature heat is released from the condenser. Thermal energy at
higher temperature is used to convert low grade heat into high grade heat.
Coefficient of performance of an absorption heat pump is the ratio of heat
removed from the medium temperature heat removed form the absorber and
condenser to the high grade heat supplied in the generator.
G
CA
Q
QQCOP
5
Where,
COP = coefficient of performance
AQ = Heat released by the absorber
CQ = Heat released by the condenser
GQ = High temperature heat used in the generator
c) Absorption heat transformer – The basic schematic diagram of absorption
heat transformer is shown in Figure 14. Absorption heat transformer consists of
the same units as absorption heat pump. However, the main difference is that
EP/G060045/1 Final Report
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evaporator and absorber are maintained at a higher pressure, while in absorption
pump they are at a lower pressure. Low grade heat is used in the generator and
evaporator to produce heat at higher temperature in the absorber. The process
can be described briefly as follows: High pressure refrigerant vapour from an
evaporator is absorbed into the lean refrigerant absorbent solution in the
absorber. High pressure strong solution of refrigerant absorbent is passed via a
throttle valve to reduce the pressure. This solution exchanges heat with weak
solution from a generator, before it reaches the generator. Low temperature heat
in the generator is used to separate the refrigerant from the solution. Refrigerant
vapour from the generator is condensed in a condenser. The refrigerant is
subsequently pumped to higher pressure where it gains heat at low temperature
to convert into vapour.
Figure 14: Absorption heat transformer (Ammonia water)
The ratio of high temperature heat from the absorber to the low grade heat
supplied in the generator and evaporator is defined as the coefficient of
performance of absorption transformer.
EG
A
QCOP
6
Where,
Heat Exchanger
Absorber Evaporator
Generator Condenser
EP/G060045/1 Final Report
33
COP = coefficient of performance
AQ = Heat released by the absorber
EQ = Heat consumed in the evaporator
GQ = High temperature heat used in the generator
4.1.3 Boiler feed water (BFW) heating
Low grade heat can be used to increase the temperature of make-up water to
reduce the fuel cost in the boiler. Additional heat exchanger capital cost is
required for exchange of heat between the boiler make up water and low grade
heat. The increase in temperature of make up water using low grade heat
decreases the fuel consumption in the boiler.
4.1.4 Organic Rankine Cycle (ORC)
A Rankine cycle for extracting electricity from waste heat sources is possible with
the use of organic fluids as working fluids. Efficiency of operation of Rankine
cycle depends on conditions of the cycle and working fluid. A typical organic
Rankine cycle consists of an evaporator, turbine, condenser and pump
respectively (Figure 15). Organic fluid such as benzene, toluene, p-xylene and
refrigerants R113 and R123 [23] have been used as working fluids in ORC.
Working fluid vaporises by exchanging heat with low grade heat in the
evaporator. Vapour is passed through turbine for generation of electricity. Vapour
is condensed in condenser at lower temperature and releases heat to the outside
atmosphere. Organic fluid is raised from lower pressure to high pressure in the
pump. The amount of energy consumed in pumping the fluid is considerably low.
EP/G060045/1 Final Report
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Figure 15: Organic Rankine Cycle (ORC) Efficiency of ORC is defined as the ratio of power generated by the turbine to the
low grade energy supplied in the evaporator.
E
turbORC
Q
P
7
Where,
ORC = Efficiency of ORC
EQ = Heat received at low temperature by the evaporator
turbP = Electric power generated by the turbine
4.1.5 Thermo-compressor
Thermo-compressor uses high pressure steam to compress low or intermediate
pressure waste steam into medium pressure steam. Figure 16 shows a thermo-
compressor where high pressure steam enters as a high velocity fluid, which
entrains the low pressure steam by suction. The resulting mixture is compressed
and discharged as a medium pressure steam from the divergent section of the
thermo-compressor. The main advantage of thermo compressor is high reliability
and less compression power requirement.
Turbine
Pump
Condenser
EP/G060045/1 Final Report
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Figure 16: Thermo compressor1
4.1.6 Drying
Biomass (wood, bagasse, grass, straw, agriculture residues, etc.) have
significant amount of moisture. This moisture reduces the theoretical flame
temperature as a part of heat of combustion is used in evaporation of moisture
from the biomass [24]. Calorific value and theoretical flame temperature from the
biomass fuels can be increased by drying. Effective use of industrial waste heat
in drying of biomass increases the overall efficiency of the process, leading to
significantly lesser amount of fossil fuel to be burned and hence much less green
house emissions.
4.2 Algorithm
Once the number of steam levels and their pressure has been determined by
optimization in total site profiles, the performance of the system can be either
optimized to obtain the best design, or to obtain the optimum operating
conditions for an existing design, considering the part load performance of the
equipment. The simulation and optimization of the utility systems require
accurate and yet simpler model for each element of the system. Varbanov [10]
and Aguillar [25] developed simple models for the equipments in the utility
systems. Models developed by Aguillar [25] have been adopted for the purpose
of optimization which determines the optimum design (i.e. the configuration of
utility systems) or operating conditions in this work (Appendix A).
1 (http://www.em-ea.org/Guide%20Books/book-2/2.8%20Waste%20Heat%20Recovery.pdf)
EP/G060045/1 Final Report
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The algorithm for evaluation of integration of low grade heat upgrade
technologies with an existing site utility system is shown in Figure 17. The
characteristics of low grade energy such as available heat load at temperatures
for use in heat pump, ORC, and boiler feed water heating is obtained from total
site sink and source profiles. HYSYS simulation is used to obtain the
performance indicators such as COP, efficiency, purchase cost etc. for low grade
heat upgrade technology. Heat load is varied for the HYSYS simulation to
calculate the change in performance and purchase cost. This information is fed
to the optimization framework for calculating the overall annual cost with
integration of these design technologies. The optimization framework [25] is used
for minimization of overall annual cost or operating cost minimization for a
multiperiod operational, retrofit or grassroots design problem. Linear models
have been derived for all the energy equipments so that MILP optimizers can be
used for optimization to reduce the computational cost.
Figure 17: Algorithm for evaluation of low grade heat upgrade technology
4.3 Case study
The various design options for low grade heat upgrade are evaluated with the
help of a case study. The base case design is shown in Figure 18. The base
design consists of four boilers each with capacity of 40 kg/s. There are four back
EP/G060045/1 Final Report
37
pressure turbines for generation of electricity from VHP to HP and one back
pressure turbine between HP and LP steam levels. Two multistage turbines are
available for expansion of steam between HP-MP and MP-LP respectively. Four
mechanical pumps having a steam turbo driver and an electric motor supply the
feed water to the boiler.
Figure 18: Base case design [25] Site data for heat load, electricity demands, pump electricity demand,
condensate return and cooling water is shown in Table 7. The site operating
seasons are divided into two major categories summer and winter, with 67% of
year as winter. The ambient temperature, relative humidity, electricity natural gas
and fuel oil price is shown in Table 8. The total number of working hours for the
site is assumed to be 8600 hrs per year. The latent heat values for fuel oil and
natural gas are 45 and 50.24 MJ/kg respectively.
Table 7: Total site data - Requirements for the utility system
Units Winter Summer
Electricity demand MW 62 68
EP/G060045/1 Final Report
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VHP steam demand
MW 116.36 110.82
HP steam demand MW 30.61 21.4
MP steam demand
MW 16.67 9.34
LP steam demand MW 88.54 73.62
Total steam demand
MW 252.17 215.17
Condensate return % 80 80
Power Pump 1 MW 5.2 5.0
Power Pump 2 MW 1.3 1.1
Power Pump 3 MW 2.2 2.0
Power Pump 4 MW 0.6 0.6
Process CW demand
MW 200 300
Table 8: Site conditions
Season Units Winter Summer
Fraction of the year % 67 33
Ambient temperature
oC 10 25
Relative humidity % 60 60
Electricity prices Peak ($/kWh) 0.07 0.08
Off- Peak ($/kWh) 0.05 0.05
Peak hours /day Hrs 7 12
Fuel Oil price $/kg 0.19 0.19
Natural gas price $/kg 0.22 0.22
Raw water price $/ton 0.05 0.05
Grand composite curves (GCC) of the individual process are modified by
removing the pockets corresponding to additional heat recovery within the
process. These modified process GCC are then combined together to form the
total site sink and source profile (Figure 19(a)). Sink profile is shifted until the
source and shifted sink profile touch each other (Figure 19(b)) or the source and
the sink steam generation and consumption lines touch each other
corresponding to site pinch. Site utility grand composite curve (SUGCC)
represents the horizontal separation between the source and the sink. Steam
demand at VHP, HP, MP and LP levels are 110.8, 21.4, 9.3 and 73.6 MW
respectively. Power generation potential is represented as areas in SUGCC with
EP/G060045/1 Final Report
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VHP-HP, HP-MP and MP-LP cogeneration potential of 79.8, 58.4 and 49.1 MW
respectively (Figure 19(c)).
-400 -300 -200 -100 0 100 200 300 4000
50
100
150
200
250
300
350
Enthalpy (MW)
Tem
pera
ture
(oC
)
-400 -300 -200 -100 0 100 200 300 4000
50
100
150
200
250
300
350
Enthalpy (MW)
Tem
pera
ture
(oC
)
(a) (b)
0 50 100 150 200 2500
50
100
150
200
250
300
350
Enthalpy (MW)
Tem
pera
ture
(oC
)
(c)
Figure 19: Site composite curves; (a) Site source and sink composite curve (b) Site source and shifted composite curve with the cogeneration potential area (c) Site utility grand composite curve (SUGCC)
4.4 Results and discussions
4.4.1 Integration of heat pump
HYSYS model heat pump
A model of heat pump has been simulated in HYSYS. It consists of four
equipments evaporator (E-102), compressor (K-100), condenser (E-100) and a
throttle valve (VLV-100). Refrigerant R112-a is used as a working fluid. Low
grade heat is supplied in the evaporator at the temp of 115oC. High grade electric
energy is used in the compressor to raise the pressure of the vapour. LP steam
EP/G060045/1 Final Report
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is generated from the condenser at temperature of 150oC. Throttle valve is used
to reduce the pressure of the vapour liquid mixture from the condenser.
Figure 20: Vapour compression heat pump
Figure 21 shows the variation of COP for heat pump system with respect to
variation in the evaporator duty. COP varies within a small range from 3.24-3.31
and can be assumed to be constant for the refrigerant (R-112a) and the
corresponding heat pump cycle (Figure 20). COP of 3.3, means that 1 MW of
electric energy and 2.3 MW of low grade energy generate 3.3 MW of high grade
energy.
EP/G060045/1 Final Report
41
3.23
3.24
3.25
3.26
3.27
3.28
3.29
3.3
3.31
0 10000 20000 30000 40000 50000 60000 70000 80000 90000
Evaporator Duty
CO
P
Figure 21: COP with respect to evaporator duty
Purchase cost of heat pump
Purchase cost of heat pump is calculated as the sum of the cost of evaporator,
condenser, and compressor. Purchase cost of heat pump is approximated based
on a linear correlation between the cost and the evaporator duty.
BHAPC evapumpheat 8
Where,
pumpheatPC = Purchase cost heat pump
evaH = Evaporator duty (MW)
A, B = Regression coefficients
A = 0.1 MM$/MW
B = 1.15 MM$
Purchase cost
y = 0.0001x + 1.1491
0
2
4
6
8
10
12
0 10000 20000 30000 40000 50000 60000 70000 80000 90000
Evaporator duty (kW)
Pu
rch
ase C
ost
(MM
$)
Figure 22: Linear correlation between purchase cost and evaporator duty
EP/G060045/1 Final Report
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The total site source and sink profile before and after integration of the heat
pump is shown in Figure 23 and Table 9. LP steam demand changes from 73.62
to 18.67 MW in summer and from 88.54 to 33.59 MW in winter. Low grade heat
is extracted from the site source only till 115oC corresponding to temperature diff
of 10oC in the evaporator of the heat exchanger. COP of heat pump as
calculated from HYSYS simulations is 3.3. Therefore, the external electricity
consumption from the site increases as shown in Table 10 from 68.82 to 85.42
MW in summer and from 62.2 to 78.8 MW in winter.
Table 9: LP steam demand before and after integration of heat pump
Summer (MW) Winter (MW)
Before heat pump 73.62 88.54
After heat pump 18.67 33.59
Table 10: Electricity demands before and after integration of heat pump
Summer (MW) Winter (MW)
Before heat pump 68.82 62.2
After heat pump 85.42 78.8
-400 -300 -200 -100 0 100 200 300 4000
50
100
150
200
250
300
350
Enthalpy (MW)
Tem
pera
ture
(oC
)
Figure 23: Site composite curve with heat pump integration
Annualized capital cost with operational optimization of the existing plant
Operational optimization of total site annual cost with the integration of heat
pump is shown in Table 10. External power cost increases from 22.67 MM$ to
EP/G060045/1 Final Report
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35.03 MM$ after integration of heat pump, while fuel cost decreases from 93.08
to 82.09 MM$. Total annual cost increases to 118.67 MM$/yr from 116.32
MM$/yr after integration of heat pump. Therefore, with these costs of fuel and
electricity and the capital cost of heat pump it is not economic to set up a heat
pump.
Table 11: Annual costs before and after integration of heat pump
External Power (MM$) Fuel Cost (MM$)
Before heat pump 22.67 93.08
After heat pump 35.03 82.09
4.4.2 Integration of Organic Rankine Cycle (ORC)
HYSYS model ORC
HYSYS is used to calculate the efficiency and the purchase cost function for
ORC. ORC set up consists of an evaporator (E-100), turbine (K-100), condenser
(E-101) and a pump (P-100). Benzene is used as the organic working fluid. Low
grade heat at 110 oC is used to vaporize benzene at high pressures (1.145 bar).
Benzene vapour is used to drive a turbine along with reduction in pressure (14.5
kPa). Vapour stream from turbine at low pressure condensed in the condenser
(27oC). Pump is used to pump the low pressure organic liquid stream to high
pressure (1.145 bar) before being fed to the evaporator.
EP/G060045/1 Final Report
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Figure 24: Organic Rankine Cycle (ORC)
Efficiency of ORC
Figure 25 shows the variation of efficiency of ORC with respect to evaporator
duty. The efficiency of ORC is approximately constant around 11% with the
variation in evaporator duty.
0
2
4
6
8
10
12
0 2 4 6 8 10 12
Evaporator duty (MW)
Eff
icie
ncy
Figure 25: ORC efficiency with evaporator duty Purchase cost of ORC
Purchase cost of ORC is given as the total cost of equipments such as
condenser, evaporator and turbine. The cost of the evaporator and condenser is
EP/G060045/1 Final Report
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obtained from the online database2, while turbine cost is obtained from Peters et
al. [26]. Purchase cost of ORC is approximated based on a linear correlation
between the cost and the evaporator duty.
BHAPC evaORC 9
Where,
ORCPC = Purchase cost ORC
evaH = Evaporator duty (MW)
A, B = Regression coefficients
A = 0.01 MM$/MW
B = 25.1 MM$
y = 1E-05x + 0.2506
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 10000 20000 30000 40000 50000 60000 70000 80000
Evaporator Duty (kW)
Pu
rch
ase C
ost
(MM
$)
Figure 26: Linear correlation between purchase cost and evaporator duty The total site source and sink profile after integration of heat pump is shown in
Figure 27. Low grade heat corresponding to 62.11 MW is saved corresponding to
a temperature of 105oC. Cold utility requirement is reduced by 62.11 MW. As
shown before with the efficiency of 11%, the amount of electrical energy is
reduced from 68.82 to 61.99 MW during summer and from 62.2 to 55.39 MW
during winter. Purchase cost of ORC corresponding to given evaporator duty is
17.13 MM$.
2 http://www.matche.com/EquipCost/Compressor.htm.
EP/G060045/1 Final Report
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-400 -300 -200 -100 0 100 200 300 4000
50
100
150
200
250
300
350
Enthalpy (MW)
Tem
pera
ture
(oC
)
Figure 27: Site composite curve with ORC integration
Table 12: Electricity demands before and after integration of ORC
Summer (MW) Winter (MW)
Before heat pump 68.82 62.2
After heat pump 61.99 55.39
4.4.3 Integration of Absorption refrigeration
HYSYS model of absorption refrigeration
Absorption refrigeration system (Figure 28) consists of absorber (T-101), pump
(P-100), heat exchanger (E-104), generator (T-103), evaporator (E-100), and
condenser (E-103). Heat is released at temperature of 32oC to the surrounding at
a pressure of 13 bar in the condenser (E-103). Ammonia vapours are passed
through a throttle valve (VLV-101) to reduce the pressure to 14.50 kPa before
they can absorb heat from the surroundings at low temperature (-5oC) as
refrigeration load in the evaporator (E-100). Ammonia vapour is absorbed with
the lean solution of ammonia in the absorber (T-101). Heat is released to the
surroundings from the absorber. Concentrated solution of ammonia water is
pumped from 14.59 kPa to 13 bar into the generator. Low grade heat is used in
the generator (T-100) to separate ammonia from the concentrated solution to
produce a lean solution of ammonia water. Heat is exchanged between outgoing
EP/G060045/1 Final Report
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lean solution of ammonia water and incoming strong solution in exchanger T-
103.
