design building energy and environmental management and control system

7/29/2019 Design Building Energy and Environmental Management and Control System

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Lecturers:Tim DalyPdraig McGuigan

Kimmitt Sayers

DESIGN: BUILDING ENERGYAND

ENVIRONMENTAL MANAGEMENTAND

CONTROL SYSTEM

4th Year Sustainable Design Practical Final Report

Candidate: Chris Pullen

Student Number: D00131950

Course Code: DK_ESUSD_8 Y4


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DESIGN: BUILDING ENERGYAND

ENVIRONMENTAL MANAGEMENTAND CONTROLSYSTEM4th Year Sustainable Design Practical Final Report

FOREWORD

This document and the content herein have been compiled from

research and practical works carried out by Chris Pullen at Dundalk

Regional Technical Institute and at various select Industrial premises

within Ireland. This project has been submitted for assessment and

consideration for credit against an Honours Engineering Degree in

Sustainable Design.

This project was extremely large in scope from the perspective of

design and technical difficulty. It was not possible to completely

design, test and deploy the complete system concept within a

timescale of only 2 semesters due to the limited resources available.

However the aim of this project was to implement some of the more

novel and exciting features of the system and thereby prove the

concept is valid and technically sound. A Bonus target was set to

actualise the project into a saleable product and to validate the

concept by proving that a real life market exists.

The product name will be referred to by the acronym BEEMS for

conciseness within this report (Building Energy Environmental

Management System). The concepts and features developed for

BEEMS were borne out of a great deal of application of thought and

experience in Industry by Chris Pullen. A consultation process was

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undertaken with Dundalk Institute of Technology Engineering faculty

and with several potential Industrial customers.

The consultation process had three important outcomes.

Firstly it confirmed through market and technical research that the

product concept was not only feasible but also marketable.

Secondly it highlighted and prioritised desirable features of the

product.

Thirdly it allowed the project Supervising Lecturers at Dundalk

Institute of Technology assess and guide the project from a broader

perspective, which included industry data and feedback, rather than

working with just theory and a prototype. This allowed for the setting

of clear project deliverable goals.

There are of course similar products on the market. Building

management systems (BMS) are very common indeed. However what

has been achieved with this product is a smarter, more cost effective

and more flexible system design than anything else currently available

on the market. The product delivers real cost savings and

environmental benefits. It is cost positive due to the short return on

investment (ROI) period. Therefore the BEEMs system is a triumph in

Sustainable Design and a validation of the ideology and thesis behindthe Sustainable Design Practical course at Dundalk Institute of

Technology.

STATEMENTOF ORIGINALITY

I hereby state that the works and concepts carried detailed in this

report are the work of Chris Pullen except where other sources arecredited. All site works and data collection has been undertaken by

Chris Pullen and not by any other party. This is an original work and

assistance from third parties is acknowledged at the end of the main

report.

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Signed:_____________________

Dated:______________________

Table of ContentsForeword .......................................................................................................... 2

Statement of Originality .................................................................................. 3

Table of Contents ............................................................................................. 4

........................................................................................................................ 8

Goals for Semester 2 ........................................................................................ 8

BEEMS Goals Category Diagram .................................................................... 11

Gantt Chart .................................................................................................... 12

IPPC licence Management through BEEMS ..................................................... 13

Instrument Panel with IPPC configured BEEMS at BOC Gases .....................15

Discharge to Sewer Monitoring point ........................................................ 15

Rain Gauge Installation .............................................................................. 15

Trend produced illustrating discharge flow ................................................. 15

Trend produced illustrating site rain fall..................................................... 16

Trend produced illustrating discharge flow and Rain fall............................17

The Mogden Formula ................................................................................... 18

Understanding the Mogden Formula ........................................................... 19

Calculating Rainfall footprint of BOC site .................................................... 21

IPPC BEEMS Business Potential................................................................. 21

Business Potential of IPPC Market in Republic of Ireland .............................22

Boiler / Energy Efficiency Management through BEEMS .................................24

Boiler Under BEEMS Control........................................................................ 25

BEEMS Algorithm for monitoring the Boiler efficiency .................................27

The following plots are MatLab modelling results of the algorithm.............32

MatLab Code for BEEMS System Boiler Control Algorithm ........................ 40

Photos - Trinity College Boiler rooms at the McNamara Building ................45

Photos from DkIT Boiler house - appraisal visit .......................................... 45

Costings for the implementation of Boiler Energy Efficiency Testing .........45

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Costings for the implementation of Fugitive gas with BEEMS system .........45

BEEMS system hardware costs (additional to either of above costs) ..........46

Tumble Dryer Energy Management through BEEMS ......................................47

Testing Strategy .......................................................................................... 48

Sensor and Transducer Installation ............................................................. 50

Handheld Instruments and BEEMS data logging .........................................50

Data Analysis - Graphical Display (Mimics) for live test data .....................50

Data Analysis - Trending modified-v-unmodified Vent Temperature ..........51

Data Analysis - Trending modified-v-unmodified Power Consumption ...... .54

Data Analysis - Spreadsheet Power difference in Vented Air Masses ........55

Sample Open Source Code for Dryer Testing ..............................................55

Fugitive Gas Detection and Alarming using BEEMS ........................................56

....................................................................................................................... 56

About Fugitive Gas ...................................................................................... 57

Fugitive Gas Monitoring Setup .................................................................... 59

Data Analysis Trending of Boiler House Gas Data ................................... 59

Flexible connectivity ................................................................................... 63

Mimics and user interface ........................................................................... 64

Example: Trending viewed through Internal Wed server mimic ...............65

Example: Live data viewed through internal web server mimic ...............65Example: Data retrieve embedded into internal web server interface .....66

Example: Channel Text listing viewed through internal web server ........66

Data trending .............................................................................................. 66

Alarm Logging and Reporting ...................................................................... 68

Example: Text Report file generated from logged data .......................... 68

Example: Alarm report automatically generated .....................................69

Legislation and funding .................................................................................. 70

Acknowledgements ........................................................................................ 70Appendix ........................................................................................................ 71

Sample Open Source Code for Dryer Testing .............................................71

DT85 Specification sheet ............................................................................. 73

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Summary of Semester 1 Report Content

The report compiled for Semester 1 covered the following topics:

Identify and establish a need for the product.

Industrial User Interviews (potential customers)

o BOC Gases

o Green Star , KTK facility

o Trinity College Dublin

Product Definition

o Product description

o Problem Statement

o Product Definition

Topology of BMS systems currently on the Market

The BEEMS system

o Overview of advantages of BEEMS over existing BMS

o BEEMS with conceptual Data over Mains (SCOM) system

o Diagrammatic Illustration of Key BEEMS applications

o Narrative of Key BEEMS applications

Project Objectives

o Degree Credit

o Sustainable Product Design

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Monitoring

Control

Reporting

o Cost Savings

Integration of OTS (off the shelf) Systems

Intelligent Application and Bespoke Algorithms

o Diagnostic and Analysis tools

Facilities (Estates) Management

Health and Safety Management

Personnel Management

Environmental Management

INvironment and Psychrometric Management

Energy Management

o Triple Bottom Line and Capital expenditure justification.

o Proposed Project Targets for Semester 2

Boiler / Energy Efficiency Management through

BEEMS

IPPC licence Management through BEEMS

Data over Mains (SCOM) communications with

BEEMS

GUI with BEEMS

Mimics and user interface

Data trending

Alarm Log / Reporting

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2-way SMS alarming and control through BEEMS

Outline of preliminary Boiler Control and management

Algorithms.

Outline of preliminary IPPC management Algorithms.

