project deliverable - more-nmp monitoring and optimization ... indicators for the resource...

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Project Deliverable

Project Number: Project Acronym: Project Title:

604068 MORE

Real-time Monitoring and Optimization

of Resource Efficiency in

Integrated Processing Plants

Instrument: Thematic Priority

COLLABORATIVE PROJECT NMP

Title

D1.6 Final report on real-time resource efficiency indicators for batch and continuous processes

Due Date: Actual Submission Date:

Month 40 (February 2017) March 3rd, 2017 (Month 41)

Start date of project: Duration:

November 1st , 2013 40 months

Organisation name of lead contractor for this deliverable:

Document version:

VTT V1

Dissemination level ( Project co-funded by the European Commission within the Seventh Framework Programme)

PU Public x

PP Restricted to other programme participants (including the Commission)

RE Restricted to a group defined by the consortium (including the Commission)

CO Confidential, only for members of the consortium (including the Commission)

604068 D1.6 Final report on real-time resource efficiency indicators for batch and continuous processes

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Abstract :

Indicators for the resource consumption and environmental impacts of products and production processes in process industries have been developed and are increasingly used for many purposes. Most companies monitor their energetic and material efficiency and report Key Performance Indicators (KPI), normally in retrospect over extended periods of time (e.g. per business year). KPI reflect the choice of raw materials, the plant technology used and the operational performance, without distinguishing between the different influences. As such, they cannot support decision making processes in daily plant operations. With real-time Resource Efficiency Indicators (REIs), the effects of technical improvements and of operational policies can be measured and actions can be derived for real-time or near real-time plant performance improvements.

In the first stage of the REI development process a list of indicators was defined (MORE 2014a, 2014b). This initial list was then put to the test, both practically and theoretically. The industrial partners rated the suggested indica-tors using their experience and knowledge. The filtered list thus obtained was the first result of the case studies and of the project. Next, the industrial partners implemented some indicators and put them to internal tests in order to ascertain whether they are viable or not. These lists of REIs and a thorough discussion among the part-ners resulted in the condensed final list of REIs presented in the paper.

There are only a few main generic REIs, providing the basis for a number of other REIs derived out of these. These REIs (the main and derived ones), presented in this report, are the final REIs defined in the MORE project, recommended as a basis for REIs selection and development in process industries.

These so called final REIs are shown to be applicable in industrial cases in the MORE project, and on the basis of answers to the questionnaire sent to industrial partners in MORE, representing the chemical industry. Additional-ly, the applicability of the MORE approach to resource efficiency improvement was evaluated in other industries (pulp and paper and sugar industry). The final REIs can be found in the database, with open access through a web page interface.

Authors (organisations):

Juha Hakala, Tiina Pajula (VTT Technical Research Centre of Finland Ltd) Stefan Krämer (INEOS Koeln GmbH) Marc Kalliski (TU Dortmund University) Cesar de Prada, José Luis Pitarch (University of Valladolid)

Validated by:

Sebastian Engell (TU Dortmund)

Keywords:

Resource efficiency, indicator, real-time, REI, resource efficiency indicators, process industry, key performance, material, energy, environment, pulp and paper, sugar industry, case studies

Disclaimer: THIS DOCUMENT IS PROVIDED "AS IS" WITH NO WARRANTIES WHATSOEVER, INCLUDING ANY WARRANTY OF MERCHANTABILITY, NONINFRINGEMENT, FITNESS FOR ANY PARTICULAR PURPOSE, OR ANY WARRANTY OTHERWISE ARISING OUT OF ANY PROPOSAL, SPECIFICATION OR SAMPLE. Any liability, including liability for infringement of any proprietary rights, relating to use of information in this document is disclaimed. No license, express or implied, by estoppels or otherwise, to any intellectual property rights are granted here-in. The members of the project MORE do not accept any liability for actions or omissions of MORE mem-bers or third parties and disclaims any obligation to enforce the use of this document. This document is subject to change without notice.


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Revision History The following table describes the main changes made in the document since it was created.