Figure 28: HYSYS model of absorption Refrigeration Coefficient of performance
Low grade heat is used to provide the heat for refrigeration load for the system.
The low grade heat supplied in the generator is 265.4 kW, while 67.86 kW of
heat is removed as refrigeration load from the evaporator, with a COP of 0.26.
EP/G060045/1 Final Report
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-400 -300 -200 -100 0 100 200 300 4000
50
100
150
200
250
300
350
Enthalpy (MW)
Tem
pera
ture
(oC
)
Figure 29: Low grade heat can be used for refrigeration load on site
4.4.4 Boiler feed water heating Integration
Low grade heat is used to raise the temperature of make up water to deaerator
from 25oC to 101.3oC. This reduces the cost of fuel consumed in the boiler. The
benefits of BFW heating depends on condensate recycling process and
condensate management. BFW heating doesn’t change the hot utility
requirement from the base case. However, the cost of fuel required to supply the
hot utility required decreases from 93.08 to 80.57 MM$/yr due to decrease in the
heating required for boiler feed water. The overall energy cost decreases from
117.83 MM$/yr in the base case to 107.63 MM$/yr.
Absorption Refrigeration
EP/G060045/1 Final Report
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-400 -300 -200 -100 0 100 200 300 4000
50
100
150
200
250
300
350
Enthalpy (MW)
Tem
pera
ture
(oC
)
Figure 30: Temperature of make up water to deaerator is increased by low grade heat
4.4.5 Comparison of design options
Techno economic analysis
Table 13 shows the comparison between the various low grade heat upgrade
options. Heat pump decreases the hot utility requirement by reducing the low
pressure steam demand for the system. Hot and low utility cost in the system
decreases from 93.08 to 82.09 MM$/yr and 0.98 to 0.90 MM$/yr respectively.
However, heat pump increases the electricity import cost for the site from 23.77
to 36.06 MM$/yr. The overall operating cost increases from 117.83 to 119.06
MM$/yr with the introduction of heat pump. Therefore, heat pump is not
economic for the current case study with the given cost of electricity and fuel.
Integration of ORC decreases the cold utility requirement and therefore reduces
the total utility cost from 94.06 to 94.02 MM$/yr. Electricity produced from ORC
reduces the cost of electricity import from 23.77 to 20.21 MM$/yr. The total
energy cost decreases from 117.82 MM$/yr in the base case to 114.23 MM$/yr
for integration with ORC. Absorption refrigeration reduces the cold utility cost
from 0.98 to 0.90 MM$/yr. However, the main advantage of absorption
Boiler feed water heating
EP/G060045/1 Final Report
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refrigeration is reduction in electricity cost in vapour compression refrigeration by
5.46 MM$/yr. BFW heating reduces the cost of hot utility requirement from 93.08
to 80.57 MM$/yr. Total energy cost decreases from 117.83 to 106.63 MM$/yr.
This corresponds to an annual savings of 9.51% in the operating cost. BFW
heating is the most economical options amongst the heat upgrade technologies.
However, benefits of BFW heating depends on the condensate recycle policy
and condensate management.
Table 13: Techno economic evaluation of low grade heat upgrade technologies
Options Hot utility (MW) Cold utility (MW) Hot utility cost (MM$/yr)
Cold Utility cost (MM$/yr)
Total utility cost (MM$/yr)
Electricity import (MM$/yr)
Total energy cost (MM$/yr)
Winter Summer Winter Summer
Base case 252.17 215.17 368 368 93.08 0.98 94.06 23.77 117.83
Heat Pump 197.23 160.23 344.11 344.11 82.09 0.90 82.99 36.07 119.06
ORC 252.17 215.17 344.11 344.11 93.08 0.94 94.02 20.21 114.23
Absorption refrigeration
252.17 215.17 344.11 344.11 93.08 0.90 93.98 23.77 117.75
BFW heating
252.17 215.17 368 368 80.57 0.98 81.55 25.08 106.63
5 Over the fence process Integration
There has been limited work in literature on the integration of waste heat with DH
network. Ajah et al. [27, 28] evaluated the technical, economic and environment
feasibility of integration of waste heat from pharmaceutical industry with DH
network in the Netherlands. In this work, low grade heat is upgraded by chemical
and mechanical heat pumps [27, 28]. Holmgren [29] studied the impact of
integration of waste heat from industries, and waste incineration into the DH
network. They studied the impact of the price of electricity on the performance of
CHP system. The waste heat from industry accounted for approximately 15% of
the overall heat demand. However, this work does not take into account the part
load performance of CHP and boiler after the integration of waste heat.
Svensson et al. [30] and Johnsson et al. [31] evaluated the tradeoffs for the
usage of excess heat in pulp mill to supply the internal units inside pulp mill and
external consumers (DH). They evaluated the integration of new energy-efficient
EP/G060045/1 Final Report
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technology, such as process-integrated evaporation, drying, etc. identified by
Axelsson et al. [32, 33], in order to increase the surplus energy from the pulp mill.
This surplus heat can be either integrated within the process or exported to the
external consumers. The objective of their work was to study the techno-
economic impact of integration of excess heat with an external DH system.
However, their model does not account for the part load performance of CHP and
boilers. Heat load and electricity production in the DH system is variable for
different time of the day, seasons, etc. Hence, the performance of DH system is
dependent on the part load performance of CHP.
Carcasci and Cormacchione [34] analyzed the part load performance of gas
turbine CHP systems for providing heat to the DH network. They discussed
different strategies for part load operation of the gas turbine CHP plant. However,
their work was limited to data for part load operating strategies for gas turbines
and does not produce a generic model for CHP units. Furthermore, their work
does not consider the optimization of the performance of CHP based on the heat
demand profile for the locality. It is necessary to simultaneously account for heat
and electricity produced from the DH system to satisfy the varying heat demand
for DH system. The integration of waste heat from the process site with the DH
network reduces the heat produced by DH itself. This decrease in energy
production reduces the part load on the CHP and boilers which operate under
partial load. The economic feasibility of the integrated system is determined by
the part load performance in the DH systems. Therefore, it is imperative to study
the impact of integration of DH with the waste heat from the process industry.
The aim of this work is to: a) evaluate the impact of integration of waste heat on
part load performance of energy equipments in DH systems, and b) develop a
novel heat integration tool for the integration of DH and industrial total site
profiles.
EP/G060045/1 Final Report
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The techno-economic impact of integration of the low grade heat from process
industry with DH systems is analyzed through a case study in this work. This
paper is organized as follows: the first section discusses the importance of waste
heat integration with DH and the lack of relevant literature in this direction. The
new design methodology proposed in this paper is discussed in second section,
followed by a case study. The results are discussed in the penultimate section
followed by conclusions and future suggestions for work.
5.1 Design Methodology
DH system provides a unique opportunity to produce heat and electricity by the
combination of boiler, combined heat and power (CHP), etc. There is a clear
incentive to identify low grade heat available from the industrial plant and to
facilitate waste heat recovery with DH systems as this can reduces the overall
energy cost of the system by reducing the fuel consumption as a whole.
However, there are interactions between power production, heat consumption
and part load performance depending on the relative cost of electricity and heat.
The electricity can be either imported or exported to the grid, depending on the
deficit or surplus of electricity produced by DH. The heat has to be supplied from
the DH system, including the waste heat from the industry. The waste heat from
the industry is not available during shut-down, etc. Therefore, additional
capacities of the DH supply should be provided to maintain the continuous supply
of heat, when waste industrial heat is not available. The CHP and the boiler in
the DH systems operate at part load, depending on heat demand. Therefore, the
part load performance of CHP and boilers should be evaluated for the
optimization of operating cost of DH.
The methodology used in this work is shown in Figure 31. The total site
composite [3, 35] is a process integration tool to integrate the heating and cooling
requirements of different processes within a total site. It gives target for the
steam generation and consumption, heat demand form the hot utility boiler and
the overall cold utility demand for the overall site [3, 7, 35, 36]. The amount of the
total low grade heat available from a total site is determined by total site
EP/G060045/1 Final Report
53
composite as the heat available above the practical working temperature for DH.
The practical working temperature is defined as the sum of the temperature at
which heat is supplied to DH and the minimum approach temperature
considered.
The temperature at which waste heat from industries can be supplied to DH is
dependent on the supply temperature for the DH network and minimum
temperature of approach. The heat distribution network of DH network consists of
pipes supplying heat at 90-120oC and pipes returning the used water from DH
network at 40-70oC[2]. The minimum approach temperature of 15oC is assumed
in this work. The DH system under consideration is taken from the work of
Rolfsman [37] and it consists of waste boiler, oil boiler, oil/gas CHP and mixed
fuel CHP. The details of the DH systems are described in the next section.
The annual operating energy cost of the DH networks is a function of the heat
sold to the DH network, electricity sold to the grid and the cost of the fuel
consumed in the DH network. The demand of heat in the DH network varies with
the time of the day and season. The performance of the DH network is
considered in two cases; with and without integration of waste heat from the
process industry. The optimization framework is formulated to minimise the
annual operating energy costs, subject to process conditions and design
constraints, which allows to identify the most cost-effective way of utilising waste
heat and integrating it with DH systems, as well as to systematically assess the
economic impact of such an integrated design. The mathematical model for
integrating DH systems is detailed in below, including part load performance of
units employed in DH systems, and the objective function for the annual
operating energy cost.
EP/G060045/1 Final Report
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Figure 31: Methodology for techno-economic analysis of integration of waste
heat from total site with district heating design
The performance of DH is evaluated with two optimization frameworks with and
without integration of waste heat from process industry. The optimum results are
compared to evaluate the impact of waste heat integration.
5.2 Modelling of energy equipment
a) Combined heat and power (CHP) gas turbine
The power generated from CHP systems is obtained by the equation for the gas
turbine developed by Aguillar [25]. It is a function of the part load of a CHP unit,
heat output from the CHP unit, and the design work load from the CHP unit.
CWB
QfAW D
gt
CHP
CHPCHPCHP
10
CHPW = Power produced by a CHP unit, MW
A, B, C = Regression coefficients
CHPf = Part load fraction for a CHP unit
CHPQ = Heat produced from a CHP unit, MW
D
gtW = Design work from a CHP unit, MW
Total site composite
curves
District heating system
waste boiler
oil boiler
oil/gas CHP
mixed fuel CHP
Waste heat
Annual operating energy cost for district heating before and
after integration of waste heat from the total site
EP/G060045/1 Final Report
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The heat from the CHP unit ( CHPQ ) is a direct function of part load ( CHPf ) and the
design heat produced from the CHP unit ( D
CHPQ ). The part load ( CHPf ) is a variable
for the optimization framework.
D
CHPCHPCHP QfQ 11
b) Boilers
Heat produced from a boiler is given as:
D
boiboiboi QfQ 12
Where,
boiQ = Heat produced from boilers
boif = Part load fraction of boilers
D
boiQ = Design heat output from boilers
5.3 Optimization formulation
The optimization problem formulation can be described in terms of the
optimization variables, an objective function, and constraints.
Optimization variables
The performance of the integrated system is influenced by the heat demand
varying with the time of the day, season, and weekday or weekend. The part load
percentage of the energy producers (i.e. boilers and CHP units) are positive
variables. The boilers and CHP units are not required to operate for all the time
duration. Hence, their functioning of boilers and CHP are described by binary
variables, so that operating below their part load limits can be avoided.
Objective function
EP/G060045/1 Final Report
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The operating energy cost of the DH network is made up of the cost of fuel for
production of heat and electricity. The electricity sold to the grid and the heat sold
to the consumers is the revenue for DH.
heatelecfuelop CCCC 13
Where,
opC = Annual operating energy cost for the DH system, M$/y
fuelC = Annual cost of the fuel for a DH network, M$/y
elecC = Annual profit by exporting electricity to the grid, M$/y
heatC = Annual profit by selling heat to customers, M$/y
Model constraints
Fuel cost: The annual fuel consumption is dependent on the performance of
boilers and CHP units. The supply of heat in turn is related to the time of the day,
day of the week and the season of the year, etc. The fraction of each of the
variation defines the performance of the energy producing equipment. Fuel
consumption is determined from the heat supplied and the thermal efficiency for
individual energy generating equipment.
i j k
fueli
CHP
D
CHP
kjjki
CHP
i j k
fueli
CHP
D
CHP
kjjki
CHP
i j k
fueli
boi
D
boi
kjjki
boi
i j k
fueli
boi
D
boi
kjjki
boi
fuel
CswkfsQhpddpwf
CswkfsQhpddpwf
CswkfsQhpddpwf
CswkfsQhpddpwfC
4
22
,,
2
3
11
,,
1
2
22
,,
2
1
11
,,
1
/
/
/
/
14
Where,
i = season of the year (winter, summer or transition)
j = day of the week (weekend, weekday)
k = hours of the day (morning, afternoon, evening, night)
EP/G060045/1 Final Report
57
p = 1-4 represents boilers and CHP units
jdpw = days per week
kjhpd , = hours per day
ifs = fractions for each season in the year
ki
boipf , = part load fraction for a boiler p
D
boipQ = design heat supplied by a boiler p, MW
boip = thermal efficiency of a boiler p
fuelpCs = cost of fuel supplied to a boiler p, M$/MWh
ki
CHPpf , = part load fraction for a CHP unit p
D
CHPpQ = design heat supplied by a CHP unit p, MW
CHPp = thermal efficiency of a CHP unit p
fuelpCs = cost of fuel supplied to CHP p, M$/MWh
wk = number of weeks per year
Electricity cost: The electricity production from a CHP unit is determined by the
part load work from the CHP unit, depending on the heat production by the CHP
unit. It is assumed in this work all the electricity produced from the CHP unit can
be directly exported to the grid and there is no upper limit on the quantity that is
exported to the grid.
electi
i j k
kjkji
CHP
jelec CpwkfshpdWdpwC ,,,
1
15
Where,
elecC = Annual profit by exporting electricity to the grid, M$/y
electCp = Selling price of electricity to grid, M$/MWh
ifs = fractions for each season in the year
wk = number of weeks per year
ki
CHPpW , = Power produced from a CHP unit p, MW
EP/G060045/1 Final Report
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kjhpd , = hours per day
Heat cost: The heat demand is satisfied by the heat produced from the units. Any
excess heat is loss to the system and hence is minimized by the optimization
framework.
d
boi
kji
boi
d
boi
kji
boi
d
CHP
kji
CHP
d
CHP
kji
CHP
kji
demand QfQfQfQfQ 2
,,
21
,,
12
,,
21
,,
1
,, 16
Where,
i = season of the year (winter, summer or transition)
j = day of the week (weekend, weekday)
k = hours of the day (morning, afternoon, evening, night)
p = 1-4 represents boilers and CHP units
kji
demandQ ,, = Heat demand, kW
kji
pboif ,,
, = part load fraction for a boiler p
kji
pCHPf ,,
, = part load fraction for a CHP unit p
The revenue from heat generation is determined by the heat demand and the
unit price of heat supplied to the consumers. It is assumed that the unit price of
heat is independent of the variation of heat demand.
heati
i j k
kjjkji
demand
heat CpwkfshpddpwQC ,,,
17
Where,
kji
demandQ ,, = Heat demand kW
heatC = Annual profit by selling heat to customers M$
heatCp = Selling price of heat to customers $/kWh
ifs = fractions for each season in the year
wk = number of weeks per year
kjhpd , = hours per day
jdpw = days per week
EP/G060045/1 Final Report
59
Operational limits: The minimum part load fraction of the boilers and CHP units is
greater than 20% and 60% of the design capacity respectively.
CHPCHPCHP xUfx 1*60*100
boiboiboi xUfx 1*20*100
18
where,
CHPx Binary variables for CHP units
boix Binary variables for boilers
CHPf Load percentage for CHP units
boif Load percentage for boilers
U A large number
DH system consists of interconnected units which ensure a constant supply of
heat under varying demand, prices and ambient conditions. There are multiple
demands and operating degree of freedom that can be utilized in the optimization
of DH. In this work, linear models have been used to describe the performance of
energy equipment. The binary variables characterize whether the equipments are
running during time duration. This model presents a novel methodology for the
optimization of DH energy systems under variable demands with and without
integration of waste heat. The optimization problem is modelled as mixed integer
linear programming problem (MILP). The optimization problem is solved using
CPLEX MILP solver in GAMS 2.0.13.0 IDE. The execution time is less than a
second.