Acknowledgements.

Appendix of Research and support materials.

Glossary of terms.

GOALSFOR SEMESTER 2

At the beginning of semester 2, after consultation with Tim Daly and

Pdraig McGuigan, it was decided that the BEEMS project was in

danger of becoming undeliverable unless tight goals were defined and

focus on. The concept of the BEEMS system is extremely large in its

scope and the duration of semester 2 was quite limited. In order to set

these goals the following was done:

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Progress from semester 1 was reviewed.

Features required to market BEEMS to industry were considered.

Available time, equipment and facilities were assessed.

Progress from Semester 1 was reviewed and it was felt that the

marketing and research strongly suggested that the BEEMS concept

was viable as a product. The technologies involved were realistic and

the costings indicated the product would be attractive to consumers.

Critical analysis suggested that the project would have to be narrowed

significantly in order to successfully deliver the project on time and

functional.

Features required to market BEEMS to industry were considered. In

order to market the system the GUI would need to allow the customer

to trend historical data, to allow live viewing of parameters on screen

(preferably via an mimic), a reliable connection to the customers PC

must be available and alarming and reporting would be required. The

BEEMS system concept incorporates a suite of Pre-configured tools for

handling various building, energy and environmental management

tasks. At least 1 of these pre-configurations would have to be

completed and tested to deliver a product to market. Critical analysis

suggested that a Beta site would be required to prove the system

works as expected and to quantify a cost-benefit analysis for

marketing purposes.

Available time, equipment and facilities were assessed. It was decided

that the amount of work to be done in semester 2, in order to deliver a

completed project, was immense. The time scales were very tight so a

Gantt chart was produced and the tasks associated with each goal wasentered onto it. This allowed for the time management to be quantified

and some facets of the project (such as the SCOM system) proposed in

semester 1 were not pursued in semester 2. The Gantt chart was a

very useful tool to critically analyse the time management of this

project.

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The outcome of this process was the setting of the following goals:

1) IPPC licence Management through BEEMS

2) Boiler / Energy Efficiency Management through BEEMS

3) Fugitive Gas Detection and Alarming using BEEMS

4) Back End (User end) software interfacing to BEEMS

a. Mimics and user interface

b. Data trending

c. Alarm Log / Reporting

Some Bonus goals were set but would only be pursued after goals 1

4 above were achieved or significantly progressed.

5) To implement SMS alarm and control.

6) To realise the marketing and sale of the BEEMS system to

Industry.

Critical review of the project as outlined above also lead to the decision

not to proceed any further with the SCOM feature. The time and

resources required to deliver this feature were too great for the scope

of this project. The decision to sideline the SCOM feature was

important and was a result of the application of project planning and

management.

The Diagram shown on the following page (10) illustrates the goals set.

Each Boxed area of the BEEMS system represents a project goal. These

goals were laid out chronologically and broken down into sub tasks.

They were charted on a Gantt chart which is included on page 11 of

this report.

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BEEMS GOALS CATEGORYDIAGRAM

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GANTT CHART

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IPPC LICENCE MANAGEMENTTHROUGH BEEMS

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The Diagram on the previous page (12) shows the IPPC management

portion of the BEEMS system indicated in a grayed out box. An IPPC

licence is an integrated Pollution Prevention Control licence. These are

issues by the EPA (environmental protection Agency) to industrial site

operators where there is trade effluent deemed potentially hazardous

to the environment. The IPPC licence imposes strict monitoring and

control criteria on the licensee. Ongoing monitoring of effluent quality

and quantitive parameters is required to ensure that any exceedances

above or below limits set out in the IPPC licence are recorded and

reported.

IPPC licence holders must bear all costs for the implementation of the

IPPC licence. Apart from the Cap-ex costs of the monitoring equipment

they also face significant ongoing cost for the reporting and

maintenance of their system(s) to comply with their licence conditions.

As part of this project a configuration was developed for the BEEMS

system to minimize the human input required to implement the IPPC

licence. This is by itself an attractive feature from a cost saving point

of view. However an additional Algorithm was developed to quantify

the volume of rain fall ingress from the customers site footprint into

the fouls sewers. Rain water (or storm water) should be separated out

and directed to the storm water drains where it then is discharge to

rivers. When storm water ingress to foul sewers occurs the IPPC licence

holder must pay the local authority for the treatment of this rainfall asif it were trade effluent. The charge for this treatment is based on the

Mogden formula and is qualitive as well as quantitive based.

The BEEMS system allows the IPPC holder to prove to the EPA and/or

local authority that a quantified volume of the trade discharge is in fact

storm water. This is used to then reduce the treatment bill form the

local authority. It can also be used internally (within the IPPC licensed

company) to justify cap-ex budgets and to calculate the time to recoup

the costs of remedial civil works through reduced charges.

The IPPC configured BEEMS system was installed in BOC Gases on the

Naas road in Dublin. The configuration was localized to their particular

licence and Beta tested over the course of 2-3 months. Data was then

collected and analysed and reports generated for the customer to

supply to the EPA for their quarterly report. The volumes of rainfall and

the man hour savings were quantified. BOC were very happy with the

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results and the ROI period. They decided to purchase the system and

are now considering systems for Belfast and Cork. The following pages

show the Beta system in situ as well as the data analysis and the

calculations required for the discharge to sewer monitoring.

Instrument Panel with IPPC configured BEEMS at BOC Gases

Diagram removed;

Discharge to Sewer Monitoring point

Rain Gauge Installation

Gauge Mounted on Pole. Self emptying Tipping

Mechanism.

Trend produced illustrating discharge flow

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Trend produced illustrating site rain fall

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Trend produced illustrating discharge flow and Rain fall

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The Mogden Formula

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Factories and soon restaurants, pubs, farms and hotels will have toadhere to strict effluent discharge conditions as set out in an IPPClicence. If this effluent (waste water) is discharged to foul sewer it willultimately end up in a municipal sewage plant. Since the localauthority has to burden the cost of treating this effluent it recoups the

cost (plus a handsome profit) by levying charges per cubic metertreated. These charges are calculated based on a number ofparameters based on the quantity and quality of the effluent.

In order to accurately and repeatedly assess the charges to be leviedon the IPPC licence holder an industry standard formula called theMogden formula is employed.

The Mogden Formula is as follows:

C = R + V + Vb + (B x Ot/Os) + (S x St/Ss)

Where:

C = Total charge rate for disposal (Euro/cubic metre)

R = Unit cost for conveyance (Euro/cubic metre)

V = Unit cost for volumetric treatment (Euro/cubic metre)

Vb = Additional volume charge if there is no biological treatment

B = Unit cost for biological treatment (Euro/cubic metre)

Ot = COD of trade effluent (mg/l);

Os = COD settled sewage (mg/l)

S = Unit cost for sludge disposal (Euro/cubic metre)

St = Solids value trade effluent (mg/l);

Ss = Solids value* settled sewage (mg/l)

Understanding the Mogden Formula

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Waste water treatment costs are dependent on the local authorityrates as well as:

1) Volumes discharged

2) COD (Chemical Oxygen Demand)

3) Suspended Solids

4) BOD (Biological Oxygen Demand)

The treatment charges can be reduced by reducing any of theparameters in the Mogden formula. The BEEMS system reduced thesecosts by accurately quantifying the discharge volumes, quantifying theration of trade effluent versus storm water and on larger systemscalculating the costs at the licence holders site itself by measuring the

water quality parameters using inline probes.