Revision Date Description Author (Organisation)

V0.1 05.01.2017 Creation Juha Hakala (VTT), Tiina Pajula (VTT)

V0.2 13.01.2017 Review Sebastian Engell (TUDO)

V0.3 Revision Juha Hakala (VTT), Tiina Pajula (VTT), Stefan Krämer (INEOS), Marc Kalliski (TUDO), Cesar de Prada (UVA), José Luis Pitarch (UVA),

V1 20.02.2017 Review Sebastian Engell (TUDO)


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Table of Contents Table of Contents 4

List of Figures 5

List of Tables 5

Glossary 6

1. Introduction 7

2. Methodology 8

2.1.Resource efficiency ................................................................................................................. 8

2.2.Resource efficiency indicators (REIs) as (key) performance indicators .................................. 8

2.3.MORE real-time resource efficiency indicators (REIs) ............................................................ 9

2.4.MORE principles for defining REIs ......................................................................................... 10

2.5.Generic and specific indicators ............................................................................................. 11

3. Evaluation framework for final REIs 13

3.1.Summary of applicability of the indicators ........................................................................... 13

4. Case Studies and applied REIs 15

4.1.Petronor – Efficiency in the hydrogen network of an oil refinery ........................................ 15

4.2.INEOS – Resource efficiency of an integrated complex ........................................................ 17

4.3.BASF – Process monitoring and control of an integrated plant based on renewable feedstock ..................................................................................................................................... 20

4.4.Lenzing -Evaporator optimisation of a cellulose plant .......................................................... 21

4.5.Pulp & Paper .......................................................................................................................... 22

4.6.Sugar industry ....................................................................................................................... 24

5. Final MORE real-time resource efficiency indicators (REIs) 29

5.1.Web interface for the REI database ...................................................................................... 29

5.2.Final list of REIs ...................................................................................................................... 32

6. Summary 39

7. References 41


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List of Figures

Figure 1: Categories of resource efficiency indicators ...................................................................................... 9

Figure 2: Plant hierarchy with indicators, only generic indicators can be aggregated ................................... 12

Figure 3: Schematic of the developed system at Petronor ............................................................................. 16

Figure 4: Architecture of the DMC application ................................................................................................ 16

Figure 5: Dashboard prototype for AN plant O17; baselines of the plant units fluctuate with the plant load ......................................................................................................................................................................... 19

Figure 6: Selectivity, EnPI and REI for the EO plant ......................................................................................... 19

Figure 7: Dashboard of the REI case study of BASF ......................................................................................... 20

Figure 8: Specific steam consumption of a single evaporator for the selected time period .......................... 21

Figure 9: Specific steam consumption of a single evaporator for different control values (operating points) ......................................................................................................................................................................... 22

Figure 10: Normalised average costs per time for evaporator 38 with the calculated optimal cleaning cycle time and the historical cleaning cycle time ..................................................................................................... 22

Figure 11: Pulp and paper industry – Multi-product mill (IPPC, 2015) ........................................................... 23

Figure 12: Schematic of a sugar factory ....................................................................................................... 25

Figure 13: REIs can be searched in the Analysis page by entering data of your process and of your interests ......................................................................................................................................................................... 30

Figure 14: By clicking Submit in the Analysis page, the Evaluation page shows the search results. A detailed overview is shown in the Result page by clicking the link. The parent REI can be accessed in the Result page, if the REI in question is derived from the parent REI ...................................................................................... 31

List of Tables

Table 1: Examples of different indicators, including new resource efficiency indicators (REIs). EnPI, as an example, is one of the key performance indicators (KPIs) ................................................................................ 9

Table 2: Summary of applicability of REIs -Statistics from the answers to the questionnaire sent to industrial partners in MORE ............................................................................................................................................ 14

Table 3: REIs selected by INEOS for monitoring purposes .............................................................................. 18


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Glossary

ACP Advanced Control Performance

AN Acrylonitrile

DCS Distributed Control System

DSS Decision Support System

DR Data Reconciliation

EFA Energy Flow Analysis

EnPI Energy Performance Indicator

EO Ethylene oxide

GDP Gross Domestic Production

GWP Global Warming Potential

ISO International Organisation for Standardization

KPI Key Performance Indicator

MFA Mass Flow Analysis

MORE Abbreviation of the project: Real-time Monitoring and Optimization of Resource Efficiency in Integrated Processing Plants