5.4 Case Study 1: Integration of industrial waste heat with district heating (DH) systems
EP/G060045/1 Final Report
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5.4.1 Waste heat available in an industrial site
Over-the-fence process integration is an interesting concept, where the excess
heat from process industry is supplied to the DH network. This reduces the fuel
consumption in DH systems. The additional costs would include the capital cost
of installation of the additional network to carry the heat from the industry to the
DH site and the operation cost for the supply network. However, another
important factor is the part load performance of the energy equipment of the DH
network and the additional revenue generated by the DH network by using
domestic and industrial waste as the fuel. The current work consists of a boiler
that uses domestic and industrial waste as a fuel. Since it utilizes the waste, the
cost of the fuel to this boiler is negative representing revenue due to the
consumption of industrial and domestic waste.
The economics of integration of process heat with the DH network is based on a
case study of industrial site utility systems and associated site-wide energy use
which was presented by Aguillar [25]. The total site sink and source profile for the
given case study is shown in Figure 32. The steam demand at VHP, HP, MP and
LP levels are 110.8, 21.4, 9.3 and 73.6 MW respectively (Figure 33). The power
generation potential is represented as areas in site utility grand composite curve
with VHP-HP, HP-MP and MP-LP cogeneration potential of 79.8, 58.4 and 49.1
MW respectively when a full steam recovery is made within the site utility
systems (Figure 33).
EP/G060045/1 Final Report
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-400 -300 -200 -100 0 100 200 300 400 5000
50
100
150
200
250
300
350
Enthalpy (MW)
Tem
pera
ture
(oC
)VHP
HP
MPLP
CW
Figure 32: Site source and sink composite curve
0 50 100 150 200 250 3000
50
100
150
200
250
300
350
Enthalpy (MW)
Tem
pera
ture
(oC
)
VHP
HP
MP
LP
Figure 33: Site Utility Grand composite curve
The amount of low grade heat available at a temperature higher than 105oC is
62.11 MW as shown in Figure 34. The temperature of 105oC corresponds to a
temperature of supply of 90oC and minimum temperature of approach of 15oC.
Site source profile below 105oC is shifted by 62.11 MW to account for the
EP/G060045/1 Final Report
62
extraction of low grade energy and hence the CW requirement for the total site
decreases by 62.11 MW. The total site profiles and hence the waste heat from
process industry is variable with respect to different seasons. However, in this
work it is assumed that DH demand is variable, while low grade heat from total
site is uniform throughout the year.
-400 -300 -200 -100 0 100 200 300 400 5000
50
100
150
200
250
300
350
Enthalpy (MW)
Tem
pera
ture
(oC
)
VHP
HP
MPLP
CW
62.11 MW
Figure 34: Low grade heat available from the site profiles
5.4.2 District heating (DH) systems
The existing DH supply system has two CHP (combined heat and power) units; a
mixed-fuel CHP unit and alternative waste and oil CHP unit. The alternative
waste and oil CHP unit can use both waste and oil, depending on the availability.
The system has two boilers; a waste fired boiler and an oil-fired boiler as shown
in Figure 35. Gas turbine/HRSG (Heat Recovery Steam Generator) unit supplies
the electricity and the heat to the DH network and the grid respectively. The
detailed data for performance of the four units on the supply side for Linkoping
network is shown in Table 14 [37]. The negative cost of the waste fuel indicates
EP/G060045/1 Final Report
63
profit by burning of the waste. The part load heat and electricity output from each
unit is decided by optimization based on whether or not the waste heat from the
industrial process is integrated to the network. The fuel to the oil boiler is the
most expensive with a price of 51.9 $/MWh.
Oil
Waste
Waste Boiler Oil Boiler
-400 -300 -200 -100 0 100 200 300 4000
50
100
150
200
250
300
350
Enthalpy (MW)
Tem
pera
ture
(oC
)
Heat
Electricity
Gas turbine /
HRSG
Fuel
Air
Electricity
Figure 35: Existing district heating system [37]
Table 14: Existing plant data in Linkoping [37]
Plant Heat
Max
(MW)
Electricity
Max
(MW)
Heat
Min
(MW)
Electricity
Min
(MW)
Fuel
Price
($/MWh)
Efficiency Electricity /
heat ratio
Waste
boiler
80 - 16 - -11.25 0.9 -
Oil
boiler
360 - 72 - 51.9 0.85 -
EP/G060045/1 Final Report
64
Alt.
waste
and oil
CHP
90 47 72 37.6 7.2 0.82 0.52
Mixed
Fuel
CHP
201 59 160.8 47.2 26.7 0.92 0.29
The demand data for residential sites is required to evaluate the performance of
the DH system before and after integration of waste heat. Figure 36 shows a
demand duration profile for a DH system with total heat demand of 1.22 TWh/y.
The heat is sold to DH at 115 $/MWh [2], while electricity is sold to the grid at
140.2 $/MWh [2]. The DH system has a maximum winter load of 175 MW and a
minimum load of 133 MW in summer. The heat produced from DH is more than
the energy demand as shown in Equation 16 for each time interval,
corresponding to time of the days, weeks, seasons , etc.
0 1000 2000 3000 4000 5000 6000 7000 8000 9000135
140
145
150
155
160
165
170
175
Duration (hours/year)
Heat
Load (
MW
)
Figure 36: DH system demand duration profile
EP/G060045/1 Final Report
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5.4.3 Feasible distance of heat transfer
The physical distance for the transfer of low grade heat available in an industrial
site to the potential consumers is first evaluated. It is assumed that all the low
grade heat (62.11 MW) from the site can be supplied directly to the potential
consumer. It is also assumed that the available low grade heat is sold to the
consumers at 80 $/MWh and the capital cost for the DH network including pipe
and related equipments is 1460 $/m on an average [2]. The operating cost for
pumping has not been considered in this simple calculation for the feasible
distance of heat transfer. It is assumed that 1% of the heat is lost in
transportation for the every km of distance from the source to the DH network.
The break even point for economic distance to heat transfer is 86.52 km from the
given data.
.
f
D
heat
waste
feasibleAtpm
revenuehpddpyQD
feasible
cos
99.0
19
Where,
feasibleD Feasible distance for heat transfer (km)
wasteQ Waste heat (MW)
dpy Days per year (365)
hpd Hours per day (24)
heatrevenue Revenue generated from selling of electricity (£/MWh)
tpmcos DH installation cost per m (1460£/m)
fA Annualization factor (0.13)
5.4.4 Optimization Results
The effect of waste heat integration on an existing DH network with the part load
performance is considered in this section. The DH network consists of boilers
and CHP systems as energy producers. The reduction in heat demand after
integration with waste heat from an industrial plant decreases the load on boilers
and CHP system. It is assumed in this work that all the waste heat is used by the
DH network. The integration of waste heat changes the amount of heat and
EP/G060045/1 Final Report
66
electricity produced from the DH systems. Hence, CHP units and boilers in the
DH system are running at part load. This reduces the efficiency in heat and
electricity generation of DH system.
The economic impact of integration is evaluated with the help of a case study.
The objective is to reduce the total operating energy cost under the constraint of
satisfying the overall heat demand for the DH system. The heat generated by the
combination of CHP units and boilers is reduced with the integration of waste
heat. The quantity of heat required from the CHP units interacts with the part load
fraction of CHP as shown in Equation 11. The corresponding power output is
calculated from Equation 10. The revenue for DH system is generated by selling
electricity to the grid, heat to DH consumers and from the consumption of waste,
while the consumption of fuel (oil, and coal) is the expenditure of the system.
The results after optimization for minimising overall annual operating energy cost
are shown in Table 15. The revenue from the generation of electricity that can be
exported to the grid is 56.58 M$/y. Waste heat boiler generates a revenue of 6.27
M$/y, while the cost for using other fuels is 8.97 M$/y. The overall annual
revenue is 202.53 M$/y. The integration of waste heat (62.11 MW) with DH
decreases the amount of the thermal load. This, in turn, reduces the part load on
CHP units, which leads to the decrease in the annual electricity production.
Therefore, the electricity revenue decreases from 56.58 M$/y to 49.26 M$/y. The
details of the part load of CHP units and boilers for the weekend and weekdays
of three seasons are shown in Table 16-Table 19.
The integration of waste heat reduces the heat supplied from CHP units and
boilers. Therefore, the fuel requirement decreases with the integration of waste
heat. However, due to the revenue generated (11.25 $/MWh) from the
consumption of waste fuel in boiler 1 (Table 14), the annual cost of fuel use
increases from 1.7 M$/y to 6.84 M$/y, due to the decrease in the utilization of the
waste fuel to produce steam from the boiler. The overall annual operating energy
EP/G060045/1 Final Report
67
revenue from the DH system decreases from 202.53 M$/y to 193.26 M$/y.
Hence, the integration of waste heat with the DH network is not economically
viable in the given case study.
0 1000 2000 3000 4000 5000 6000 7000 8000 900060
80
100
120
140
160
180
Duration (hours/year)
Heat
Load (
MW
)
Without integration
With integration
Figure 37: Heat supply with and without integration of waste heat
Table 15: Effect on annual DH cost by integration of waste heat
Revenue
(Electricity)
(M$/y)
Revenue
(Heat)
(M$/y)
Revenue
(Waste Fuel
consumption)
(M$/y)
Cost (Fuel
consumption)
(M$/y)
Annual
operating
energy
Revenue
(M$/y)
Before
integration
56.58 151.20 6.27 1.70 202.53
After
integration
49.26 151.20 0.84 6.84 193.26
EP/G060045/1 Final Report
68
Table 16: Part load percentage for Boiler 1
Weekday
Morning Afternoon Evening Night
before after before after before after before after
Winter 20.00 28.6
1
82.50 20.00 86.25 20.00 66.25 0
Summer 70.00 0 61.25 0 63.75 0 56.25 0
Transition 83.75 20.0
0
70.00 0 72.50 0 60.00 0
Weekend
Morning Afternoon Evening Night
before after before after before after before after
Winter 96.25 20.0
0
82.50 20.00 85.00 20.00 66.25 0
Summer 68.75 0 62.50 0 65.00 0 56.25 0
Transition 80.00 20.0
0
70.00 0 72.50 0 60.00 0
Table 17: Part load percentage for Boiler 2
Weekday
Morning Afternoon Evening Night
before after before after before after before after
Winter 20.00 0 0 0 0 0 0 0
Summer 0 0 0 0 0 0 0 0
Transition 0 0 0 0 0 0 0 0
Weekend
Morning Afternoon Evening Night
before after before after before after before after
Winter 0 20 0 20 0 20 0 0
Summer 0 0 0 0 0 0 0 0
Transition 0 20 0 0 0 0 0 0
Table 18: Part load percentage for CHP1
Weekday
Morning Afternoon Evening Night
before after before after before after before after
Winter 96.67 100.00 100.00 86.54 100.00 89.88 100.0
0
89.88
Summer 100.00 93.21 100.00 85.43 100.00 87.66 100.0
0
80.98
Transition 100.00 87.66 100.00 93.21 100.00 95.43 100.0
0
84.32
Weekend
EP/G060045/1 Final Report
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Morning Afternoon Evening Night
before after before after before after before after
Winter 100.00 98.77 100.00 86.54 100.00 88.77 100.0
0
89.88
Summer 100.00 92.10 100.00 86.54 100.00 88.77 100.0
0
80.99
Transitio
n
100.00 84.32 100.00 93.21 100.00 95.43 100.0
0
84.32
Table 19: Part load percentage for CHP 2
Weekday
Morning Afternoon Evening Night
before after before after before after before after
Winter 0 0 0 0 0 0 0 0
Summer 0 0 0 0 0 0 0 0
Transition 0 0 0 0 0 0 0 0
Weekend
Morning Afternoon Evening Night
before after before after before after before after
Winter 0 0 0 0 0 0 0 0
Summer 0 0 0 0 0 0 0 0
Transition 0 0 0 0 0 0 0 0
Comparison of the integration of waste heat with DH system and internal
use
The integration of low grade heat upgrade methodologies such as heat pump,
ORC (organic Rankine cycle) , absorption refrigeration, and BFW (boiler feed
water) heating was evaluated in the previous work [38]. The impact of waste heat
upgrade and its utilization within the site is shown in Table 20. The integration of
absorption refrigeration, ORC and BFW heating decreases the overall energy
cost for the total site. However, waste heat upgrading by heat pump compression
increases the overall cost due to the increase in electricity consumption. When
the integration of waste heat with DH systems is compared with integrated
options shown in Table 7, it can be seen that the utilisation of waste heat within
the site, for example, low grade heat recovery and integration for BFW heating, is
more economic, than employing over-the-fence process integration options,
EP/G060045/1 Final Report
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based on economic parameters and energy system configurations considered in
this case study.
Table 20: Integration of waste heat within a total site[38]
Options Hot
utility
cost
(M$/yr)
Cold
Utility
cost
(M$/yr)
Total
utility
cost
(M$/yr)
Electricity
import
(M$/yr)
Total
energy
cost
(M$/yr)
Base case 93.08 0.98 94.06 23.77 117.83
Heat Pump 82.09 0.90 82.99 36.07 119.06
ORC 93.08 0.94 94.02 20.21 114.23
Absorption
refrigeration
93.08 0.90 93.98 23.77 117.75
BFW
heating
80.57 0.98 81.55 25.08 106.63
5.5 Case Study 2: Integration of waste heat with a local energy systems
The total site profiles from process industry are now attempted to combine with
the local energy systems for analysing the potential for the integrated system.
The data for local energy systems is based on the information studied by Perry et
al. [36]. The system under consideration consists of a large hospital complex.
The hospital complex consists of 11 streams with temperature ranging from 18oC
to 121oC (Table 21).
Table 21: Process steam data for hospital complex site C[36]
Stream Name Tsupply (oC) Ttarget (
oC) DH (kW) CP (kW/
oC)
1 Soapy water 85 40 23.85 0.53
2 Condensed
steam
80 40 96.4 2.41
3 Laundry
sanitary water
25 55 17.7 0.59
4 Laundry 55 85 77.4 2.58
5 Boiler feed
water
33 60 7.2 0.24
EP/G060045/1 Final Report
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6 Sanitary water 25 60 77 2.2
7 Sterilization 30 121 12.74 0.14
8 Swimming pool
water
25 28 151.68 50.56
9 Cooking 30 100 59.5 0.85
10 Heating 18 25 100.8 14.4
11 Bedpan washers 21 121 5 0.05
Hot water and district heating requirements for the locality is given in Table 22.
DH hot water is supplied at 60oC and 80oC for hot water supply and residential
heating respectively.
Table 22: Process data for industrial and residential complexes D[36]
Stream Name Tsupply (oC) Ttarget (
oC) DH (MW) CP (kW/
oC)
1 District
heating
15 60 6.00 133.33
2 Hot water 15 80 5.00 76.92
Site profiles from the industrial site in Figure 32 have been combined with the
process data for district heating Table 21 and Table 22 to produce the overall
composite curve for the integrated system. The total site sink and source
composite curves are shown in Figure 38. The overall heat requirement from the
boiler to supply VHP steam decreases from 110.8 MW for the standalone
process industry site to 83.13 MW for the integrated system. This corresponds to
a saving of 25% in heat for the integrated system. The total site profile is a
simplified analysis tool for analysis of the possibility of integration between
process industry and local heating system. However, for a real integration, there
would be considerable cost for the construction of heat recovery and supply
system including heat exchangers, pipes, etc.
EP/G060045/1 Final Report
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-400 -300 -200 -100 0 100 200 3000
50
100
150
200
250
300
350
Enthalpy (MW)
Tem
pera
ture
(oC
)
Figure 38: Site source and sink composite curves after integration of local
heating
6 Conclusions & future work
The selection of steam level conditions is important as this significantly affects
heat and power management for the industrial site. A new cogeneration targeting
model has been developed in this work, as existing models have been shown to
give misleading results, compared to detailed design procedure. This new model
is based on isentropic expansion and the results obtained from the new model
have been shown to agree well with the results from the detailed isentropic
design method simulated in STAR®. The new method has been incorporated in
the optimisation study which systematically determines of the levels of steam
mains at minimum utility requirement.
Multiple options such as heat pumping, CHP, integrated gas turbines, absorption
refrigeration, drying, etc, are available for upgrading low grade heat. Heat pump
can reduce the LP steam requirement and subsequently the fuel consumed in
the boiler. However, electrical consumption in the site increases with the
integration of heat pump. The overall operating cost increases with the heat
EP/G060045/1 Final Report
73
pump for the current case study for the current ratio of fuel to electricity price.