The Mogden formula also allows for the charging for treatment forstorm water volumes (Vb) as well as trade effluent volumes (V). This isbecoming more and more common as local Authority want industry toremediate their storm water and foul water systems. This reduces thetreatment plant energy costs but saddles effluent discharge industrieswith the cost of the remedial civil works. However the BEEMS systemwill still significantly reduce the treatment costs as the tariff for Vb canbe as little as 20% of the tariff for V. Therefore the more storm waterthat can be proven to be a component of the total discharge volume,

the lower the treatment cost.

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http://www.oasisenviro.co.uk/chemical_oxygen_demand.htmhttp://www.oasisenviro.co.uk/chemical_oxygen_demand.htm


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Calculating Rainfall footprint of BOC site

Site footprint in square meters = 19,918 m3

Total Q1 rainfall in meters = 0.1253 m (from Rain gauge / BEEMS)

Total rainfall Discharged Q1 2011 = 2,496 m3 ( Total Rain x Footprint )

Estimated Annual Rainfall discharge to sewer = 10,000 m3

Estimated treatment cost for Rainfall discharge = 8,700 per annum

BEEMS system cost = 10,000 (once off Capital expenditure)

BEEMS maintenance cost = 1,100 per annum

Estimated Return on Investment Period = 18 months.

IPPC BEEMS Business Potential

ORDERS FOR SYSTEM ALREADY ACHIEVEDClient Description Order Value

BOC Gases IPPC Management system Dublin Plant ~ 10,000

[ Cork and Belfast sites under consideration]

Uisce Technology IPPC Management system - Trial site #1 ~ 6,000

Uisce Technology IPPC Management system - Trial site #1 ~

6,000


6,000


6,000

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[Up to 20 sites per year under consideration]

IFM IPPC Management system Galway Trial ~ 4,000

[ 3 Other Sites under consideration]

Total~

38,000

The figures above illustrate the IPPC configured BEEMS system sales as

of 20th April 2011. The difference in the cost for the systems is

accounted for as follows:

The BOC order was a complete turnkey supply, install and commission.

Hence the system cost is full anticipated retail cost.

The Uisce Technology orders are on a supply and final commissionbasis and the installation costs are borne by the customer. So there

are significant Labour and sundry parts savings compared to the BOC

sale. Also there is some quantity discount included as the customer

ordered 4 systems to assess.

The IFM sale is for supply only and all install and commission is at the

remit of the customer. Hence the relatively low price.

The differing technical support requirements of these orders will help

to highlight any technical issues with the product delivery.

Business Potential of IPPC Market in Republic of

IrelandEPA Licences granted in 2009: 54

EPA Licences granted in 2008: 40






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Total Number of IPPC licences listed on EPA.ie : 1025

County/City councils: 34

Borough councils: 5

Town councils: 75

Estimated Number of Discharge licences : 4000

10% Market share: 500 @ 5,000 = 2.5 million

[conservative]

20% Market share: 1,000 @ 7,000 = 7 million [optimistic]

Potential added value items (service & spares): = 375K - 1

million

Gross Profits: 40% (Product Sales), 55% (Value added Service

work).

These figures are current market value not per annum values. However

legislation changes will bring Restaurants, hotels, bars, farms and

many other industries under the IPPC umbrella making the figure

above realistic annual targets going forward.

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BOILER / ENERGY EFFICIENCY MANAGEMENTTHROUGH BEEMS

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The above diagram shows the Boiler / Energy Efficiency Management

feature of BEEMS as indicated by the grayed box which is the second

project goal.

Boiler Under BEEMS Control

Theory of operation

In Figure B1 above the Thermostat (1) is a temperature sensor,

normally a simple Bi-metallic strip, used for crude ON/OFF control of

the Boiler Burners. With the BEEMS system temperatures from several

rooms could be aggregated to determine the burner requirement. The

circulation Pump (11) could be replaced with a Variable speed drive

under the governance of the BEEMS system. This would allow for very

close matching of output to demand. It would also reduce overshoot ofthe system which would reduce Energy wastage. In order to reduce

costs and for ease of retrofitting existing Boiler systems the BEEMS

system could employ existing circulation pumps with the addition of a

proportional control valve. The BEEMS system could implement PID

control techniques on the control system to give a very smooth and

accurate environmental response.

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The Boiler Controls (2) would still operate the boiler but only in thecrude ON/OFF fashion originally designed for. The Controls (2) only job

is to switch the Burner (3) in and out as demand requires. This is done

by means of an integral solenoid valve and switching power through to

the Fuel Pump (4) (Note: if the fuel is gas then there is no fuel pump

but rather a spring closed valve known as a slam shut valve).

The Fuel Pump sucks oil through the filter (5) and delivers it under

moderate pressures to the burner nozzles which protrude into the Burn

Chamber (6). The nozzles need oil delivered at pressure so they can

aspirate (create a mist or spray) the oil which allows for efficient

mixing with the second combustant, i.e. Oxygen from air. This fuel/air

mix is ignited by either a spark or a pilot flame (depends on the fuel

and the system setup). During operation the combustion box gets

extremely hot. At this point the combustion thermal energy heats the

Heat Exchanger plates (10) (Also called the Boiler because this is

where the water in the system gets heated). The Hydronic system

passes water (or a thermal oil or a water glycol mix) through the heat

exchanger via a series of small gauge pipes that pass back and forth

through the heat exchanger block.

The heated water is pumped through the delivery side of the system

by means of a Circulation Pump (11). An Expansion Vessel (12) is

located on the hot feed flow line from the Boiler. This is a critical piece

of equipment. Since the system is a closed loop system and water

cannot be compressed, then , should the water get too hot it cannot

expand or compress. It would be trapped in the pipe work of fixed

dimensions until the heat energy in the water would overcome the

material strength of the weakest parts of the system. The result would

be a blow out with super hot water (steam) being ejected. This wouldbe very dangerous as well as costly in terms of damages. The

expansion vessel is filled with slightly compressed air (0.5 to 1 bar in a

domestic system would be typical). As the system heats up the water

can expand by further compressing the air in the expansion vessel.

Since the water cannot be compressed then the force of the air in the

vessel expanding is transferred hydraulically through the water. A

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Pressure relief valve close to the boiler will blow open relieving the

pressure in a controlled fashion should the system get too hot and

therefore the system pressure get too high.

BEEMS Algorithm for monitoring the Boiler efficiency

The concept behind the BEEMS system is to allow for intelligent

management of energy and environmental parameters within the

remit of its building management role. Therefore as part of this project

two features were explored that offer users real cost savings and

genuine improvement over current typical setups. These features are

not exhaustive. They are to demonstrate the potential of the product.

Time and resource limitations prevent further development of the

Boiler management at this time.

The first feature explored is the monitoring, in real time, of the boiler

efficiency. This allows users to then:

View trends (historical data)

Generate energy efficiency reports The data can be used as a

powerful tool to

o Improve the boilers usage

o Determine optimal service intervals

Generate Automatic alarms via various communications methods

(especially SMS)

Allow for restarts in trip situations remotely (especially by SMS)

Monitor and report TWA (Time Weighted Averages) Health and

safety requirements.

Monitor and report STEL (Short Term Exposure Levels) Health

and Safety requirements.

Automatically trigger a Slam shut valve on the gas supply line in

case of fugitive gas detection (gas leak)

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The algorithm developed for the energy efficiency is as

follows:

Energy out of Boiler = [(Tout Tin) x Qout*] x Specific heat

capacity of hydronic fluid

*Qout = hydronic fluid mass flow = Volumetric flow x Specific gravity of

fluid.

Energy in to Boiler = Qin x Net Calorific value of fuel.