LCA Life Cycle Assessment

PI Performance Indicator

RACER Evaluation tool for Relevant, Accepted, Credible, Easy and Robust

SEC Specific Energy Consumption

REI Resource Efficiency Indicator


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1. Introduction

Indicators for the resource efficiency and environmental impacts of products and production processes in the process industries have been developed and are increasingly used for many purposes, for example for the communication of company performance to stakeholders, evaluation of alternative supply options or decisions on investments regarding new production units or facilities. However, these indicators do not necessarily provide information about the day-to-day efficiency of the operations. There is a lack of robust resource efficiency indicators (REIs) for real time plant or company operations that could be used for steer-ing daily operations of process plants more efficiently.

The EU-funded R&D project “Real-time Monitoring and Optimization of Resource Efficiency in Integrated Processing Plants” (MORE) defined principles for the definition of real-time Resource Efficiency Indicators (REIs) and proposed a number of indicators for integrated chemical plants that can be efficiently used to steer daily operations. The defined indicators are computed on the basis of the processing of real-time data available from monitoring and control systems, including innovative analytical measurements. As the MORE project is focused on large integrated chemical and petrochemical plants with many interconnected units, the real-time REIs and decision support tools have been developed specifically for this domain. The REIs and decision support tools were implemented in four industrial use cases from different sectors of the chemical industry, from oil refining to the batch production of fine chemicals. The full range of options for the use of REIs was realized, from monitoring to improving resource efficiency by model-based, real-time optimization, and has yielded encouraging results. In addition to addressing the chemical industry, the am-bition of the MORE project is to transfer real-time resource efficiency indicators to different sectors of the process industry. This transfer has been investigated in two different case studies, for the sugar industry and for the pulp and paper industry, and the indicators were demonstrated to be sufficiently general and flexible.

The availability of real-time resource efficiency indicators allows optimised online monitoring of plants attempting to reduce the consumption of raw materials and energy and to decrease waste streams and degradation of material.


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2. Methodology

Different key performance indicators (KPI) exist for different purposes. Here, in order to differentiate from the other indicators, the term real-time Resource Efficiency Indicator (REIs) is used. Resources in this case refer to primarily materials and energy and their combination. In addition, the MORE REIs introduced also cover environmental impacts.

Definition of resource efficiency is the backbone of resource efficiency indicators (REIs). These REIs are de-signed to measure the efficiency of the utilization of material and energy at industrial production sites in an objective fashion such that the effect of technical improvements and of operational policies on the re-source efficiency can be measured and actions can be derived for real-time plant performance improve-ments. Real-time REIs are significantly different from REIs or KPIs obtained from historic analysis because they allow online monitoring and rapid intervention in order to improve resource efficiency. The MORE project has defined eight principles to be taken into account when starting to identify REIs. There are two types of REIs, generic and specific, which are important to differentiate. Generic ones need to be defined on a scale on which the net effect on the resource efficiency can be measured on a plant level. Specific REIs may also be necessary, and can mainly be used for the equipment performance rather than for overall plant performance.

2.1. Resource efficiency There is no standardised definition for the term “resource efficiency”. The European Commission defines resource efficiency in the following way (European Commission 2016):

“Resource efficiency means using the Earth's limited resources in a sustainable manner while minimising impacts on the environment. It allows us to create more with less and to deliver greater value with less in-put.”

In the MORE project, this political definition is refined into a technical definition:

“’Resource Efficiency’ is a multidimensional entity that includes the environmental load and the efficiency of the utilization of material and energy in the production of the desired products. Other resources such as e.g. manpower, production capacity, land use, and capital are not included” (Kalliski & Engell 2016).

2.2. Resource efficiency indicators (REIs) as (key) performance indicators A (key) performance indicator (KPI) is a type of performance measurement that evaluates the success of an activity. Often KPI relate inputs and outputs and are either intensities, when stated as inputs per unit of product output (Fitz-Gibbon 1990), or efficiencies which are the reciprocals of intensities (Parmenter 2010):

“KPIs represent a set of measures focusing on those aspects of organizational performance that are the most critical for the current and future success of the organization.”