ORC decreases the annual operating cost for the total site by reducing the
electricity demand from the site. Absorption refrigeration only reduces the
demand of cold utility. However the major savings comes from reduction in the
electricity demand for an existing vapour compression refrigeration system on the
site. Heating of boiler feed water decreases the fuel consumption in boiler and
hence the overall operating cost of the site. In conclusion BFW heating the
optimum option for integration with the total site in this case study. However, the
best heat upgrade technology is dependent on the site fuel and electricity cost,
condensate management system, and characteristics of low grade heat (quality
and size).
The distance of a DH centre from the process site is evaluated to determine
whether economic benefits for transferring of waste heat from process industry to
the DH centre is realistic and practical. This maximum distance of the economic
transfer of heat is calculated based on the assumption of the constant rate of
heat supply and no variability on the supply side of the DH network.
The integration of waste heat with an existing DH network decreases the heat
production from the existing supplying units. The economic feasibility for the
integration of waste heat with DH systems is case-specific, as the performance of
such an integrated system is heavily dependent on the part load performance of
the energy equipment and the cost of heat and electricity.
In this work, a case study for the integration of waste process heat to the DH
network has been evaluated with respect to economic impacts on the DH
network. The case study consists of two boilers and two CHP units using
different fuels for the generation of heat. It was shown here that the utilization of
waste heat is not economically beneficial to the DH network. However, it should
be noted that it strongly depends on the design and operating conditions of
energy infrastructure and economic parameters, for example, the prices of heat
and electricity in the market.
The developed methodology will be applied to further extended to other case
studies. Integration of renewable energy sources such as solar, wind, geothermal
EP/G060045/1 Final Report
74
etc to the total site will be considered in future work. The variation in renewable
energy sources will be incorporate to the framework.
Acknowledgement
Financial support from Research Councils UK Energy Programme
(EP/G060045/1; Thermal Management of Industrial Processes) is gratefully
acknowledged.
7 References
[1] Pellegrino JL, Margolis N, Justiniano M, Miller M, Thedki A. Energy Use,
Loss and Opportunities Analysis.
http://www1.eere.energy.gov/industry/intensiveprocesses/pdfs/energy_use_loss_
opportunities_analysis.pdf. In: Energy UDo, ed.: Energetics, Incorporated and
E3M 2004:169.
[2] Davies G, Woods P. The potential and costs of district heating networks.
2009 [cited 20/12/2010]; Available from:
http://www.ilexenergy.com/pages/documents/reports/electricity/District_heating_e
xec_summary.pdf
[3] Dhole VR, Linnhoff B. Total site targets for fuel co-generation, emissions,
and cooling. Computers and Chemical Engineering. 1993;17(Suppl):101-9.
[4] Kundra V. To Develop a systematic methodology for the implementation of
R-curve analysis and its use in site utility design and retrofit [MSc. Dissertation].
Manchester: University of Manchester; 2005.
[5] Mavromatis SP, Kokossis AC. Conceptual optimisation of utility networks
for operational variations - I. Targets and level optimisation. Chemical
Engineering Science. 1998;53(8):1585-608.
[6] Salisbury JK. The Steam-Turbine Regerative Cycle - An Analytical
Approach. Trans ASME. 1942;64:231-45.
[7] Raissi K. Total site integration [PhD Thesis]. Manchester: UMIST; 1994.
EP/G060045/1 Final Report
75
[8] Varbanov PS, Doyle S, Smith R. Modelling and optimization of utility
systems Chemical Engineering Research and Design. 2004;82(5):561-78
[9] Sorin M, Hammache A. A new thermodynamic model for shaftwork
targeting on total sites. Applied Thermal Engineering. 2005;25(7 SPEC.
ISS.):961-72.
[10] Varbanov PS. Optimisation and synthesis of process utility systems.
Manchester: UMIST; 2004.
[11] Medina-Flores JM, Picón-Núñez M. Modelling the power production of
single and multiple extraction steam turbines Chemical Engineering Science.
2010;65(9):2811-20
[12] Peterson JF, Mann WL. STEAM-SYSTEM DESIGN: HOW IT EVOLVES.
Chemical Engineering (New York). 1985;92(21):62-74.
[13] Varbanov PS, Doyle S, Smith R. Modelling and optimization of utility
systems. Chemical Engineering Research and Design. 2004;82(5):561-78.
[14] Smith R. Chemical Process Desing and Integration: John Wiley & Sons
Ltd. 2008.
[15] Klemes J, Friedler F, Bulatov I, Varbanov P. Sustainability in the Process
Industry: Integration and Optimization. New York, USA: McGraw Hill 2010.
[16] Perry S. Synthesis of total utility system Process Integration Research
Consortium. Manchester 2009.
[17] Chua KJ, Chou SK, Yang WM. Advances in heat pump systems: A review.
Applied Energy. 2010;87(12):3611-24.
[18] Singh H, Muetze A, Eames PC. Factors influencing the uptake of heat
pump technology by the UK domestic sector. Renewable Energy.
2010;35(4):873-8.
[19] Iyoki S, Uemura T. Performance-characteristics of the water lithium
bromide zinc-chloride calcium bromide absorption refrigerating machine,
absorption heat-pump and absorption heat transformer. International Journal of
Refrigeration-Revue Internationale Du Froid. 1990;13(3):191-6.
EP/G060045/1 Final Report
76
[20] Manzela AA, Hanriot SM, Cabezas-Gómez L, Sodré JR. Using engine
exhaust gas as energy source for an absorption refrigeration system. Applied
Energy. 2010;87(4):1141-8.
[21] Dincer I, Dost S. Energy analysis of an ammonia-water absorption
refrigeration system. Energy Sources. 1996;18(6):727-33.
[22] Sozen A, Yucesu HS. Performance improvement of absorption heat
transformer. Renewable Energy. 2007;32(2):267-84.
[23] Hung TC. Waste heat recovery of organic Rankine cycle using dry fluids.
Energy Conversion and Management. 2001;42(5):539-53.
[24] Amos WA. Report on Biomass Drying Technology. 1998 [cited NREL/TP-
570-25885; Available from: http://www.nrel.gov/docs/fy99osti/25885.pdf
[25] Aguillar O. Design and optimisation of flexible utility systems [PhD Thesis].
Manchester: University of Manchester; 2005.
[26] Peters MS, Timmerhaus KD, West RE. Plant Design and Economics for
Chemical Engineers: McGraw-Hill 2003.
[27] Ajah AN, Mesbah A, Grievink J, Herder PM, Falcao PW, Wennekes S. On
the robustness, effectiveness and reliability of chemical and mechanical heat
pumps for low-temperature heat source district heating: A comparative
simulation-based analysis and evaluation. Energy. 2008;33(6):908-29.
[28] Ajah AN, Patil AC, Herder PM, Grievink J. Integrated conceptual design of
a robust and reliable waste-heat district heating system. Applied Thermal
Engineering. 2007;27(7 SPEC. ISS.):1158-64.
[29] Holmgren K. Role of a district-heating network as a user of waste-heat
supply from various sources - the case of Goteborg. Applied Energy.
2006;83(12):1351-67.
[30] Svensson IL, Jönsson J, Berntsson T, Moshfegh B. Excess heat from
kraft pulp mills: Trade-offs between internal and external use in the case of
Sweden-Part 1: Methodology. Energy Policy. 2008;36(11):4178-85.
[31] Jönsson J, Svensson IL, Berntsson T, Moshfegh B. Excess heat from
kraft pulp mills: Trade-offs between internal and external use in the case of
EP/G060045/1 Final Report
77
Sweden-Part 2: Results for future energy market scenarios. Energy Policy.
2008;36(11):4186-97.
[32] Axelsson E, Olsson MR, Berntsson T. Heat integration opportunities in
average Scandinavian kraft pulp mills: Pinch analyses of model mills. Nordic Pulp
and Paper Research Journal. 2006;21(4):466-75.
[33] Axelsson E, Olsson MR, Berntsson T. Increased capacity in kraft pulp
mills: Lignin separation and reduced steam demand compared with recovery
boiler upgrade. Nordic Pulp and Paper Research Journal. 2006;21(4):485-92.
[34] Carcasci C, Cormacchione NAC. Part load operating strategies for gas
turbines in district heating applications. Proceedings of the Institution of
Mechanical Engineers, Part A: Journal of Power and Energy. 2001;215(5):529-
44.
[35] Klemes J, Dhole VR, Raissi K, Perry SJ, Puigjaner L. Targeting and
design methodology for reduction of fuel, power and CO2 on total sites. Applied
Thermal Engineering. 1997;17(8-10):993-1003.
[36] Perry S, Klemes J, Bulatov I. Integrating waste and renewable energy to
reduce the carbon footprint of locally integrated energy sectors. Energy.
2008;33(10):1489-97.
[37] Rolfsman B. Optimal supply and demand investments in municipal energy
systems. Energy Conversion and Management. 2004;45(4):595-611.
[38] Kapil A, Bulatov I, Smith R, Kim J-K. Site-wide Low-Grade Heat Recovery
with a New Cogeneration Targeting Chemical Engineering Research and Design.
2011;Submitted.
8 Appendix A
8.1 Optimization framework
8.1.1 Objective function
The present work used overall operating cost along with annual capital cost for
the new design heat upgrade technology as the minimization function.
EP/G060045/1 Final Report
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FixOpCstFEmmCstWatCstPowCstFuelCstOpCst op
cst 20
Where,
op
cstF Factor to increase operating cost by a percentage (fraction)
OpCst Overall annual operating plant cost (MM$/yr)
FuelCst Overall fuel cost for the site utility system (MM$/yr)
PowCst Overall electricity cost for the site utility system (MM$/yr)
WatCst Overall water cost for the site utility system (MM$/yr)
EmmCst Overall emission cost for the site utility system (MM$/yr)
FixOpCst Fixed charge for operating cost (MM$/yr)
Capital cost for any additional unit is defined as a function of the purchase cost
( nPurCst ) for each piece of equipment.
fix
n
ninstcepci CapPurCstFFCapCst
21
Where,
cepciF Chemical engineering plant cost index
instF Installation factor to consider other plant expenses
fixCap Fixed capital cost for the whole plant (MM$)
nPurCst Purchase cost for n equipment unit (MM$)
Total cost for the whole site is given by the following expression
annFCapCstOpCstTotCst 22
Where,
TotCst Total annualized cost (MM$/yr)
Fann Annualisation factor
EP/G060045/1 Final Report
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Optimization constraints
8.1.2 Electric balances
The cost of electricity consumed or produced on a site on the overall annual
operating cost is calculated by the electrical balance between site sources and
sinks.
impgenlossauxdem WeWeWeWeWeWe exp 23
demWe Total electricity demand of the process in each period (kWe)
expWe Electricity exported by the utility system in each period (kWe)
auxWe Electricity consumed by auxillary units including boiler fans, pumps,
cooling fans, motor drivers (kWe)
lossWe Distribution and control electricity loss (kWe)
genWe Electricity generation from the site utility system (kWe)
impWe Electricity imported by the site (kWe)
8.1.3 Mass balances
The mass balance at each node is
outin MM 24
Mass flow of steam into the deaerator where water is scrubbed with LP steam
before it is delivered to the boiler as saturated water.
vnt
dea
bfwmkupcondretstm
dea MMMMMM 25
Mass balance for the steam header is based on the mass flow from producers
(boilers, HRSG), receive or deliver steam to and from steam turbines, let down
valves or process etc.
k k k
vnt
k
outlet
k
k
outST
k
cons
k
k
dsh
k
k
inlet
k
k
inST
k
k
gen
k
k
HR
k
k
boi
k
MMMM
MMMMMM
26
EP/G060045/1 Final Report
80
Make up water is equal to the condensate lost by the process, along with the
losses in the utility plant including losses in boiler, HRSG, vent, gas turbine, and
process etc.
vnt
loss
vnt
dea
ret
k
gen
k
cons
k
k
vnt
k
inj
GT
HR
bldwn
boi
bldwn
mkup MMMMMMMMMM 27
Where,
k Steam header index in the utility plant
inM Mass flow into a mixing node (kg/s)
outM Mass of steam out from a mixing node (kg/s)
stm
deaM Mass flow rate of steam into deaerator (kg/s)
retM Returning condensate from the process (kg/s)
condM Mass flow rate of the condensate (kg/s)
mkupM Water make up for the utility system (kg/s)
bfwM Mass flow of water to the boiler (kg/s)
vnt
deaM Vented steam from the deaerator (kg/s)
boi
kM Steam delivered by boiler to header k (kg/s)
HR
kM Steam delivered by HRSG to header k (kg/s)
gen
kM Steam generated by process and delivered to the header (kg/s)
inST
kM Discharge from steam turbine into header k (kg/s)
inlet
kM Letdown steam entering header k (kg/s)
dsh
kM De-superheating boiler feed water injected into header k (kg/s)
cons
kM Steam consumed by process at header k (kg/s)
outST
kM Steam release by steam turbine to header k (kg/s)
outlet
kM Steam leaving header k by letdown (kg/s)
vnt
kM Vented steam for header k (kg/s)
mkupM Water make up for utility system (kg/s)
boi
bldwnM Blowdown for all boiler in utility system (kg/s)
HR
bldwnM Blowdown from all HRSG in utility system (kg/s)
EP/G060045/1 Final Report
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inj
GTM Steam injected to all gas turbine in utility system (kg/s)
vnt
kM Steam vented from header k (kg/s)
8.1.4 Heat balance
Heat balance for two streams in adiabatic mixing is shown in Equation 28-29.
outin QQ 28
outoutinin MhMh 29
Enthapy balance for the deaerator is given by Equation 30. The enthalpy balance
for a steam header consists of heat from the generator (boiler and HRSG), both
production and consumption from steam turbine, let down, and process
(Equation 31-32).
vnt
dea
g
dea
bfw
dea
f
dea
mkupmkupcondcondretretstm
dea
stm
dea MhMhMhMhMhMh 30
k k k k k k
vnt
k
dsh
k
outST
k
gen
k
bHR
k
boi
k
hdr
k
k
vnt
k
hdr
k
k
dsh
k
bfw
k
k
inlet
k
inlet
k
k
inST
k
inST
k
k
gen
k
gen
k
k
bhr
k
hdr
k
k
boi
k
hdr
k
MMMMMMh
MhMhMh
MhMhMhMh
31
k k
dsh
k
inlet
k
inST
k
k
gen
k
hdr
k
dsh
k
bfw
k
k
inlet
k
inlet
k
k
inST
k
k
gen
k
gen
k
MMMMh
MhMhQMh
32
Where,
k Steam header index
inQ Heat entering a mixing node (kW)
outQ Heat leaving a mixing node (kW)
inh Specific enthalpy of heat entering a mixing node (kJ/kg)
EP/G060045/1 Final Report
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outh Specific enthalpy of heat leaving a mixing node (kJ/kg)
f
deah Enthalpy of saturated steam at deaerator pressure (kJ/kg)
g
deah Enthalpy of saturated vapour at deaerator pressure (kJ/kg)
bfw
kh Enthalpy of feed water needed to de-superheat steam (kJ/kg)
stm
deah Enthalpy of stripping steam to the deaerator (kJ/kg)
reth Enthalpy of returning condensate from the process (kJ/kg)
condh Enthalpy of condensing water entering the deaerator (kJ/kg)
mkuph Enthalpy of make up water (kJ/kg)
gen
kh Enthalpy of steam generated by the process (kJ/kg)
hdr
kh Enthalpy in steam header k (kJ/kg)
inlet
kh Enthalpy of let down steam header k (kJ/kg)
inST
kh Enthalpy of discharge from steam turbines at header k (kJ/kg)
8.2 Equipments
8.2.1 Multi-fuel boilers
In a boiler the chemical energy of the fuel is extracted to heat the condensate
or feed water to generate steam at the required temperature. There are
numerous types of boilers and control schemes along with different unit size
and actual load. This results in different performance trends. Aguillar [25]
assumed a linear relationship between fuel consumption and steam
production as shown in Equation 33.
boi
boi
boi
fboi
stm DB
33
Where,
boi
fQ Net heat from the fuel consumed inside the boiler (kW)
boi
stmQ Actual heat added to the water/steam inside the boiler (kg/s)
boiB , boiD Regression parameters
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83
With the assumptions that boiler blowdown is extracted at saturated
conditions and as a fixed fraction of boiler steam output, the heat supplied to
the water/steam cycle can be expressed as:
boi
T
boi
bid
boi
ecoboi
T
boi
stm
boi
stmh
FhhMQ
.1..