Energy out

Boiler Efficiency % = Energy in X 100%

As already stated the concept behind the BEEMS system is to allow forintelligent management of energy and environmental parameters

within the remit of its building management role. So the Algorithm

developed above is a very useful management tool for monitoring the

boiler operation. The next step is to develop another Algorithm for the

BEEM system to Control the boiler operation as efficiently and

smoothly as possible.

To summarise the exploring Figure B1 the BEEMS system could be

used to tightly and smoothly control the output of the boiler based on

temperature(s) in the INvironment. The thermostat would becomeonly a fall back control (becomes a failure scenario rather than the

norm in an ON/OFF control methodology). The addition of a

Proportional control valve on the Boiler output would allow for variable

flow rates and therefore variable heat delivery into the Invironment.

The following is the development of an Algorithm to control this

Variable heat delivery system.

Figure B2 illustrates the Algorithm in a block diagram fashion. The set

point is the desired temperature to be obtained from the heating

system. The controller is the BEEMS system ( or more accurately thealgorithm about to be developed here). The Valve is the proportional

control valve that can be used to throttle the Heat flow rates. The heat

Exchanger is the Boiler itself and would need to be tuned for each

Boiler by plugging in manufacturers data. Alternatively the Boiler

Efficiency algorithm previously developed could be used to achieve

this tuning. The last element in the model is the feedback component

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which is a temperature sensor (or sensors) placed in the Invironment

to be temperature controlled. The Controller will require PID

(proportional Integral and Derivative) functionality, the Valve will be

modelled by a first order lag plus gain, the Heat exchanger will be

modelled by two first order lags plus gain (which is really one second

order component broken into two first order factors i.e. one for each

temperature component in the heat exchanger), The temperature

feedback is also modelled by a first order lag and gain.

Now that a block diagram model for the control system has been

developed it is necessary to progress to a mathematical model. This is

done below in Figure B3. First the open loop response for the model is

determined. This was done using a software package called MatLab.

The Program used for the system model can be seen on page 31.

However first a discussion of the Model illustrated in Figures B3 & B4

(page 30) is required.

Under ideal circumstances the temperature out of the system (right

hand side) should be equal to the set point fed into the system (on the

left hand side). However disturbances in the system will lead to errors

and therefore drift between the desired and the actual temperature.

To overcome this the controller will act to position the valve until theheat released from the heat exchanger is such that the output

temperature is equal to the set point (actual = Desired). Therefore the

error will be Zero, The temperature sensor provides the signal being

fed back for comparison with the set point and the control will act

proportionally to the size of the error between the actual and the

desired temperatures.

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The following mathematical operators have been used:

s=tf('s'); S is a Laplace Transfer function so we dont need to do any

calculus

Kp= PID proportional gain constant

Kd= PID derivative gain constant

Ki=PID integral gain constant

Kv=Valve Gain constant

Kh=Heat Exchanger gain constant

Kt=Temperature transducer (feedback loop) gain constant

Tv=Tau constant for Valve

Th1=Tau constant 1 for Heat Exchanger 1st order

Th2=Tau constant 2 for Heat Exchanger 1st order

Note: (Th1xTh2) gives a 2nd order

Tt=Tau constant for temperature transducer.

T=Sample period

A = Kp*(1+Kd*s+Ki/s) = PID response model

V = Kv/((Tv*s)+1 = Valve response model

HE= Kh/(((Th1*s)+1)*((Th2*s)+1)) = Heat Exchange response model

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TT=Kt/(Tt*s+1) = Temperature feedback sensor response

model

OLT=(V*HE*TT) = the Open loop transfer function to a step response

of the system

CLT=(Kp*V*HE*TT) = the Closed loop Transfer Function (PID excludedfor OLT not for CLT)

CLT=(Kp*V*HE)/(1+(Kp*V*HE*TT)) = Calculate Closed loop function

with feedback, P only.

CLT=((Kp*(1+Kd*s))*V*HE)/(1+((Kp*(1+Kd*s))*V*HE*TT)) = Add

Derivate gain to PID model.

CLT=((Kp*(1+Kd*s+(Ki/s)))*V*HE)/(1+((Kp*(1+Kd*s+(Ki/s)))*V*HE*TT))

= Add I gain to PID .

The following plots are MatLab modelling results of the

algorithm.

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Valve Model Transfer Response to Step input.

As expected the response is first order Lag + Gain in nature.

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Heat Exchanger Model Transfer Response to Step input.

As expected the response is second order Lag + Gain in nature.

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Temperature sensor Model Transfer Response to Step input.

As expected the response is 1st order Lag + Gain in nature.

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Open Loop Transfer Function Bode Plot of frequency to determine Gain

Margin for Controller.

36

At phase angle of -180 Deg the

frequency is 0.389 radians/second.

At 0.389 radians /second the Gain

margin is -13.9 dB.

=>13.9dB = 20 log (Gain) =13.9/20

= log (gain)

=>0.695 = log (gain)

Gain = 100.695

=> Gain = 4.955 (max gain) (above

this system unstable)


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Closed Loop Transfer Step Response with Gain Margin in unstable region.

37

The Transfer response is unstable in this plot. This

is because the Kp (Proportional gain) was set to 6

when we had calculated the gain margin to be4.955. SO this response is as expected and proves

our gain margin calculation.

Notice the Oscillation growth. This could be

destructive to equipment if implemented in the real

world. Our Algorithm for the BEEM system wont

do this because of our Mathematical

modelling/Simulations.


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Closed Loop Transfer Step Response with Gain Margin in stable region.

38

Gain of controller (P only gain) kept below gain margin

of 4.955 so the response is stable . However theovershoot is quite large at approx 25%. So we need to

add some Integral gain to slow the rise ramp up rate.

Overshoot has a cumulative tariff on energy costs so

we want to

Dampen the response to get a fast and smooth

response.


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Closed Loop Transfer Step Response with Integral (Ki) as well as

Proportional (Kp) Gain.

39

We added a small amount of Integral Gain but it

made little difference for this system.

This is expected because the Integration of a

step is approximately the same shape as the

plotted response anyway. We now need to see if

adding Derivative Gain (Kd) will

Help to dampen the system response. It should

as this type of gain has a Predictive nature.

Notice the steady state level has increased due

to the extra gain of the controller.


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Closed Loop Transfer Response with Proportional (Kp), Integral (Ki) and

Derivative (Kd) Gain.

MatLab Code for BEEMS System Boiler Control Algorithm

40

This is a beautiful response. Notice how smooth the

rise is and notice that we have virtually eliminated

overshoot. This algorithm would give a very energyefficient control on our BEEM system boiler control.

Also the steady state error is very flat so the valve

will not be hunting unnecessarily.


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s=tf('s'); % Setting S as a transfer function

Kp=9; %PID proportional gain constant

Kd=1; %PID derivative gain constant

Ki=1; %PID integral gain constant

Kv=1;%Valve Gain constant

Kh=10;%Heat Exchanger gain constant

Kt=0.1;%Temperature transducer (feedback loop) gain

constant

Tv=1.5; % Tau constant for Valve

Th1=5; % Tau constant 1 for Heat Exchanger 1st order

Th2=3;%Tau constant 2 for Heat Exchanger 1st order

(Th1*Th2) gives a 2nd order

Tt=2;%Tau constant for temperature transducer.