34
Where,
boi
stmM Actual steam output from the boiler (kg/s)
boi
Th Enthalpy difference between feedwater and outlet steam conditions
(kJ/kg)
boi
ecoh Enthalpy difference across boiler economiser (kJ/kg)
boi
bldF Boiler blowdown fraction taking as reference the outlet steam
flowrate (kg blowdown/kgsteam)
Equation 35 is obtained by rearranging Equations 33 and Equation 34. The
coefficients for this boiler model are obtained by regression from operating or
design data.
boi
T
boi
bid
boi
ecoboi
T
boi
stm
boi
boi
boi
f
h
FhhMD
B
Q .1..
35
Here, boiB & boiD are regression coefficients.
8.2.2 Gas turbines (GT)
Gas turbines convert the chemical energy of fuels into electrical energy via a
three step process
Compression: The inlet pressure and temperature of the ambient air is
increased by the compressor.
Combustion: Heat is added at high pressure by fuel ignition.
Expansion: The hot combustion gases are expanded through the
turbine to drive the compressor and to provide power (electricity).
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The relationship between power output from the gas turbine to the required
heat input is approximated by a straight line which is known as the Willans
line.
gtgtgtgt WQCW int 36
gtW Gas turbine power output (kW)
gtQ Gas turbine fuel input (kW)
gtC , gtWint Regression parameters
8.2.3 Heat recovery steam generators (HRSG)
HRSG utilize the waste heat from the gas turbine to produce steam which can
be further used to generate power or provide heating to consumers. HRSG
can be further classified into the following types:
a) Unfired units: Steam production is limited by the temperature and
available energy in the exhaust gases.
b) Supplementary fired units: The remaining oxygen in the exhaust gases is
used to burn fuel to boost steam generation.
c) Fully fired units: Additional quantity of air is supplied for further
consumption of fuel and hence increases production of steam.
Aguillar [25] derived a simple equation for the steam production from a gas
turbine based on the mass and heat balance for the unfired HRSG.
gtgt
D
gtgt
D
gthr
m
hr
satgt
D
gtgt
Dgtgt
Dhr
eva
hr
sh
hr
exh
hr
radhr QQQTTQ
QQkTexh
hh
CpFM
1
37
Where,
hrM Maximum HRSG steam production from GT exhausts (kg/s)
hr
radF Radiation losses factor for the HRSG
EP/G060045/1 Final Report
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hr
exhCp Average specific heat for the exhaust gases (kJ/kg-°C)
hr
shh , hr
evah Steam enthalpy difference across HRSG superheater, evaporator
(kJ/kg)
hr
mT Minimum temperature difference between gas and steam/water
profiles (°C)
gt
DTexh Design temperature at the exhaust of the gas turbine (oC)
hr
satT Saturation temperature for the steam produced in the HRSG (°C)
gtk , gt , gt Regression coefficients
gt
DQ Design heat from the gas turbine
gtQ Actual heat from the gas turbine
8.2.4 Electric motors (EM)
Electric motors are devices that convert electricity into shaft power by
inducing electromagnetic forces in its rotational wounding (i.e. rotor). The
units are broadly classified into synchronous, direct current, three phase
induction and single phase [25].
Willans line describes the part load performance of the electric motors in
terms of regression parameters with the full load performance of the motor.
emem
D
emem
D BWAWe 38
em
DWe Design electric consumption of the motor (kWe)
em
DW Design motor power output (kW)
emA , emB Regression parameters
8.2.5 Steam turbines (ST)
Steam turbines convert energy from steam into electrical energy by expanding to
lower pressure. They can be classified as single or multiple extraction turbines
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according to number of equipments attached to the shaft. The back pressure
steam turbine expands steam to a lower pressure, while steam is expanded to
liquid water in a condensing turbine.
Single stage steam turbine
Aguillar [25] developed linear models to describe the performance of steam
turbines. The design steam flow rate ( st
DM ) in steam turbine is a function of
isentropic enthalpy change ( st
ish ) and the design capacity of the unit ( st
DW ).
st
D
stst
st
is
st
D WBAh
M
1
st
sat
st TaaA 10 st
sat
st TaaB 32
39
Here a0, a1, a2, a3 are regression coefficients,
st
satT is the saturation temperature difference across the turbine.
The power of the unit ( stW ) is proportional to the steam mass flow rate ( stM ) and
the ordinate intercept of the Willans line ( stWint ).
stststst WMnW int 40
The actual shaft power from a single stage turbine is a function of maximum
output size ( st
DW ), actual steam flow ( stM ) and inlet and outlet conditions of the
steam in the turbine.
st
ststst
D
stst
st
stst
is
st
B
ALWLM
B
LhW 1
1
st
sat
st TaaA 10 st
sat
st TaaB 32 st
satLL
st TbaL
41
Here a0, a1, a2, a3, aL, bL are regression coefficients,
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st
satT is the saturation temperature difference across the turbine.
Multi-stage steam turbine
Muti-stage turbine discharges steam at different outlet steam levels. A multi-
stage steam turbine can be decomposed into several single stage turbines
connected in series. Aguillar [25] developed linear models to evaluate the
performance of multi-stage turbines. Isentropic enthalpy difference across the
downstream stages is evaluated by assuming a typical isentropic efficiency
across all the stages. The enthalpy for a three stage turbine is calculated by the
following equations:
mst
sP
mst
in
mst
is hhh 0,1010
mst
is
mst
sP
mst
in
mst
is hhhh '
321,2121
mst
is
mst
sP
mst
in
mst
is hhhh '
322,3232
mst
sP
mst
in
mst
is hhh '1,2'1
'
21
mst
sP
mst
in
mst
is hhh '2,3'2
'
32
mst
is
mst
is
mst
in
mst
in hhh 10
'
0'1 mst
is
mst
is
mst
in
mst
in hhh '
21
'
'1'2
42
Where,
0,1,2,3 Sub indexes for steam conditions at inlet, first, second and third
extractions of the turbine.
1’,2’,3’ Sub indexes indicating approximate steam conditions at first,
second and third outlet of the turbine.
mst
iiish )1( Isentropic enthalpy difference for the i stage of steam turbine (ie.
between i and (i+1) respectively) (kJ/kg)
mst
inih Enthalpy of steam entering stage i (kJ/kg)
mst
inih ' Approximate enthalpy of steam entering each stage i
mst
is
' Isentropic efficiency value to approximate steam conditions in
downstream stage
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The shaft power equation for single stage steam turbine is extended to each of
the stages of the multi-stage turbine. The overall shaft power for a multi stage
steam turbine ( mst
totW ) is calculated as:
mst
mstmstmst
D
mstmst
mst
mstst
is
mst
B
ALWLM
B
LhW
1
11111
1
1101 1
1
mst
mstmstmst
D
mstmst
mst
mstst
is
mst
B
ALWLM
B
LhW
2
22222
2
2212 1
1
mst
mstmstmst
D
mstmst
mst
mstst
is
mst
B
ALWLM
B
LhW
3
33333
3
3323 1
1
mstmstmstmst
tot WWWW 321
43
Where,
mst
iW Shaft work from i stage of the multi-stage steam turbine
mst
iA , mst
iB Regression coefficients for i stage of multi-stage steam turbine
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Part II
Environmental and Socio-Economic Issues
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9 Barriers to Process Efficiency Improvements and Low Grade Heat Utilisation
Barriers to process efficiency improvements and low grade heat recovery were
investigated by a literature review, a workshop with over 50 attendees from
industry and academia, canvassing viewpoints of key stakeholders and analytical
mapping techniques. The detailed results from these have been shared with
stakeholders in 2 detailed, freely accessible reports, a conference paper and
published journal paper, as well as discussions with groups such as the
Committee on Climate Change. A summary of key points is given below:
The recovery of low grade heat (LGH) has potential to increase the energy
efficiency of process industries, but there are many barriers to its utilisation. The
main technical barrier is generally the lower temperatures involved. This often
results in recovery technologies operating at lower efficiencies than for other
energy provision systems. Other factors include the potential condensation of
corrosive or fouling elements. These and other issues may result in process
disruption and maintainability.
Non-technical barriers to the uptake of LGH are diverse and involve numerous
actors. Consultation with industrial and academic stakeholders in the UK
established that cost, return on investment and technology performance were
key barriers to process industry energy efficiency improvements. However, for
low grade heat utilization, stakeholder engagement and strategic mapping found
that location, cost and the availability of infrastructure were the most significant
barriers. This is augmented by a number of institutional issues relating to
company strategy and priority, specifically, instances where energy efficiency is
not perceived as a practical concern, or directly related to productivity.
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Discussion with stakeholders reinforced the fact that there is, at present, little
commercial appetite in this area. A key technology differentiator was perceived to
be the ability to extract/remove an energy efficiency measure during and after
installation. Process interruption risk is a major disincentive to implementing
energy efficiency measures in the process industries.
Interestingly capital cost seems to be a more significant barrier than project rate
of return, again suggesting support with infrastructure development is central to
developing the sector. In terms of policy there is a need to incentivise the use of
low grade in the earliest planning stages. Unlike the use of heat at higher
temperature the efficient use of LGH may be contingent on the availability of
“over the fence” options and an external demand for recovered heat. In that
regard regional heat load mapping may be an effective method of intervening
between suppliers and potential users. A key message appears to be that, at
present, EEMs are not sufficiently important (or visible) to management and that
purely fiscal measures will not create momentum in this area, since many
measures have been economical for some time, but have not been implemented.
Therefore additional levers or incentives must be identified in order to incentivise
dedicated finance options such as risk sharing which may complement or
accentuate any associated financial benefits. Such measures must also address
infrastructural issues which may impede “over the fence” options to utilize
recovered heat.
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10 Environmental and Economic Analysis
The varied forms in which LGH can be recovered from process industries and the
distinct technological options for its recovery, preclude a generic account of the
potential environmental benefits of its utilisation. Therefore in order to evaluate
the environmental benefits (and costs) of low grade heat recovery a life cycle
assessment approach was applied to a number of case studies which were
agreed with academic and industrial partners as representative of potential future
development options. A key objective of this work was to quantify greenhouse
gas savings for different low grade heat recovery options. However, the analysis
also extended beyond this to cover other environmental impact areas (such as
eco toxicity) to provide a more nuanced (and realistic) appraisal of the benefits
(and trade-offs) of LGH recovery. Since different aspects of economic viability
had been highlighted in the barriers work, assessments were also carried out of
the economic performance of different process options, using simple discounted
cash flow techniques.
The three case studies were chosen to represent different industries and means
of LGH recovery, as well as different degrees of “over the fence” interactions, but
each case study is “internally consistent” in that it uses technology appropriate
for that application. The case studies are:-
Waste heat from a coke oven flue gas stream is recovered as electricity
through the operation of an organic Rankine cycle (ORC).
Latent (and sensible) heat from woodchip boiler flue gas is recovered
through a condensing boiler to preheat district heating return water.
Hot wastewater from a paper mill is used in conjunction with a heat pump
to provide thermal energy for a multi effect desalination (MED) process.
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The first case study produces a co-product which may be used directly on or
offsite. The second case study increases the energy efficiency of a closed loop
system whereby the energy contained in return water is augmented by LGH
recovered onsite. In contrast the final case study demonstrates complete
disassociation between the LGH source and the eventual product, in this case
potable water for human consumption. Each augmented process has been
assessed by project partners from a thermodynamic perspective. This data
assists in estimating the direct emissions and lifecycle impacts associated with
each process as well as distinguishing the lifecycle benefits from LGH recovery
through comparison of two scenarios, one in which LGH is recovered and one in
which it is not. In each case the lifecycle impacts are allocated to a rational
reference unit; 1 kg of coke, 1 MWh heat and 1 m3 of potable water. For each
case study the resources, energy and emissions associated with each discrete
stage are quantified. These are aggregated and assessed using proprietary
lifecycle assessment (LCA) software [1] to expresses the overall environmental
impact in terms of consolidated categories.
Each of these case studies has been reported in detail in conference and journal
papers. However, a summary of project results is given below:-
10.1 Organic Rankine cycle integrated into a coke oven.
Project partners [2] have identified the flue gas emitted from a coke oven within
an integrated steelworks as a viable source of LGH. This was estimated to yield
21 MW of recoverable energy. Given its continual production cycle, an organic
Rankine cycle (ORC) was chosen as a suitable recovery mechanism due to its
unobtrusive interaction with the process. The ORC effectively mimics a traditional
steam cycle but this configuration is unsuitable for temperatures under 370 °C [3]
necessitating a organic working fluid. Additional consultation with project partners
suggested an ORC efficiency of 11% resulting in an approximate electricity
generation estimate of 2.31 MW [4]. Based on the UK electricity generation mix
this is estimated to negate the emission of 10,927 t CO2 annually. Direct
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emissions due to coke production are calculated based on the estimated coke
yield and the gaseous fuels used in the coke production process. It is estimated
that ORC operation reduces the carbon intensity of the coking process by 1.39%,
although it allows for a surplus of electricity at the oven itself.
The lifecycle emissions associated with coke production are based on 3 main
lifecycle stages, the production of coal, its transportation and the coking process
itself. The data obtained on coal production was based on environmental reports
from a number of Australian coal mines (representing the main source of coking
coal arriving in the UK) [5]. The lifecycle impacts associated with each stage of
the coking process have been estimated, assuming different types of coal mines:
underground mines with varying levels of methane emissions and surface mines
with varying degrees of electricity demand. The results for a number of very
distinct environmental impact categories were calculated and reported, including
global warming potential, acidification potential, human toxicity etc. However,
normalised, weighted results are commonly used to express the overall/net
environmental impact of processes. This approach has its shortcomings,
particularly since normalisation factors inevitably introduce some degree of
subjectivity. However a well established and recognized normalisation approach
was used in this work, which allowed us to compare the net environmental
impact of different elements of two coke production systems: one with an ORC to
recover low grade heat and one without. The results in table 1 are expressed in
units of millipoint (mPts), which is an aggregate measure that represents the
environmental impact of an average European during a single year.
Table 23: Normalised results for 1 kg of Coke expressed in millipoints (mPTs) [6].
Underground Surface
Process/Activity Gassy Non Gassy High Elect. Low Elec.
Emissions at Coke plant 67.9 67.9 67.9 67.9
Hard Coal Coke production plant 1.11 1.11 1.11 1.11
Hard coal Mix 171 169 158 117
UK ORC Electricity 0.5 0.5 0.5 0.5
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Water and Chemical Inputs 0.01 0.01 0.01 0.01
Freight Rail 0.01 0.01 0.01 0.01
Blast furnace gas 1.4 1.4 1.4 1.4
Ocean Freight 29.6 29.6 29.6 29.6
ORC components 0.004 0.004 0.004 0.004
Recovered electricity -0.64 -0.64 -0.64 -0.64
Total (with recovery) 271 269 258 217
As can be seen from Table 1 above the ORC operation has a negligible effect on
the lifecycle impacts of coke. Indeed the choice of coal has a more pronounced
influence. This is a reflection of the high level of carbon intensity of the coking
process, which cannot be significantly offset by low grade heat recovery through
ORC operation.
However a techno-economic evaluation suggests that this might still be a
financially viable method of achieving some greenhouse gas reductions in a
“hard-to-decarbonize” industry sector. A discounted payback (DPP) period of
between 2.8 and 6.3 years was calculated under different economic
assumptions, which was within the bounds considered attractive by stakeholders
at the project workshop described above.
10.2 Condensing boiler applied to woodchip combustion.
One of the most common means of improving thermal efficiency is the
introduction of a condensing boiler to recover latent heat from the waste gas
steam. This benefit is more pronounced for raw biomass systems, where the
higher moisture content means that up to half of the calorific value of the fuel is
recoverable. Both for this reason and its lack of process disruption, Chen et al.
[8] have examined the impact of a condensing boiler on a Finnish woodchip
fluidized bed boiler which provides heat for a district heating system. The
woodchip plant demonstrates a basic (pre-condenser) output of 40 MW. The
heating systems served by this plant generally consist of water radiators whereby
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the return water temperature is between 35 and 40°C. The return water
temperature is preheated in the condenser using both the recovered latent heat
of water vapour and the sensible heat of the flue gas. It is estimated that the
condenser increases the thermal output to 52 MW. An examination of the
enthalpies of the various process streams suggests that the system has a
thermal input of 44 MW. Comparing thermal efficiencies suggests a fuel saving of
22%. Based on the fuel consumption rate and the carbon content of the
woodchips, this equates to offsetting 36,059 t direct CO2 annually. It must be
clarified that the operation of a condensing boiler will not result in a decrease in
the actual amount of carbon emitted from the facility rather it allows for an
increased district heating capacity without necessitating additional woodchip
inputs. The Lifecycle assessment (See Table 2) highlights the impact of the
condensing boiler and incorporates data from a number of distinct lifecycle
stages such as forest nursery, tree cultivation, felling, as well as boiler operation.
The operation of a condensing boiler (and associated woodchip savings) reduces
the lifecycle impact estimate for most categories [9]. However, it is noted that
one of the key impact categories on which this work was focused is reduction of
greenhouse gas emissions and the global warming potential is reduced by only
6.8%, compared to larger reductions of e.g. 22.49% for photochemical oxidation.
This is largely because the heat recovery application here is effectively reducing
the amount of fuel required and, since that fuel is wood, it has significant
land/cultivation related impacts, which are being reduced correspondingly.
Table 24: Lifecycle impact of producing 1 MWh of district heat using CML midpoint
assessment.
Impact category Unit No Recovery Recovery % Impact
Abiotic depletion kg Sb eqv. 0.15 0.15 1.48 %
Acidification kg SO2 eqv. 1.33 1.03 -22.05 %
Eutrophication kg PO4 eqv. 0.37 0.29 -22.08 %
GWP100 kg CO2 eqv. 38.79 36.16 -6.80 %
Ozone depletion kg CFC-11 eqv. 0.0002 0.0002 -22.95 %
Human toxicity kg 1,4-DB eqv. 82.33 65.75 -20.15 %
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Fresh water ecotoxicity kg 1,4-DB eqv. 9.98 10.26 2.82 %
Marine ecotoxicity kg 1,4-DB eqv. 15967.14 15365.26 -3.77 %
Terr. Ecotoxicity kg 1,4-DB eqv. 0.57 0.49 -14.61 %
Photochem. Oxidation kg C2H4 0.10 0.08 -22.49 %
The overall lifecycle impact reduction may seem disappointing considering the
associated fuel reduction, however the requirement to maintain flue stream
buoyancy after condensation means that the net electrical demand at plant (per
MWh) is increased when this supplementary requirement is included. The costs
associated with the condensing boiler are based on the choice of material as well
as the ancillary costs associated with fan operation. Chen et al. [8] estimate the
discounted payback period to range from 1.7 years for system produced using
carbon steel at a discount rate of 5% to 6.93 years for a system constructed
using stainless steel with a discount rate of 15%. The savings are based on
reducing the required quantity of woodchips. However, the scale of annual
woodchip consumption makes the DPP sensitive to woodchip price. If it is
assumed that the woodchips are produced onsite (increasing costs by 15%), this
reduces the DPP by 23% on average.
10.3 Heat pump for desalination
Project partners [10] have examined the thermodynamic properties of using a
heat pump (HP) to recover LGH from a wastewater stream thereby negating the
use of a fossil powered boiler within a multiple effect distillation (MED)
desalination system. MEDs have have been used for facilitating the production of
freshwater through seawater evaporation. Within a MED, the steam generated
from the previous (or first) stage becomes the source of heat for the subsequent
stage. The heat pump is used to essentially upgrade the LGH to a point where it
can be used to evaporate seawater. In estimating the fossil energy requirements,
the energy of seawater evaporation is taken to be 2333.8 kJ/kg. For the MED in
question, a gain output ratio (GOR) of 10 is assumed, (representing the ratio
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between the amount of water produced per unit mass of dry saturated steam
supplied [11]) suggesting that the fossil energy requirement of a desalination
plant (without a HP) becomes 233 kJ/kg [11]. Based on an assumed boiler
efficiency of 80% this is estimated to offset 14.56 kg of coal or 7.52 m3 of natural
gas (NG) per treated m3, effectively avoiding 9,924 or 3,150 tonnes of direct CO2
respectively. However the impact of integrating a heat pump will necessitate
additional material (due to the increased heat exchanger areas) but will increase
the relative electricity demand 4 fold, mostly due to the increased pumping
requirement. The lifecycle assessment results for desalination using a heat
pump (MED HP), natural gas fuel (MED NG) and coal fuel (MED Coal) are
summarised in Table 3 below.
Table 3: Lifecycle impact of producing 1 m3 of potable water using CML midpoint
assessment.
Impact category Unit MED HP MED NG MED Coal
Abiotic depletion kg Sb eqv. 0.023 0.025 0.520
Acidification kg SO2 eqv. 0.049 0.167 0.276
Eutrophication kg PO4 eqv. 0.008 0.005 0.028
GWP100 kg CO2 eqv. 6.428 18.538 60.703
Ozone depletion g CFC-11 eqv. 0.0002 0.00213 0.0001
Human toxicity kg 1,4-DB eqv. 0.001 0.002 0.023
Fresh water ecotoxicity kg 1,4-DB eqv. 0.022 0.008 0.037
Marine ecotoxicity kg 1,4-DB eqv. 3715.766 1957.008 9230.746
Terr. Ecotoxicity kg 1,4-DB eqv. 2.035 0.647 4.266
Photochem. Oxidation kg C2H4 2.143 1.371 7.033
As can be seen from Table 3 above, the use of coal within the MED results in the
highest environmental impact in all categories with the exception of o-zone
depletion. In this instance, the production of natural gas (with its associated
fugitive emissions of o-zone depleting compounds) results in a significantly
higher impact value. However as a considerable portion of the UK‘s electricity is
generated through nuclear power and coal combustion the water produced in
conjunction with heat pump operation is seen to have higher toxicological
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impacts than water produced by the gas fired MED. It may be argued by some
that treating recovered LGH as being effectively “free” from embodied impacts is
a misrepresentation. By way of example, the lifecycle impacts of the potable
water are reassessed whereby the LGH containing stream is allocated emissions
based on an exergy allocation scheme. In this example 0.3% of the impacts
associated with producing 1 kg of kraft paper are allocated to 13 litres of LGH
containing wastewater [12]. This alters the relative impact of LGH recovery and
demonstrates a higher impact than an MED powered through natural gas in the
toxicological and eutrophic impact categories, questioning the implication that
LGH recovery is inherently beneficial in all instances. Project partners have
estimated the payback period to range from 4 to 7 years depending on MED
configuration [10].
10.4 District Heating
Analysis was also carried out as part of this project of a heat pump used to
recover low grade heat for a district heating scheme. The results of this have not
been reproduced here as they essentially confirmed the observations noted
above for heat pumps applied to desalination, namely that if the heat is treated
as waste heat, which has no associated environmental impact then there are
significant positive impacts in most environmental categories, but the additional
consumption of electricity increases toxicity impacts. It should be noted that this
result is obviously related to the assumptions made about the national mix for
power generation plant and that a future high renewables generating scenario
would have lower toxicity impacts, though high levels of nuclear power could
increase the toxicity impact.
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11 Social aspects: perceptions of heat users
The main objective in this work was to add to the very limited empirical evidence
on UK citizen and consumer opinion on the use of waste process heat for district
heating. Projects aiming to make use of waste process heat for domestic and
commercial space and water heating will likely be more difficult, if not impossible,
without consumer and citizen support. This part of the project first conducted two
focus groups on district heating with older, potential users in Newcastle. While
this involved participants’ consideration of hypothetical installation, all engaged
closely with the issues. The second part of the project elicited resident opinion on
the prospective use of waste process heat for district heating in the local
authority area of Neath Port Talbot in Wales, where waste process heat from
Corus, a local integrated steelworks, could potentially be used to provide local
buildings with space and water heating. This was a relatively ‘live’ context, in
which opinions may be more actualised than in hypothetical consideration. While
at the time of the study there were no definite plans for district heating, this was
being given serious consideration by local agencies.
Both the qualitative and quantitative results provide an insight into the end-user
(consumer) criteria that retro-fitted district heating will need to meet. Focus
groups with members of the public for whom heat is particularly salient, and a
public questionnaire survey in a locality where a district heating scheme using
waste process heat is plausible, both indicated that while ‘citizen-consumers’ are
favourable to the idea, this support is conditional on a range of conventional
purchasing criteria being met, including acceptable cost, reliability and flexible
contractual arrangements. The ‘practice’ literature from science and technology
studies also implies that district heating will need to ‘fit in’ with the existing
routines and habits of users, if lock-in of a new system is to be achieved.
While there is a need to be cautious when generalising to other cases and
localities, our findings suggest that gaining UK consumer support for a change
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from existing, individualised heat systems to a communal district system based
on process waste heat may stand most chance of acceptance where the heat
supply can be guaranteed at lower than market cost. In this regard, business
vulnerability to market conditions clearly will have a bearing on maintaining a
reliable heat supply for a community. This vulnerability would suggest that
processes or operations that are less at risk from changing market conditions
may be best suited to supplying waste heat. Energy from waste operations are
an obvious candidate and such schemes already exist in the UK (e.g. in Sheffield
and Newcastle). Similarly, on the demand side, supplying concentrated heat
loads (‘heat anchors’) such as blocks of housing or offices, schools, hospitals etc
is likely to be more practicable than recruiting individual households on a street-
by-street basis, where the possibility of non-acceptance may be problematic,
particularly if a commitment for longer than 24 months is required. Overall, even
where supplying waste process heat to district heating schemes makes energetic
sense (i.e. where the heat cannot be recycled internally), it will rarely be
uncomplicated.
Providing a little more detail, while those questioned were broadly supportive of
the idea of district heating, particularly if this would involve reductions in domestic
heating costs, both the qualitative and quantitative work revealed significant
concern about contractual lock-in, hence the title of the paper based on the work:
Don’t lock me in: public opinion on the prospective use of waste process heat for
district heating. In contrast, the stability of long-term demand is highly valued by
those responsible for the supply-side, which obviously sets up a tension that
would need to be resolved.
We also observed some gender differences in the first reactions to district
heating. Specifically, women were more neutral in terms of their stated
propensity to buy a property on the basis of what they have been told about
district heating and were also less certain about district heating. We concluded
that while the results imply that an appeal to the environmental performance of
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district heating with waste heat may be facilitate acceptance, trust-building and
price inducements will also be required to overcome end-user concerns.
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12 Conclusions
This work has shown that some applications of low grade heat recovery can
significantly reduce the greenhouse gas emissions of energy systems. However,
in other very carbon and fuel intensive processes the impact of low grade heat
recovery makes only a very marginal difference to overall greenhouse gas
emissions. It should also be noted that some of the systems studied incur
substantial direct energy consumption (most often electricity) as part of the heat
recovery scheme. Depending on the electricity grid generating mix this may
increase the overall impact in some environmental impact categories. By contrast
other options, such as the organic Rankine cycle, may actually offset process
electricity demand and, where this is the case
Nevertheless there are greenhouse gas savings that can be made with
implementation of low grade heat recovery options. In many cases these are
economically viable based on stakeholder’s stated economic acceptability
criteria. However, there is still little commercial appetite for implementation of
these measures.
There are many reasons why this is the case, but key issues revolve around risk,
capital outlay and location/communication barriers. It seems unlikely therefore
that economic incentives focused on carbon or energy savings would be
sufficient to offset these and result in significantly increased uptake of low grade
heat recovery options. Much more radical interventions would be necessary to
support the necessary infrastructure development to make best use of low grade
heat and it should be noted that it is much easier to plan for future low grade heat
use than to modify established process
Overall the present energy policy context provides no incentive for recovery of
low grade heat, even where this does reduce greenhouse gas emission, since
the carbon reductions achieved would not be formally rewarded e.g. as the
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Renewables Obligation does for renewable electricity or the Renewable Heat
Incentive for renewable heat. However, the disparity between the different case
studies evaluated indicates that if low grade heat recovery were to be
encouraged to promote energy efficiency, great care would be needed to ensure
that an appropriate framework actually rewarded greenhouse gas reductions and
did not inadvertently increase other environmental impacts or electricity
consumption.
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13 Appendix B: List of Published Outputs Peer reviewed journal papers (see full texts below) Walsh, C. and Thornley, P., “Barriers to improving energy efficiency within the process industries with a focus on low grade heat utilisation”, Journal of Cleaner Production, 23 (1) pp. 138-146, 2012 Walsh, C and Thornley, P., “The environmental impact and economic feasibility of introducing an Organic Rankine Cycle to recover low grade heat during the production of metallurgical coke”, Journal of Cleaner Production, 2012 Walsh, C. and Thornley, P., “A comparison of two low grade heat recovery options”, Applied Thermal Engineering, 2012 Upham, P. and Jones, C. “Don’t lock me in: public opinion on the prospective use of waste process heat for district heating”, Applied Energy, available online 17 March 2011. Peer reviewed conference proceedings Walsh, C. and Thornley, P., “Barriers to improving energy efficiency within the process industries with a focus on low grade heat”, Proceedings of SusTEM 2010, Newcastle upon Tyne Upham, P., Chisholm, F. and Jones, C. (2010) “Don’t lock me in: public opinion on the prospective use of waste process heat for district heating” Proceedings of SusTEM 2010, Newcastle Upon Tyne. Walsh, C. and Thornley, P., Lifecycle impacts and techno-economic benefits associated with the introduction of a condensing boiler to a woodchip fluidized bed boiler, 19th Euroepan Biomass Conference, Berlin 2011 Yasmine Ammar, Hanning Li, Conor Walsh, Vinol Rego, PatriciaThornley, Vida Sharifi, Tony Roskilly "Desalination using low grade heat in the process industry: challenges and perspectives", submitted Jan 2012 Reports Walsh, C. and Thornley, P., “Stakeholder views on barriers to utilisation of low grade heat for process efficiency improvements”, University of Manchester, 2010
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Thornley, P. and Walsh, C. “Addressing the barriers to utilisation of low grade heat from the thermal process industries, University of Manchester, 2010 Other outputs 2011 Paul Upham was Invited speaker on public perceptions at the International Energy Agency Committee on Energy Research and Technology, Expert’s research group (EGRD) event "The Transition to a Low-Carbon Society: Socio-Economic Considerations", Will Baden, Austria 24-25 May 2011.
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A comparison of two low grade heat recovery options
Conor Walsh a*
, Patricia Thornley a
a Tyndall Centre for Climate Change Research, Pariser Building, The University of Manchester, Manchester, M13 9PL,
UK
* Corresponding author. Email: [email protected]; Tel. +44(0) 1612754332
Abstract Low grade heat (LGH) recovery is one way of increasing industrial energy efficiency and
reducing associated greenhouse gas emissions. The organic rankine cycle (ORC) and condensing
boilers are two options that can be used to recover low grade heat (<250 °C). This paper assesses
the lifecycle greenhouse gas reduction impacts and discounted payback periods associated with
both technologies. Generation of electricity through the operation of the ORC saves
approximately 11 kt of CO2 annually, but the high carbon intensity of the coking process means
this has a negligible influence (<1 %) on the overall process lifecycle impacts. However, if the
electricity generated offsets the external purchasing of electricity this results in favourable
economic payback periods of between 3 and 6 years. The operation of a condensing boiler within
a woodchip boiler reduces the fuel required to achieve an increased thermal output. The thermal
efficiency gains reduce the lifecycle impacts by between 11 and 21%., and reflect payback
periods as low as 1.5 to 2 years, depending on the condenser type and wood supply chain. The
two case studies are used to highlight the difficulty in identifying LGH recovery solutions that
satisfy multiple environmental, economic and wider objectives.
Keywords: Low grade heat; lifecycle assessment; discounted payback
1 Introduction
The recovery of low grade heat (LGH) has been recognised as a potential means of improving the
energy efficiency of industrial installations. Most industrial installations emit large quantities of
LGH as part of normal operations. Traditionally increasing energy consumption was seen as
being preferable to recovered heat of lower thermal quality. In most instances, the feasibility of
LGH recovery will depend on the thermal quality of the heat as well as its potential uses. Ideally,
recovered heat will have the capacity to be used within the installation itself. Alternatively, “over
the fence” options will have to be evaluated. In order to be appropriate for integration into
existing industrial processes, LGH recovery options must meet a number of the requirements.
Process managers will need reassurances on any new technology, particular if the process is well
established. The need to balance the perceived risk and potential rewards is a barrier to the uptake
of new technologies. Primarily the technology must be suitable for the low temperature range
involved. The integration of any new technology must be unobtrusive in terms of normal process
operation. Any potential technology must be sufficiently flexible to reflect the potential
variability within the process, particularly where the process runs continuously. Once these
requirements are met it is vital that there is a use for the recovered heat, either within or outside
the process. Finally any technology must demonstrate sufficient redundancy to allow it to be
repaired or removed without issue. This paper seeks to inform the expectations of what benefits
can be reasonably expected by the recovery of LGH by assessing its potential for reducing the
lifecycle environmental impact associated with two different industrial processes as well as the
likely discounted payback period (DPP).