T=1; %Sample period%%

A=Kp*(1+Kd*s+Ki/s);

figure(1)

step(A);

%%

V=Kv/((Tv*s)+1);

figure(2)

step(V);

%%

HE=Kh/(((Th1*s)+1)*((Th2*s)+1));%

figure(3)

step(HE);

%%

TT=Kt/(Tt*s+1);

figure(4)

step(TT);

%%

OLT=(V*HE*TT);%Multiply out Open Loop Transfer Function

(PID excluded for OLTF)figure(5)

Step(OLT);

Bode(OLT)

%%

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Kp=0.978;

CLT=(Kp*V*HE*TT);%Multiply out Open Loop Transfer Function

(PID excluded for OLTF)

figure(13)

Step(CLT,100);

%%

% Controller gain was calculated at max 4.954 before

exceeding the

% -180Degree gain margin and the loop becoming unstable

Kp=1.09;

CLT=(Kp*V*HE)/(1+(Kp*V*HE*TT));

%CLT=feedback(OLT,TT);

%B1 = OLT;

%B2 = TT;%B3 =feedback(OLT,TT);

figure(7)

step(CLT,200);

%%

OLT=(V*HE*TT);%Multiply out Open Loop Transfer Function

(PID excluded for OLTF)

figure(9)

step(OLT);

rlocus(OLT)

%%

Kp=0.978;

Kd=0.5;

CLT=((Kp*(1+Kd*s))*V*HE*TT);%Multiply out Open Loop

Transfer Function (PID excluded for OLTF)

figure(12)

Step(CLT);

%%rlocus(CLT)

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%%

Kp=1.13;

Kd=0.5;

CLT=((Kp*(1+Kd*s))*V*HE)/(1+((Kp*(1+Kd*s))*V*HE*TT));

%CLT=((Kp*(1+Kd*s))*V*HE*TT);%Multiply out Open Loop


figure(13)

Step(CLT,200);

%rlocus(CLT)

%%

Kp=1.2;

Kd=3.5;

Ki=0.1;

CLT=((Kp*(1+Kd*s+(Ki/s)))*V*HE)/(1+((Kp*(1+Kd*s+(Ki/s)))*V*HE*TT));

%CLT=((Kp*(1+Kd*s))*V*HE*TT);%Multiply out Open Loop


figure(261)

Step(CLT,200);

%rlocus(CLT)

%END OF SYSTEM MODEL

As can been seen from the modelling plots the Algorithm for the

boiler control using the BEEMS system works very well in Mathematical

simulation. Unfortunately the time and cost constraints of this project

do not allow the testing in practice of this algorithm. For this reason a

lot of time was spent getting it fully modelled and simulated in

MatLab. This demonstrates the concept principle.

It was planned that Dundalk Institute of Technology would allow access

to boiler plant to perform a deployment of the BEEMS system for

energy efficiency testing. Written requests and proposals weresubmitted to the Estates department at the college and Tim Daly and

Dr. Dan OBrien assisted greatly in negotiating access to a plant room.

Initially it did not appear that this access would be granted. A

contingency plan was formulated whereby TCD (Trinity College Dublin)

had agreed to allow the project to be tested on one of their boiler

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systems. The installation requirements were assessed during the

interview process of with industry in semester 1. The plan was to fit 2

clamp on thermocouples and clamp on time of flight ultrasonic flow

meters (1 for the hot water and 1 for the incoming oil or gas). This

would allow a full test the boiler using the BEEMS system and the

energy efficiency algorithm developed earlier (see page 26).

In order to proceed further with these works the following

documentation would need to be put in place:

Insurances

Training certification

Health and Safety statements

Risk Assessment for works

Method statement for works

The pictures on Page 44 illustrate the plant room offered by TCD.

However Dundalk Institute of Technology did grant access to their

plant and the TCD site was not required. The pictures on Page 45

illustrate the plant room at DkIT which was assessed for these works.

Tim Daly (Engineering faculty) and Christian Maas (Estates

Department) assisted in providing access to and use of the Boiler plant

room. Christian was particularly helpful in determining the best way to

approach the deployment of any equipment required.

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Photos - Trinity College Boiler rooms at the McNamara Building

Photos from DkIT Boiler house - appraisal visit

Tim Daly and Christian Maas alongside Duty and Standby Gas Boilers

Incoming gas flow meter (left) and Boiler output hot feed lines and pumps

(right)

Proposed flow meter installation point and electrical control cabinet.

Costings for the implementation of Boiler Energy Efficiency

Testing

1 x DN100 Mag5100W flow meter sensor 1,700 + VAT

1 x Mag5000 Converter for flow meter 1,200 + VAT

1 x Mag5000 remote mounting bracket and PCB 220 + VAT

1 x Special fitting fabrication 500 + VAT

Sum Fitter to fit Flow meter and remove later 800 +

VAT(estimated)

2 x PT100 clamp on temperature sensors (4-20mA) 300 + VAT

Sum Cable , cable tray, Thermal cladding 100 + VAT

Subtotal-1 4,820 + VAT

Costings for the implementation of Fu gitive gas with BEEMS

system

1 x Special cable for Gas meter 100 + VAT

(estimated)

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1 x OLCT10 CH4 gas sensor 200 + VAT

1 x CEX300 CO2 gas sensor 400 + VAT

1 x CEX300-IR O2 gas sensor 800 + VAT

1 x slam shut valve (not required will use relay to mimic)

Sum Calibration gases (N2, Clean Air, CH4, CO2) 450 + VAT


BEEMS system hardware costs (additional to either of above

costs)

1 x DT85 Data logging module 2,000 + VAT

1 x UPS of system 50 + VAT

1 x GSM modem for SMS alarms and control 150 + VAT

12 x relays for Plant pump monitoring/trip reset 240 + VAT


The cost of implementing the boiler energy efficiency testing proved to

be too expensive for the budget available. This was most unfortunate

but unavoidable. However it was decided to still use the accessgranted to the Boiler plant room to carry out a deployment of the

BEEMS system to prove the concept of fugitive gas monitoring and

control. This will be discussed in the Fugitive Gas section (See Page

58).

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TUMBLE DRYER ENERGY MANAGEMENTTHROUGH BEEMS

Since the Boiler energy efficiency was too expensive to implement it

was decided to find an alternative application to demonstrate the

concept of using BEEMS for energy management purposes. Another

project running at DkIT was an improved Tumble Dryer system that

was more energy efficient than the standard model. Noel Rooney the

student carrying out this work is a Mechanical Engineering student and

was in need of assistance in collecting data on temperature, relative

humidity , air flow and electrical energy in order to profile and compare

the modified and unmodified tumble dryers. It is not within the remit of

this report to detail the engineering differences between these 2

dryers, rather this report will detail the testing performed by BEEMS.

Below is a picture of 2 identical tumble dryers manufactured by Creda.

The machine on the left has been modified by Noel Rooney in order to

achieve energy efficiency. The machine on the right is an unmodified

dryer as built by Creda.

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Testing Strategy

In order to profile and compare the two machines each dryer was fitted

with identical sets of sensors. BEEMS was configured to record data

measurements from all measuring points at the following intervals:

1 second (mimic update -no recording), 10 seconds, 1 minute and 10minutes.

48

Diagram Copyright of Chris Pullen


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The customer requested the following items to be logged:

Parameter Sensor

Ambient air intake temperature PT100

Drum temperature PT100

Vent temperature (exhaust air) PT100

Relative humidity Handheld meter

Air velocity Handheld anemometer

Phase voltage Voltage transducer

Phase current Current transducer

Time and date Data points stamped

Clothes weight pre dry Weighing scales

Clothes weight post dry Weighing scales

Vent pipe CSA Vernier calipers

Derived Parameter Sensor

Total Air Volume Vent CSA x Air Velocity x

Time

Total Electrical Power Voltage x Current x Time

Kg CO2 reduction Kwh usage x CO2 Kg/kwh

Energy cost Kwh usage x cost/kwh

Both Machines were data logged in parallel meaning all of the above

parameters were required for EACH machine.