1.1 Industrial case-studies.
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Two alternative technologies have been identified as being suitable for the recovery LGH; the
organic rankine cycle (ORC) and condensing boiler. These have been selected as they are
sufficiently different to allow for interesting comparison of the any benefits while demonstrating
established technologies. The first case study identified is the integration of an ORC to recover
heat from the flue gas leaving a coke oven within an integrated steel works. The Rankine cycle is
a thermodynamic cycle which converts heat into work which ultimately generates electricity
through a turbine. It is likely that approximately 80% of the electricity generated globally is a
result of the Rankine cycle. Within a Rankine cycle heat is supplied externally to a closed loop,
which usually uses water as the working fluid. Figure 1 below demonstrates a simplified Rankine
cycle.
Figure 39: simple (Organic) Rankine Cycle taken from Hung et al., [1].
A Rankine cycle which employs water as a working fluid is not economical if recovering heat
below 370°C. For that reason organic chemicals or refrigerants are often substituted for water
within a Rankine cycle, resulting in what has been termed the Organic Rankine Cycle (ORC).
Most organic fluids demonstrate relatively low critical pressures which require ORCs to be
operated at lower pressures and with significantly smaller heat capacities than traditional water-
vapour cycles. In an analogous point to the one discussed above, Lakew and Boland [2] state that
if a process seeks to recover power from condensing vapour then it will be necessary to choose a
working fluid with a critical temperature above that of the source fluid. Therefore, an ORC
system must function below the temperature and pressure at which the fluids are chemically
unstable [3]. McKenna and Norman [4] have identified the iron and steel sector as the largest
user of heat with a heat load of approximately 213 PJ but also demonstrate significant
potential for heat recovery. While a number of streams containing LGH have been identified
within the steel plant but flue gas from coke oven was chosen as being most suitable for recovery.
The coking process itself is integral to modern integrated steel works, as coking coal is the main
reducing agent in the blast furnace. Its suitability is due to the consistent operation of the coke
oven and the high thermal quality compared to other sources of LGH as well as the reduced
potential for process disruption. The gas stream has a temperature of 221 °C with a flow rate of
66 kg/s. This was estimated to yield 21 MW of recoverable energy [5].
One of the most common means of improving thermal efficiency is the introduction of a
condensing boiler to recover latent heat from the waste gas steam. Condensing boilers normally
fall into two main categories, direct and indirect content systems. Within direct-contact
condensing boilers there are no boundaries isolating hot combustion gases from the stream to be
heated. An indirect contact condensing boiler recovers heat from hot flue gases by passing them
through one or more heat exchangers. This benefit is more pronounced for raw biomass systems,
where the higher moisture content means that up to half of the calorific value of the fuel is
recoverable. Both for this reason and its lack of process disruption, Chen et al [6] have examined
the impact of a condensing boiler on a Finnish woodchip fluidized bed boiler which provides heat
for a district heating system. The woodchip plant demonstrates a basic (pre-condenser) output of
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40 MW. The heating systems served by this plant generally consist of water radiators whereby the
return water temperature is between 35 and 40°C. The return water temperature is preheated in
the condenser using both the recovered latent heat of water vapour and the sensible heat of the
flue gas. It is estimated that the condenser increases the thermal output to 52 MW.
2 Material and methods
2.1 Direct carbon savings
The direct carbon saving for both systems are calculated in a different manner. It is estimated that
1 tonne of coke requires 2.95 GJ to produce it. It is estimated that 2% of the energy demand is
satisfied by electricity, 5% is satisfied by steam and 93% by a gas source [4]. This latter may
include natural gas, blast furnace gas or coke oven gas (COG) itself. The calculation of the
emissions associated with the production of coke was based on equation 4.2 published in [7] and
shown below. The equation used in the calculation is shown below:
t CO2/ t coke = [ (1/y)*Ccoal + Σ (Qgas i* EFgas i) – 1* Ccoke ] * 44/12 [Eq 1]
Within the equation y refers to the coke yield (t coke/t coal), Ccoal is the carbon content of coal (%
w/w). Qgas equates to the quantity of gas used in coke production (Gj). EFgas represents the
emission factor for each specific gas (t C/Mj). Ccoke refers to the carbon content of coal (% w/w).
44/12 is used to translate C into CO2. At the steelworks under review, the underfiring gas used in
the production of coke was a mixture of blast furnace gas and COG. Based on the gas stream data
provided by the environment department of Corus, it was assumed that blast furnace gas and coke
oven gas (COG) represented 50/50 % by volume (A. Patsos, Pers. Comm.). As the COG
represents an energy source provided by the oven itself, its emissions are excluded from Equation
1 in order to prevent double counting. The carbon savings are estimated by comparing the
emissions negated though the generation of electricity by the ORC.
The amount of carbon directly emitted is calculated based on the fuel demands and the carbon
content of the woodchips themselves. The direct carbon savings due to the operation of the
condensing boiler within a woodchip boiler are quantified based on the relative fuel savings per
unit of thermal output. In order to accurately calculate fuel savings, an estimate for the thermal
efficiency of the system without the condenser is necessary. Table 1 and Figure 2 below are taken
from [6] and demonstrates the enthalpies of the various boiler process streams. Flow rates and
enthalpy changes are used to calculate the boiler, condenser and combined thermal output, which
in turn allow the thermal efficiency (w/o condenser) to be estimated.
Table 25:
Process stream parameters. Taken from [6]
Description Pressure Stream no. Temp Flow rate Enthalpy
(Bar) (°C) (kg/s) (kj/kg)
Woodchips 1 1 20 5.4 -17.2
Air feed 1 2 20 20.9 -5.15
Flue pre-ESP 1 3 150 26.3 146.7
Flue pre condenser 1 4 150 26.3 146.7
Flue gas to stack 1 5 35 23 10.6
Hot water from boiler 16 6 140 111.6 590
Return water 16 7 30 111.6 128
Preheated water 16 8 55 111.6 231.7
Condensate 1 9 35 3.32 42.4
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Figure 2: Woodchip boiler process diagram, taken from [6].
Based on the change in enthalpy between the hot and return water stream, the thermal output is
confirmed at 52 MW. The change in enthalpies between the hot water and preheated water
streams is used to estimate the output of the boiler itself. Relative fuel savings (not to be confused
with fuel efficiency) per thermal output are estimated using equation 2.
% fuel Savings = 1- ( % efficiency without condenser/ % efficiency with condenser) [Eq 2]
In order to gauge any additional and indirect benefits of LGH recovery, two alternative case
studies (representing offsite and terminal woodchip production) are assessed from a lifecycle and
techno-economic perspective.
2.2 Lifecycle assessment
Lifecycle assessment (LCA) attempts to collate and characterise the environmental impacts
(including climate change as well as wider impact categories) associated with the production, use
and disposal of a product or service. Within LCA all associated impacts are expressed in terms of
a rational reference, termed a functional unit. In order to communicate the lifecycle impact of
LGH recovery, the reference unit is expressed in terms of process output. In this case, 1 kg of
coking coal and 1 MWh of heat are used as the functional units to which energy and resource
requirements as well as emissions are allocated. In order to reflect the impact of both technologies
two lifecycle modules are generated for each case study, reflecting conditions with or without
LGH recovery technology. This is vital as any potential LGH technology will also represent
additional material and energy requirements.
The lifecycle impacts are modelled using a proprietary software package [8]. Using this method
the on-site emissions are estimated for both processes whereas upstream impacts are estimated
based on data provided from literature. For example, the lifecycle impacts associated with coke
production will include the emissions and resource consumption associated with the production
and overseas transportation of coal for the coking process. By contrast the lifecycle impacts
associated with heat from a woodchip boiler will include the emissions and resources embodied
in the cultivation and harvesting of wood residue. Data from a number of sources were used to
generate lifecycle modules for both systems. Because of the amount and diversity of the data
necessary to populate a LCA it is unfeasible to present the data here.
2.3 Techno-economic analysis
It is likely that any attempt to reduce the environmental impact of industrial processes will need
to demonstrate a degree of financial viability. Net Present Value (NPV), represents the difference
between the sum of the discounted cash flows which are expected from the investment and the
amount which is initially invested (equation 3).
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N
nn
n
k
FCNPV
1
0)1(
[Eq 3]
The discounted payback period (DPP) reflects the period in which the cost of investment (and
operation) is recouped. Whereby n is the time period (year), Fn the net cash flow for year n, C0 is
the initial investment, k the discount interest rate, assumed to be 5% and N is the number of years
of the investment’s lifetime or until the invest breaks even. In relation to the ORC it is assumed
that while the external purchasing of electricity is negated the ORC will incur costs due to
installation and maintenance. The Department of Energy and Climate Change estimate that extra
large manufacturing industries paid on average 5.078p (ex vat) per kWh in 2009. The Climate
Change Levy (CCL) for electricity was also estimated at 0.47 p/kWh [15]. For the condensing
boiler the savings due to a reduced woodchip demand is compared against the increased
electricity costs associated with additional fan operation as well as capital and installation costs.
The DPP for the installation in question has already been calculated in [6] whereby relative fuel
savings result in a revenue. In order to provide an alternative, the estimates for fuel savings were
augmented to reflect terminal (onsite) chipping. It is estimated in [16] that the cost at the power
plant for material transported 80 km (average for Finland) is approximately €30-35/solid m3. The
cost of chipping at the terminal is taken from the same source and is estimated at €1.8/solid m3.
Based on estimates of wood density and average annual exchange rates these estimates result in
an increased value of $ 69/tonne, an increase of $9/tonne from the value used in Chen et al. [6].
This changes the impact of wood chip savings and results in a different range of NPV and DPP.
3 Results
3.1 Direct carbon savings
The Aspen Hysys® simulation program was used by the Centre for Process Integration (CPI) at
the University of Manchester to estimate the net energy efficiency of an ORC system used to
recover heat from an equivalent waste stream. In this analysis, it was assumed that Benzene was
the working fluid with a flow rate of 400 kg/mol/h (A. Kapil, Pers. Comm.). The ORC energy
efficiency was calculated at 11% based on the values shown in Table 2.
Table 2
ORC operational parameters in Kj/h. Energy generated Energy consumed Energy supplied Energy released
1,990,000 4,789 17,990,000 15,990,000
The energy efficiency was estimated by subtracting the energy consumed by the pump from the
energy generated by the turbine and dividing by the energy supplied to the boiler. When applied
to the recoverable energy estimate of 21 MW results in an electricity generation estimate of 2.31
MW. (The high hydrogen content of COG results in a higher heat capacity than may be expected
for other combustion gases). The carbon savings due to the offsetting of external electricity are
estimated based on the emission factor for electricity consumption in 2010 [17], taken as 0.54 kg
CO2/kWh. The operational schedule was assumed to be maintained for 8,580 h/y (assuming 98%
availability). This results in an annual carbon saving of 10,702 t CO2. While, when viewed
collectively, this remains a significant carbon savings it does however represent a reduction 1.39
% to the carbon intensity of coke production.
Using data from Table 1, the overall thermal output of the boiler is calculated at 52 MW. Boiler
output is estimated at 40 MW, confirming the output of the condenser at 12 MW. In order to
estimate the thermal efficiency, a value for the thermal input to the boiler is necessary. This is
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estimated using the net calorific value of woodchips (8.16 Mj/kg) and the enthalpies of both the
woodchip and air stream (from Table 1 above). This results in a thermal input estimate of 44
MW, confirming the overall thermal efficiency of the boiler and condenser at 118%, as suggested
in [6]. This also suggests that the thermal efficiency of boiler itself (i.e. without the condenser) is
91%. Using equation 2 it is estimated that incorporating a condensing boiler will result in a fuel
saving of 22%. Given that the estimates in Table 1 include the operation of the condenser, it is
assumed to correspond to this saving, representing 78% of the wood necessary to achieve an
output of 52 MW without the use of a condenser. Based on this the operation of the condenser is
assumed to avoid an additional 38,381 tonnes of woodchip (and associated 36,059 tonnes of CO2)
annually. It must be clarified that the operation of a condensing boiler will not result in a decrease
in the actual amount of carbon emitted from the facility. Rather the increased thermal efficiency
will reduce the carbon intensity per unit of output by allowing for an increased district heating
capacity without the need for additional woodchip inputs (which may presumably offset an
increased fuel use at domestic level).
3.2 Lifecycle savings
Due to the large amount of data involved it is unfeasible to include all the data applied in both
calculations. In order to examine the lifecycle implications of installing an ORC system and a
condensing boiler boiler, process specific information was incorporated into modules generated
by [8]. Two separate modules were calculated for each case study, one in which LGH is
recovered and one in which it is not. Keeping all other factors equal, the impact of LGH use is
estimated using a lifecycle assessment method. The assessment is carried out using CML3 2 mid-
point impact assessment. Within mid-point analysis, inventory results for each environmental
impact category are multiplied by a characterisation factor which equates individual emissions to
a wider impact category. A simple example is the use of global warming potential to estimate
CO2 equivalents. The main stages in the LCA include coal production, transportation and
production of coke itself. It was assumed that coking coal was transported from Newcastle,
Australia by ship and subsequently by rail. The coal and energy (both electricity and gas) required
within the coking process are a fundamental part of LCA. Default direct emission estimates for
coke production were augmented with more recent values [18] and flue stream composition data
for emission of CO2, CH4, and CO [5]. The environmental impact of the production of additional
materials within an ORC system was also included based on the heat exchanger area requirement
(estimated by the Aspen module). Material compositional information for a suitable turbine and
generator system was provided by Siemens (Webster, Pers. Comm.). As can be seen from the
results in Table 3, negating the consumption of electricity has a negligible effect on the overall
lifecycle impact.
Table 3:
Lifecycle impact of producing 1 kg of coke, including LGH recovery.
Impact category Unit No recovery Recovery % Impact
Abiotic depletion kg Sb eqv 0.03 0.03 -0.36%
Acidification kg SO2 eqv 0.01 0.01 -0.43%
Eutrophication kg PO4 eqv 0.0049 0.0049 -0.29%
GWP100 kg CO2 eqv 9.03 9.02 -0.14%
Ozone depletion kg CFC-11 eqv 4.5 x 10-8
4.47 x 10-8
-0.64%
Human toxicity kg 1,4-DB eqv 0.73 0.72 -0.48%
Fresh water ecotoxicity kg 1,4-DB eqv 0.71 0.71 -0.32%
3 CML is an (non English) abbreviation for the Institute of Environmental Sciences at the University of
Leiden in the Netherlands.
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Marine ecotoxicity kg 1,4-DB eqv 1582.89 1577.15 -0.36%
Terr. ecotoxicity kg 1,4-DB eqv 0.01 0.01 -0.54%
Photochemical oxidation kg C2H4 0.02 0.02 -0.01%
Because of its capacity to reduce the relative feedstock demands associated with district heating
the lifecycle effects of LGH recovery are more pronounced. The results below incorporate data
from a number of distinct lifecycle stages such as forest nursery, tree cultivation, felling, as well
as boiler operation. As can be seen from the Table 4, the operation of a condensing boiler (and
associated woodchip savings) reduces the lifecycle impact estimate for most impact categories.
The exception being ‘abiotic depletion’ and ‘freshwater aquatic eco-toxicology.’ This is not to be
unexpected given the increased impacts associated with condensate treatment. Including these
categories, a condensing boiler is seen to reduce the lifecycle impacts by an average of 13%.
Table 4:
Lifecycle impact of producing 1 MWh of district heat, including LGH recovery.
Impact category Unit No recovery Recovery % Impact
Abiotic depletion kg Sb eqv 0.15 0.15 1.48 %
Acidification kg SO2 eqv 1.33 1.03 -22.05 %
Eutrophication kg PO4 eqv 0.37 0.29 -22.08 %
GWP100 kg CO2 eqv 38.79 36.16 -6.80 %
Ozone depletion kg CFC-11 eqv 0.0002 0.0002 -22.95 %
Human toxicity kg 1,4-DB eqv 82.33 65.75 -20.15 %
Fresh water ecotoxicity kg 1,4-DB eqv 9.98 10.26 2.82 %
Marine ecotoxicity kg 1,4-DB eqv 15967.14 15365.26 -3.77 %
Terr. ecotoxicity kg 1,4-DB eqv 0.57 0.49 -14.61 %
Photochemical oxidation kg C2H4 0.10 0.08 -22.49 %
The overall lifecycle impact reduction may seem disappointing considering the associated fuel
reduction, however the requirement to maintain flue stream buoyancy after condensation means
that the operation of the fan consumes significant amounts of electricity. As electricity is a
secondary energy source it will have a greater lifecycle impact (per unit of energy) than
woodchips. Indeed the scale of the temperature drop (from 140 ºC to 35 ºC) means that the net
electrical demand at plant (per MWh) is increased when this supplementary requirement is
included.
3.3 Techno-economic analysis
The (installation, engineering, material) costs associated with the installation of the ORC were
based on a power law relationship between power generation and reported installation costs for
projects of various size. Based on the available thermal energy and the estimated efficiency rating
for the ORC in question, the investment cost of a suitable ORC system was estimated to be 2,023
€/kWe. Up to a certain output (1.6 MWe), the ratio between equipment and total costs rose
linearly with output, beyond which the ratio was seen to level off. On average the equipment and
installation/engineering was seen to contribute to 57% and 43% of total cots respectively. It was
assumed that annual operational and maintenance costs amount to 4% of total investment costs.
Assuming a discount rate of 5%, the offsetting of purchased electricity the proposed project is
seen to break even in 3-6 years, depending on the elements of the calculation. It is reasonable
that 5 years represents an upper limit for an acceptable DPP but a period of 3 years would
probably be necessary to ensure investment.
Table 5
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DPP and NPV for ORC investment based on CCL and Tax. 5% discount rate.
Calculation Cap Ex Cap Ex +25% Cap Ex -25%
DPP (yr) NPV (£)
DPP
(yr) NPV (£) DPP (yr) NPV (£)
CCL, no Vat 4.16 726,858 5.34 538,936 3.03 873,752
No CCL, no Vat 4.59 323,554 5.91 66,120 3.34 543,436
CCL, 17.5% Vat 3.53 489,834 4.52 480,941 2.59 448,748
No CCL, 17.5% Vat 3.84 159,518 4.85 150,625 2.81 195,069
The investments cost associated with the condenser will be determined by the choice of material.
Equipment costs and installation costs are approximately equal. The main ancillary cost
associated reflects the additional energy required to power the flue gas fan necessary to maintain
buoyancy after the stark reduction in flue gas temperature following condensation. Additional
costs include maintenance and condensate treatment. The revenue is based on fuel savings
associated with the increased thermal efficiency. As stated previously, the savings associated with
a different chipping regime has been included to test the sensitivity of woodchip price.
Table 6
Impact of chipping regime on DPP in years. Off-site chipping estimates taken from [3].
Discount rate Discount rate
5% 10% 15% 5% 10% 15%
Off-Site Chipping DPP Terminal Chipping DPP
Stainless Steel 4.75 5.61 6.93 3.7 4.2 4.89
Carbon Steel 1.7 1.82 1.94 1.36 1.45 1.55
As can be seen from the figures above, the increased costs associated with terminal chipping
enhances the benefits derived from fuel savings. The results above suggest that the increased cost
(+ 15%) of woodchips provided by terminal chipping increase the benefit of the any associated
fuel savings, decreasing the DPP by an average of 23%. Based on both analyses it would appear
that the recovery of LGH can be economically feasible although this will depend on the targets
set by industry.
4 Discussion
The results in table 3 show that for the particular LCA weighting system chosen for this
evaluation there is a negligible (<1%) benefit in the overall environmental impact of the coke
production system obtained by installation of an ORC system. The largest improvements are in
the reduction to the extent of fossil fuel depletion, while reductions in carcinogen and respiratory
organic levels are also achieved, correlating with this reduced fossil fuel combustion. There is
also a small reduction in the climate change impact of the overall system achieved by installing
the ORC. This reduction must be viewed within the context of coke production itself. The
recovery of LGH in the form of electricity does not have the capacity to reduce the demand for
coal or gaseous feedstock which, due to the nature of coke production, can not be meaningfully
substituted with electricity. Similarly, the combustion of blast furnace gas prevents the need for it
to be flared. This would have been the case regardless of whether it was used within the coke
oven or not. This case study provides an example of the difficulties in discussing normalised and
overall emission savings. While the percentage reduction in fossil fuel use or global warming
potential achieved is small, the scale of the industry in the UK is large, magnifying its potential
impact. McKenna and Norman [4] estimate an annual coke capacity of 4.31 Mt for the UK and
applying the savings above to this total capacity would result in annual carbon savings of 43,100
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tonnes of CO2. In order to place this value in context, the current target is to reduce UK emissions
by 34% of 1990 estimates. In 1990 the iron and steel sector emitted over 24 MT of CO2. A 34%
reduction would represent 8.2 MT of CO2. The overall carbon savings of widespread ORC
implementation would contribute to 0.5% of the required savings. When viewed collectively this
represents a significant carbon saving and may provide a more advantageous appraisal of the use
of ORC. Corus (who operate the integrated steel facility in question) estimate that 40% of their
electricity demand is currently satisfied by on-site generation such as the use of coke oven and
blast furnace gases. It is estimated that this will save approximately 700,000 tonnes CO2.
Adopting ORC technology may conceivably increase the current emission savings by 6.2%. This
advocacy should however be viewed with a caveat. Modern steel will generally be optimised at
the higher temperature range through pinch analysis and a complex network of neat exchangers.
For that reason, the recoverable LGH within a steelworks may be of insufficient thermal quality
to warrant attention. However the production of coke is a relatively standard process so the
estimate for recoverable energy is presented as being feasible. The effective determinant will be
whether the plant is an integrated steelworks which produces coke onsite or whether coke is
produced offsite and imported directly.
As stated previously the operation of the condensing boiler will significantly reduce the plume
temperature and convective flow, requiring an increased electricity demand. More immediately, this will result in the exhaust appearing as a continuous plume of steam which may contradict
existent planning or environmental licensing and regulation. This may impact upon plant location
and determine additional factors such as stack height. Because of the impact on ambient
temperature on plume buoyancy, the legal implication of plume buoyancy may be regionally
specific. In some instances this may potentially negate the option of installing a condensing
boiler. In examining the lifecycle impact of condensing boiler operation, the impact of an
increased electricity demand is seen to reduce the benefits of a significant fuel reduction. This is
significant given that electricity is a secondary energy source which incorporates the impacts not
just associated with generating the electricity itself but also those impacts embodied in electrical
infrastructure and the production of the primary fuels upon which electricity is dependent. By
contrast, as the impacts are allocated through the functional life of the plant, reducing the material
requirements associated with the condensing boiler has a less discernible effect on the overall
impact. By way of comparison, the lifecycle impacts of reducing the plant based electricity
demands by 50% are examined using the same impact categories as in Table 4. A reduction of the
electricity demand of the condenser fan by 50% results in an average lifecycle impact saving of
17% across all categories. This reinforces the importance of a secondary energy source within
LCA. If the additional electricity demands associated with the condenser can be negated by
electricity savings elsewhere in the plant, the associated lifecycle impacts are reduced by an
average value of 20%. The specific elements of the chosen impact assessment should not be
ignored. By way of comparison, the lifecycle inventory data for the woodchip plant was
reassessed by the Eco-indicator 99 endpoint assessment method (which attempts to quantify
actual human and environmental impacts such as losses to human health and species richness),
normalised to west European conditions. Using this method the actions of the condensing boiler
were seen to increase the lifecycle impact savings to 21%.
It should be mentioned however that the thermal efficiency gains supplied by the condensing
boiler do not provide a realistic appraisal of condensing boiler operations in general. This is due
to the high moisture content associated with flue streams from the combustion of raw biomass.
The plant under review represents one of the largest biomass fuelled plants in Finland and so may
not be representative of condensing boiler applications in general. In other countries, for example,
natural gas may represent a more realistic fuel of choice. In order to reassess the potential impact
of a different fuel choice, the data in Table 1 was replicated by substituting natural gas. (100%
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methane was assumed for simplicity). It is assumed necessary to maintain the same overall output
of 52 MW. The net calorific value of methane and its stoichiometric combustion pathway
(assuming 20% excess air) are used to estimate the flow rate of fuel and flue gas. The flue gas is
assumed to have the same temperature (pre and post condenser) and that the same quotient of
latent and sensible heat is recovered. Based on these assumptions the contribution of the
condensing boiler is reduced from 12 to 7 MW. This serves to reduce the fuel savings from 22%
to 14%. While this does reduce the thermal efficacy of the condensing boiler, and cautions
against an overly optimistic appraisal it does show that a condensing boiler can result in a fuel
savings within different markets.
In undertaking a technoeconomic analysis, the economic value of the electricity displaced by the
ORC is significant and could offer potentially attractive payback periods. (Although this will be
based on the chosen discount rates, increasing the discount rate to 10% is seen to increase the
DPP of the base case by an average of 14%. Increasing the discount rate to 20% increases the
DPP by approximately 72%). However, this is also reliant on the difference between electricity
selling and purchase prices. If the site owner/operator were to sell the electricity the revenue from
this would be much lower than the cost savings incurred by their not having to purchase the
electricity from an external supplier. In other words, reducing the demand for external electricity
will result in a much shorter payback period than can be expected if electricity or carbon offsets
are sold on the market. This is likely to prove significant for other LGH recovery systems where
the capacity to directly use recovered energy (in this case electricity) may not be available.
Despite the benefits of adopting “over the fence” benefits, the economic reality of these scenarios
in the current market may act as a barrier to implementation. By contrast the provision of district
heat through the use of condenser presents a different set of challenges. As opposed to the
operation of the coke oven, heat itself is the main process output. This means that while there
may be a consistent demand for the heat, the peak demand for will need to be satisfied. The
output of the boiler (without the condenser) represents full fuel feed so it could be argued that the
additional output of the condenser may (wholly or partially) satisfy peak demand. In that regard
value of the additional heat may represent more rational revenue for the condenser than a
reduction in woodchip demand. Assuming an average price of 45 €/MWh [19] for Finnish district
heat and an operational period of 7,000 hours pa, this additional output of 12 MW is seen to
reduce the DPP to 1.4 and 0.54 years for stainless and carbon steel respectively.
Perhaps the most interesting point of debate is the implications for determining both assessment
criteria and targets for the recovery of LGH. Both case studies represent fundamentally different
systems with which to recover heat in different forms. This also reaffirms that each case study for
the recovery of LGH must be viewed within its own context. While the electricity generated using
the ORC does not demonstrate a significant reduction per functional unit it is important to
remember the ORC cannot change the feedstock demand of an inherently carbon intensive
process. However the overall carbon savings may be seen as being significant. By contrast the
operation of the condensing boiler will not result in a reduction in the actual emissions and
requires extra demand (which again questions the wider applicability of the assessment) to
capitalise on this increase in thermal efficiency. The question of whether overall or normalised
reduction targets should be adopted require more input than be afforded by two case studies.
However theses studies raise two important points. Firstly, the fundamental variability in LGH
supply and recovery will frustrate attempts to standardise any assessment criteria and targets.
Secondly it is likely that lifecycle resource demands will reduce the impact of LGH recovery and
has implications for any proposed targets.
5 Conclusion
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The economic and environmental analyses provide disparate appraisals of the impact of the ORC
to recover LGH from flue gas emitted during coke production. The process under review is a
carbon intensive process, particularly when blast furnace gas is used. Despite this, the potential
savings due to on-site electricity generation suggest a DPP of less than 4 years. This is reliant on
the difference between electricity selling and purchase prices. The operation of a condensing
boiler has been shown to increase plant thermal efficiency from 91% to 118%. This increases the
thermal output to 52 MW. However the use of the condensing boiler necessitates additional
electricity consumption which reduces the lifecycle benefits of the condenser. In economic terms,
the DPP associated with the condenser varies significantly depending on material type, discount
rate and chipping regime. While many estimates fall within a timeframe of 5 years, it appears that
carbon steel represents a more feasible material choice, particularly if terminal chipping is used.
Overall the results demonstrate that LGH is a variable resource whose utility and capacity to
reduce emission and improve process efficiency will depend not just on the process itself but on
the form in which it is recovered as well as the apparent demand. This variability means that any
proposed criteria or targets for LGH recovery will have to be sufficiently tailored to be widely
applicable but also highlight the advantages which may not be immediately apparent (such as in
the case of the coke oven). Overall it should be reaffirmed that “win-win” scenarios which
perform favourably from both an environmental and economic perspective are possible through
the recovery of LGH.
References
[1] T.C. Hung, Y.T. Shai, S.K. Wang, A review of Organic Rankine Cycles for the recovery of
low-grade waste heat. Energy. 22 (1997) 661–667.
[2] A. Lakew, O. Boland, Working fluids for low-temperature heat source. Applied Thermal
Engineering. 30(2010) 1,262–1,268.
[3] B.T. Liu, K. Chien, C. Wang, Effect of working fluids on organic Rankine cycle for waste
heat recovery. Energy. 29 (2004) 1207–1217.
[4] R.C. McKenna, J.B. Norman, Spatial modelling of industrial heat loads and recovery
potentials in the UK. Energy Policy. 38(2010) 5,878-5,891.
[5] University of Newcastle, National sources of low grade heat available from the process
industry EPSRC: Thermal Management of Industrial Processes. (2010).
[6] Q. Chen, K. Finney, H. Li, X. Zhang, J. Zhou, V. Sharifi, J. Swithenbank, Condensing boiler
applications in the process industry. Energy (2010) Article in Press.
[7] IPCC. Gomez at al., Stationary combustion, In: H. S.Eggleston, L.Buendia, K.Miwa,
T.Ngara, and K.Tanabe (Eds). Guidelines for National Greenhouse Gas Inventories, prepared by
the National Greenhouse Gas Inventories Programme, IGES, Japan, (2006) pp.2.1-2.47.
[8] Pre Consultants, Simapro lifecycle software [v7.2], (2007). Amersfoort, the Netherlands.
[9] M.L. Juntunen, Use of pesticides in Finnish forest nurseries in 1996. Silva Fennica 35(2001)
147–157.
[10] S. Berg, T. Karjalainen, A comparison of Greenhouse gas emissions from forest operations
in Finland and Sweden. Foresty. 76(2003) 271-284.
[11] S. Berg, E.L. Lindholm, Energy use and environmental impacts of forest operations in
Sweden. 13(2005) 33–42.
[12] E.L. Lindholm, S. Berg, P.A. Hansson, Energy efficiency and the environmental impact of
harvesting stumps and logging residues. Eur J Forest Res. 129 (2010)1223–1235.
[13]Y. Aldentun, Lifecycle inventory of forest seedling production: from seed to regeneration
site. Journal of Cleaner Production. 10 (2002) 47–54.
[14] M. Wihersaari, Greenhouse gas emissions from final harvest fuel chip production in Finland.
Biomass and Bioenergy. 28 (2005) 435–443.
EP/G060045/1 Final Report
136
[15] DECC, Energy price statistics. Department of energy and climate change,(2010). London.
[16] J. Laitila, Cost structure of supply chains in Finland, (2005).Finnish Forest Research
Institute. NorthernWood Heat Symposium. [17] AEA, Guidelines to Defra GHG Conversion Factors for Company Reporting. (2010).
London.
[18] USEPA, Emission Factor Documentation for AP-42 Section 12.2. Coke Production: Final
Report. United States Environmental Protection Agency. (2008). North Carolina.
[19] J. Kostama, District Heat Price Formation Perspectives in Finland. Finnish Energy Industries
(2011)
EP/G060045/1 Final Report
137
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138
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139
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References [1] Pre Consultants, Simapro lifecycle software [v7.2], (2007). Amersfoort, the Netherlands. [2] University of Newcastle, National sources of low grade heat available from the process industry EPSRC: Thermal Management of Industrial Processes. (2010). [3] Hung, T.C., Shai, Y.T., Wang, S.K. 1997. A review of Organic Rankine Cycles for the recovery of low-grade waste heat. Energy. 22: 661–667. [4] Kapil, A., 2010. Personal communication. [5] NPI. 2011. Submitted emission estimates for New South Wales coal mines. Australian National Inventory. Department of Sustainability, Environment, Water, Population and Communities. Canberra, Australia. [6] Walsh, C., Thornley, P., 2012. The environmental impact and economic feasibility of introducing an Organic Rankine Cycle to recover low grade heat during the production of metallurgical coke. (In press). [7] Goedkoop, M. and Spriensma R. 2000. The Eco-indicator 99 – A Damage-oriented Method for Life Cycle Impact Assessment. Methodology Report (second ed.) Pré Consultants, B.V. Amersfoort, The Netherlands (17-4-2000) [8] Chen, Q., Finney, K., Li, H., Zhang, X., Zhou, J. Sharifi, V. Swithenbank, J. 2012. Condensing boiler applications in the process industry. Volume 89, Issue 1, January 2012, Pages 30-36 [9] Walsh, C., Thornley, P., (under review) A comparison of two low grade heat recovery options. Paper submitted to Applied thermal engineering. [10] Amnar, Y., Li, H., Walsh, C., Thornley, P., Sharifi, V., Roskilly, T., (under review) Desalination using low grade heat in the process industry: challenges and perspectives. Paper submitted to Applied thermal engineering. [11] Raluy, 2009 Evaluación ambiental de la integración de procesos de producción de agua con sistemas de producción de energía. Final year dissertation, University of Zaragoza (2009) [12] Wall, G. 1986. Exergy flows in industrial processes. Chalmers University of Technology .