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Sensor and Transducer Installation

Handheld Instruments and BEEMS data logging

The picture above illustrate the handheld instruments used during the

testing. Budget restrictions prevented the use of sensors, with signaloutputs suitable for data logging, for every parameter. Where

handheld instruments were used readings were taken every minute

and entered manual into the BEEMS system where they were recorded

with the other parameters. This was achieved by setting a list control

variable values, representing the manually measured parameters, to

the measured reading. The open source code in the DT85 Data-Taker

data logger which was used to form the data acquisition basis of the

BEEMS system allowed the CV values to be altered over a USB

connection from a PC or Laptop. The software was written to record CVvalues labelled as the parameters measured with handheld

instruments.

Data Analysis - Graphical Display (Mimics) for live test data

50

The Void, Drum and Vent Temperatures

were monitored using PT100 sensors as

shown in the pictures.

The phase voltage and phase currents

were monitored using voltage and

current transducers as shown in the

pictures.

Both machines were fitted withindependent equipment simultaneously.

Each Machine needed to be owered


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The above illustration is a screenshot of the mimics produced to allow

the customer (in this case a student) to view graphically their process

in real time. The mimics are a little crude but are more than sufficient

to demonstrate the feature of real time mimics being used in

conjunction with the BEEMS system. Improvements on the graphics

quality is simply a matter of purchasing a professional quality drawingpackage. Any bmp or jpeg image is compatible with the BEEMS

system software.

Up to 256 icon images can be embedded into a mimics screen. The

numerical values representing data channels are superimposed on top

of the background or icon image. These overlays are basically channel

number tags to tell the BEEMS software which channels value is to be

printed to the screen, what font colour and size, what the name of the

connection is (e.g. USB port 2) and how frequently to update the

screen print.

Data Analysis - Trending modified-v-unmodified Vent

Temperature

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The data trending on page 54 was produced from the data that was

collected from the testing of the Tumble Dryers. The customer was

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very happy with the trending as it greatly simplified the analysis of the

testing. This trend is just a sample of many trends produced for the

customer. The trending was an important item to deliver on as it is

kernel to the concept of using BEEMS as a tool for management and

analysis.

Similarly the customer feedback on the mimic screen was excellent as

it allowed the customer to assess the energy efficiency testing in real

time and to ensure that all monitoring equipment was working without

having to wait until the test was completed.

The next step was to demonstrate how the collected data could be

used to analyse the energy efficiency of the tumble dryers. In order to

do this a trend of the electrical power was produced to compare and

contrast the energy usage of both dryers over the testing period. Again

here the customer was extremely happy with the results. As can beseen from the trend on page 56 the BEEMS system data verified that

the energy inputted into both machines was almost identical at 2.40

Kwh for the unmodified machine and 2.42 Kwh for the modified

machine.

So the energy into both machines has been quantified. The clothes

inside were carefully matched to be the same material and weight.

They had the same water content since the loads came from the same

wash and spin. The next step to verify that the customer has identified

how to save energy from the tumble drying is to compare the energiesvented in the exhaust air. In other words how much electrical energy

can be removed from the modified machine drying cycle and still

maintain the heat levels in the vented air which is present in the

unmodified machine (this difference being energy which was

previously being lost through inefficiency).

The illustration on page 57 is a screen shot of the BEEMS data being

analysed in a spread sheet format. The vented hot air energy

difference between the modified and unmodified machines is shown to

be 9.5% higher on the modified machine. This proves that the

customers modifications have verified that energy savings are possible

with the Creda tumble dryer. The customer can reduce the heating

element size or duty cycle in order to reduce the vented hot air energy

by 9.5% without reducing the drying effectiveness of the machine. It

was highlighted to the customer that the 9.5% energy saving which

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appears to be possible should actually be in the region of 12% as the

efficiency of inductive heating coils would be no better than 80%.

Therefore reducing the energy into these heaters would yield an

additional saving. The customer feedback from this portion of the

project was very positive and it also allowed for the BEEMS system to

be demonstrated to the project supervising Lecturers.

Data Analysis - Trending modified-v-unmodified Power

Consumption

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Data Analysis - Spreadsheet Power difference in Vented Air

Masses

Sample Open Source Code for Dryer Testing

The software configuration for the BEEMS system is done via

augmenting pre-existing code with open source code. The code for the

IPPC licence configuration is not being published here as it is of

significant commercial value. However the code for the tumbler dryer

energy testing that has been outlined in this report is available in theappendix at the back of this report (see page 73). The code would bulk

out the main body of the report unnecessarily. So it was felt the best

place to include the code is in the appendix. The MatLab code was

included in the reports main body because it was felt that the

significant time spent on MatLab should be highlighted.

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FUGITIVE GAS DETECTIONAND ALARMINGUSING BEEMS

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The above diagram shows the Fugitive Gas Management feature of

BEEMS as indicated by the grayed box which is the third project goal.

About Fugitive Gas

Having obtained clearance to use one of the DkIT boiler rooms for

BEEMS testing it was a disappointment when the Boiler Efficiency goal

was found to be too expensive to implement. It was decided to instead

use the boiler room for Fugitive gas emissions monitoring. Ideally it

would be desirable to measure a suite of gases and use the BEEMS

system to interpret the readings obtained to satisfy multiple

monitoring requirements.

CH4 (Methane) is a hydrocarbon gas burned as a fuel by boilers. The

CH4 gas can escape and become a health hazard so CH4 monitoring isimportant from a health perspective as well as an explosion risk

perspective. Although CH4 is colourless it does have an added odoriser

for safety. However the nature of Hydrocarbon gas is that they act to

anaesthetise the olfactory receptors in the nose even at very low

concentrations. So the smell is only an effective indication of the

presents of gas for a short initial period. CH4 is lighter than air and

detection equipment should be suitably located at a high level within a

confined space. Note: other hydrocarbon fuels commercially available

such ad propane, butane or LPG (a mix of propane and butane) are

heavier than air so detectors should be located no more than 0.3mabove floor level.

CO2 (Carbon Dioxide) is a by-product of the combustion process for all

hydrocarbon fuels. CO2 is an asphyxiant and therefore is hazardous.

CO2 is colourless and odourless. It is heavier than air and detection

equipment should be suitably located at close to the lowest floor level

in a confined space.

CO (Carbon monoxide) is another by-product of the combustion of

hydrocarbon fuels. The presents of CO can be particular problematicwith badly services burners or badly ventilated flues. CO is colourless

and odourless. It is highly toxic even at low concentrations as it bonds

in a similar fashion to the haemoglobin in blood (causing the blood to

become cherry red in colour). CO is slightly lighter than air so detectors

should be mounted no higher than 4 foot from the floor (or lower

where children are expected to be present).

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O2 (Oxygen) is the second fuel required for the combustion ofhydrocarbon fuels (or almost all fuels for that matter). O2 enrichment

is a health hazard but by far the most common hazard with O2 is

depletion. O2 levels below 17% can cause death. Even below 19% the

body can become light headed and clumsy. This is itself a danger. O2

is colourless and odourless. It is slightly heavier than air. O2 detection

equipment is often used for 3rd party detection. The idea behind this is

that a drop in the O2 concentration in air is an indication of one of the

following: combustion or oxidation are using up the O2 present in the

air, or a 3rd

party gas is present in higher than natural concentrationsand is occupying some of the volume in air normally taken up by O2.

O2 detectors are therefore located mid height in a confined space.

The monitoring of fugitive gas in the DkIT using a BEEMS system could

be employed to do any or all of the following:

Detect fugitive CH4 [Health and explosive risk]

Detect elevated CO2 levels [Health risk]

Detect elevated CO levels [Health risk]

Detect depleted levels of O2 [Health risk and possibly indication

ofother gas]

Monitoring if boiler burn becomes dirty

Monitor if ventilation to burner becomes obstructed

Monitor if ventilation of flue becomes obstructed

Monitor for TWA (Time Weighted Average) exposure levels forpersonnel

Monitor for STEL (Short Term Exposure Level) exposure levels for

personnel

Automatically activate slam shut vale on leak detection or limit

exceedance

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Automatically generate Alarms on leak detection or limit

exceedance

Report back live levels to personnel before confined space entry

Fugitive Gas Monitoring Setup

The photos above show the Fugitive gas detector fitted in the DkIT

boiler room. A CH4 detector was installed close to the ceiling. An O2

detector was fitted at mid height in the room. A CO2 detector was

fitted close to floor level and the DT85 was fitted in the Electrical

control panel .

Each detector was calibrated using bottles calibration gases applied at

a flow rate of 0.5 litres per minute (as per manufacturers

recommendations). The CH4 ,CO2 and CO detectors were Zero set

using a pure Nitrogen (N2) gas. The CH4 was spanned using a 2.5%

CH4 by Volume gas. The CO2 was spanned using a 2% CO2 by volume

gas and the O2 was spanned using Analytical Air which has 20.9% O2

by volume.

Each sensor had a 4-20mA output and the DT85 was calibrated against

these current loop signals while the zero and span gases were ON(being applied to the detectors). Therefore the whole system was

calibrated to industry standards using traceable gas.

Data Analysis Trending of Boiler House Gas Data

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The trend illustrated on the last page (62) shows the behaviour of the

CH4, CO2 and O2 gas in the boiler house during the deployment of the

BEEMS system. It is unremarkable since the boilers in this plant room

are very well serviced. Only trace amounts of CH4 is present (see blue

trend line) and a saw tooth pattern is discernable from the ON/OFF

operation of the boilers.

The CO2 trace (the red line) shows the natural background levels of

CO2 (approx 400ppm) found in Air but with the CO2 produced by the

boiler combustion superimposed. The CO2 trend tracks the CH4 trend

since the extra CO2 is produced as a function of the operation of the

Boilers and any trace CH4 is also a function of the boiler operation.

The O2 trace (black line) shows 2 interesting trends. Firstly the Diurnal

variation can be seen. The Diurnal variation is a natural variation in

Oxygen levels found in Air between Day and night due tophotosynthesis processes of plants and marine algae. The second

trend that can be seen is again the boiler operation superimposed.

Although the saw tooth feature of the CH4 trend cannot be seen there

is a definite correlation between the average CH4 levels and the

variation in the natural O2 profile.

Although the data collected by the BEEMS system does not have

include any risk events the capability of the system to accurately

and precisely track the gas mix within the confined space has been

very well demonstrated.

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Back End (User end) software interfacing to

BEEMS

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The above diagram shows the Backend Software features of BEEMS as

indicated by the grayed box which is the fourth project goal.

The Backend software features required for the BEEMS system need to

include at a minimum the following:

Flexible connectivity


Data trending

Alarm Log / Reporting

Flexible connectivity

The BEEMS system has an extremely flexible communications

platform. Users can mix and match any of the following ways:

Ethernet 10BaseT (10Mbps)

o (TCP/IP protocol)

o Web Server

o FTP Server

o FTP Client

USB (ASCII Protocol)

Serial interfaces:

o ModBus

o RS232

o RS422m

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o RS485

o CAN Bus

Local LCD display and panel buttons

Front paned USB memory stick port


The BEEMS system mimic capability was demonstrated within the

tumble dryer testing process. The capacity of the BEEMS system is 1-

900 channels per DT85 based hub. Larger systems are possible and

DT85 hubs could be made swap information using ModBus. However

there is a limitation with the User interface software if the system size

exceeds 1 hub max capacity of 900 channels. In such a case the user

would configure the software to send all the channel data to a

database and then retrieve the data required from that database. This

allows the system to be virtually limitless with regard to the number of

channels. However it would cause the system to become more

complicated as an extra data management layer is added. The

response time of the Backend software would also be adversely

effected.

The user has a choice of presenting data in a mimic screen, a

spreadsheet and a trending screen (graphing) as we have seen

already. There are also further choices of a text screen (a scratch pad

with updating text) and a web browser interface (the web server in

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built into the DT85 but must be configured to suit the BEEMS

application).

Example: Trending viewed through Internal Wed server mimic

Example: Live data viewed through internal web server mimic

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Example: Data retrieve embedded into internal web server interface

Example: Channel Text listing viewed through internal web server

Data trending

Historical data trending has been demonstrated throughout this report.

Trending was produced for the IPPC licence BEEMS configuration, for

the Energy efficiency BEEMS configuration and again for the Fugitive

gas BEEMS configuration. The feature is well tested and proven. Live

data trending is utilized as easily as historical trending. The only

difference is the user selects a channel on a live connection rather

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than one from a data file or database. Below are thumbnail views of

trends used throughout this report.

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Alarm Logging and Reporting

Alarm logging and report generation are crucial features to ensuring

the BEEMS system is commercially successful. In order to market the

system as cost effective there must be a significant and quantifiablecost benefit. The more a system such as BEEMS can automate

reporting and data management the lower the labour cost is to the end

user.

IPPC licensing is a very good case in point. The licence holder is legally

bound to adhere to strict reporting formats and time scales as

stipulated in the licence. IPPC licence holders must report an

exceedance to the EPA within 48 hours of an occurrence. They must

also generate reports based on hourly, daily, monthly, quarterly and

annual limits. The BEEMS system can be configured to Alarm onhigh , low or complex limits for any parameter.

These alarms are stored in an alarm log database and manual or

automated reports can be generated. The user can tailor these reports

to suit the EPA requirements by simply modifying the parameters and

time scale of interest. The reduction in resources and costs required to

meet reporting requirements are an attractive selling point and

demonstrate again the sustainability benefits of this product.

The EPA or licensing Authority can also save resources throughreduced audit requirements, since the data management is a more

secure format than manual data collection and manipulation. It is also

possible for BEEMS to publish reports and data directly to a static IP

address by means of TCP/IP push. This means that anyone interested

in the data or reports need no special softwares. Anyone with a web

browser and possibly a password can view the information.

With industry heading more and more down the route of implementing

Cloud computing topologies the BEEMS system is well placed to

compete in the market place.

Example: Text Report file generated from logged data

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Example: Alarm report automatically generated

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LEGISLATIONANDFUNDING

Systems such as BEEMS will be mandatory in the near future. Alreadythe European Parliament Directive 2002/91/EC imposes obligations on

member states to legislate for new buildings to meet minimum Energy

standards. Downward pressures in Europe and Ireland will lead to this

obligation being levies on all commercial and eventually domestic

buildings.

The SEAI Energy Efficiency Retrofit Fund (EERF) allows commercial

and academic facilities to apply for grant aid to carry out remedial

works to make buildings more energy efficient and sustainable. In

some cases up to 80% of the costs can be recouped. These documentsare included in the Appendix (See Page 85 Section 5).

ACKNOWLEDGEMENTS

DKIT Lecturers / Staff:

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Tim Daly,

Pdraig McGuigan,

Kimmitt Sayers,

Dr. Dan OBrien

Christian Maas

Dr. Eoin Clancy

Mark Clarke

James Mulvany

DKIT Students

Noel Rooney

Industry Contacts

Edward Keating Uisce Technology

Stephen Moran BOC Gases

Aidan Corrigan IFM

John Robinson Trinity College Dublin

Michael Bergin Green Star EnvironmentalTom Butterly Dublin City Council Heating Department

I would also like to thank my family for their patience and support

during my studies at Dundalk Institute of Technology. I also want to

thank Omni Instruments for financing the project.

APPENDIX

Sample Open Source Code for Dryer Testing'JOB=JOB1'COMPILED=2011/04/04 00:24:41'TYPE=DT85DT=\d

6*PT385("M Void",W,=6CV)56CV("M Void",=56CV)=6CV-1.227*PT385("C HE1 Pre",W,=7CV)57CV("C HE1 Pre",=57CV)=7CV-0.97

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BEGIN"JOB1"CATTN'Spans and polynomial declarationsS1=5.62,10.52,1.396,2.145"Amps"S2=0,238,7,14.2"VAC"S3=5.69,10.63,1.375,2.175"Amps"S4=0,238,0.49,14.24"VAC"'Thermistor declarations

'Switches declarations'Parameter declarations'Global declarationsRS1S'schedule definitionRA"LIVE"("B:",ALARMS:OV:100KB,DATA:OV:1MB)5SLOGOFFA GA1*PT385("U Drum",W,=1CV)51CV("U Drum DegC",=51CV)=1CV-1.182*PT385("U Vent",W,=2CV)52CV("U Vent DegC",=52CV)=2CV-1.683*PT385("U Void",W,=3CV)53CV("U Void Deg C",=53CV)=3CV-1.134*PT385("M Drum",W,=4CV)54CV("M Drum",=54CV)=4CV-1.465*PT385("M Vent",W,=5CV)55CV("M Vent",=55CV)=5CV-0.97

8*PT385("C HE2 Pre",W,=8CV)58CV("C HE2 Pre",=58CV)=8CV-1.219*PT385("C HE2 Post",W,=9CV)59CV("C HE2 Post",=59CV)=9CV-1.0610*PT385("C HE1 Top",W,=10CV)60CV("C HE1 Top",=60CV)=10CV-1.0811*PT385("C HE2 Top",W,=11CV)61CV("C HE2 Top",=61CV)=11CV-1.01

12*PT385("C Vent",W,=12CV)62CV("C Vent",=62CV)=12CV-1.2415*PT385("Room Air",W,=30CV)30CV("Room Air DegC",=31CV)=30CV-1.3513HV(S1,"U Amps ",W,=13CV)ALARMR(13CV("U Amp Zero")


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51CV("U Drum DegC")52CV("U Vent DegC")53CV("U Void Deg C")54CV("M Drum")55CV("M Vent")56CV("M Void")57CV("C HE1 Pre")58CV("C HE2 Pre")

59CV("C HE2 Post")60CV("C HE1 Top")61CV("C HE2 Top")30CV("Room Air DegC")15CV("U Power Kw")35CV("U Power Kwh")18CV("M Power Kw")36CV("M Power KwH")13CV("U Amps ave",AV)14CV("U VAC ave",AV)70CV("M Amps ave",AV)17CV("M VAC ave",AV)90CV("U RH % man")91CV("U VEL M/S man")92CV("M RH % man")93CV("M VEL M/S man")END'end of program file

DT85 Specification sheet

Full data specification downloadable at:

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http://www.datataker.com/Library/Product_Data_Sheets_TS/TS-0067-E1%20-

%20DT85.pdf

Terms

BATNEC Best Available Technology Not incurring Excessive

Cost

BEEMS Building Energy and Environmental Management

System

BMS Building Management System

BOD Biological Oxygen Demand

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http://var/www/apps/conversion/tmp/scratch_1/%20http:/www.datataker.com/Library/Product_Data_Sheets_TS/TS-0067-E1%20-%20DT85.pdfhttp://var/www/apps/conversion/tmp/scratch_1/%20http:/www.datataker.com/Library/Product_Data_Sheets_TS/TS-0067-E1%20-%20DT85.pdfhttp://var/www/apps/conversion/tmp/scratch_1/%20http:/www.datataker.com/Library/Product_Data_Sheets_TS/TS-0067-E1%20-%20DT85.pdfhttp://var/www/apps/conversion/tmp/scratch_1/%20http:/www.datataker.com/Library/Product_Data_Sheets_TS/TS-0067-E1%20-%20DT85.pdfhttp://var/www/apps/conversion/tmp/scratch_1/%20http:/www.datataker.com/Library/Product_Data_Sheets_TS/TS-0067-E1%20-%20DT85.pdfhttp://var/www/apps/conversion/tmp/scratch_1/%20http:/www.datataker.com/Library/Product_Data_Sheets_TS/TS-0067-E1%20-%20DT85.pdfhttp://var/www/apps/conversion/tmp/scratch_1/%20http:/www.datataker.com/Library/Product_Data_Sheets_TS/TS-0067-E1%20-%20DT85.pdf


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BTU British Thermal Unit

Cap-Ex Capital Expenditure

CH4 Methane

CO Chemical symbol for Carbon Monoxide gas

COD Chemical Oxygen Demand

CO2 Chemical symbol for Carbon Dioxide gas

CSA Cross Sectional Area

CSR Corporate Social Responsibility

CV Channel Variable

DkIT Dundalk Institute of Technology

EC European Community

EPA Environmental Protection Agency

Ethernet Ether (from Greek meaning Anywhere) Network

EU European Union

Flume An open artificial channel used for flow gauging

FSK Frequency Shift Keying

Fugitive Gas Pollutant released to air from equipment and

plant leaks

Glycol An alcohol of 2 Hydroxyl groups, used for heattransfer.

GSM Global System for Mobile communications

GUI Graphical User Interface

HMI Human Machine Interface

HVAC Heating, Ventilation and Air Conditioning

Hydronic Term to describe the transfer of heat by water

Internet Interconnected Network

INvironment Indoor environment, term defined for this project

I/O Input /Output, used to describe field interface

devices

IPPC Integrated Pollution Prevention Control

Klaxon A loud siren used for raising alarms.

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Kw Kilowatt , unit of electrical power

Magmeter Magnetic flow Meter

MatLab Software Package for Mathematical Modelling

mg/l milligram per litre

Modbus Modicon communications Bus

NVM Non-Volatile Memory

OLE Object Linking and Embedding

OPC OLE for Process Control

Optoisolator Optically Isolated signal interface device

OTS Off The Shelf

O2 Chemical symbol for Oxygen gas

PC Personal Computer

PID Proportional Integral Derivate

PLC Programmable Logic Controller

Potable Water fit for human consumption

ProfiBus Process Field Bus

PSTN Public Switched Telephone Network

PT100 Platinum Resistance device with resistance of 100

@ 0CQin / Qout Flow in/Flow out, Q is notation representing a liquid

flow

RTU Remote Telemetry Unit

SCADA Supervisory Control And Data Acquisition

SCOM Superimposed Communications Over Mains Chris Pullen

SDF Secure Data Format

SEAI Sustainable Energy Authority of Ireland

Sewage Liquid and solid waste carried off in sewers or drains

Sewerage A system of sewers or drains to carry away sewage.

SHEQ Safety, Health, Environmental and Quality

SMS Short Message Service

Solenoid Magnetising coil powered by electric current.

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STEL Short Term Exposure Limit

TBL Triple Bottom Line, a term indicating the social,

environmental and financial cost as opposed to solely

a

financial cost (Bottom Line)

TCD Trinity College Dublin

TWL Time Weighted Average

UPS Uninterruptable Power Supply

USB Universal Serial Bus

design building energy and environmental management and control system

Documents