For different purposes a number of KPIs exist, often using different names for very similar measures. The KPIs used in energy management systems (ISO 50001) are called Energy Performance Indicators (EnPI). Here, in order to differentiate the indicators from others, the term Resource Efficiency Indicator (REI) is used. Resources in this case are primarily materials and energy and their combination. Additionally, the MORE indicators, REIs, also cover environmental impacts such as waste and emissions. They can be defined as efficiencies or as intensities.


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Table 1: Examples of different indicators, including new resource efficiency indicators (REIs). EnPI, as an example, is one of the key performance indicators (KPIs)

KPI Key Performance Indicator

EnPI Energy Performance Indicator (ISO 50001)

PI Performance Indicator

REI Resource Efficiency Indicator

This section also appears in NAMUR (2017).

2.3. MORE real-time resource efficiency indicators (REIs) REIs introduced in this report can be used for real-time monitoring and optimization of resource efficiency in processing plants as well as for reporting, and they are extendable to Life Cycle Assessment (LCA). De-pending on the intended use of the indicators, the interpretation of the term “real-time” varies. Loosely speaking, real-time means often and timely enough for the actions that are based on the indicators. Due to the presence of disturbances and fluctuations in all production processes, resource efficiency indicators must be averaged over realistically chosen intervals in order to avoid their values being dominated by sto-chastic influences. In order to properly reflect the effects of the operational policies, the averaging should generally not be longer than the periods over which the manipulated variables are kept constant.

Real-time REIs are significantly different from REIs or KPIs obtained from historic analysis, because they allow online monitoring and rapid intervention to improve resource efficiency. Providing REIs in real-time poses a number of challenges for measurement and for data collection and visualisation, such as missing information, missing data or incorrect measurements.

In the MORE project a measurement, analysis, an REI or an optimization technology is considered “real-time” if

1. The time delay and the sampling time of the entire analysis procedure – measurements and data processing – are sufficiently short compared to the relevant process dynamics (Minnich et al. 2016).

2. The time resolution is similar to the typical frequency of changes in manipulated variables (Kalliski and Engell 2016).

Resource efficiency indicators must be averaged over realistically chosen intervals in order to avoid domi-nation of their values by stochastic influences. In order to include the effects of the operational policies, the averaging must not be longer than the periods over which the major manipulated variables are kept con-stant.

Resource efficiency indicators (REIs) are classified into three categories (Figure 1):

1. Energy: This is based on an energy flow analysis (EFA). Indicators from this group measure how much energy is consumed for the production of one unit of product.

2. Material: This is based on a material flow analysis (MFA). Indicators from this group measure the amounts of raw materials consumed for the production of one unit of product.

3. Environmental: Here, the REI measures the environmental impact of the production process, e.g. by measuring greenhouse gas emission equivalents per ton of product.

Figure 1: Categories of resource efficiency indicators

REIs - Resource efficiency indicators

Material Environmental Energy


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For some indicators the classes may overlap. The categories “Energy” and “Material” are based on energy and material flow analyses, whereas the category “Environmental” measures environmental loads, such as greenhouse gas equivalents or water usage.

Resource efficiency is a multi-dimensional entity (because multiple resources are usually needed to produce a product or several products simultaneously), whereas economic efficiency can be measured by one single figure and in one single unit, money. The consumption of different resources and the environmental impact can be integrated into one figure by weighting the streams in comparable units. If these units are financial (prices or costs), the single figure comprising the weighted separate resources will fluctuate with price or cost fluctuation, losing its physical meaning. If the weights are chosen on physical grounds, for example the energy that is required to produce a certain carrier of energy, such an integration can help describe re-source efficiency using a single figure. Wherever possible, physical units should be preferred to make re-source efficiency transparent and to reduce the influence of external factors.

In most cases, a more resource-efficient operation is also economically advantageous, but it is possible that the two objectives conflict because of the cost of measures for the improvement of the resource efficiency or external financial incentives. From the resource efficiency perspective, for example, the minimization of all waste streams is desirable, but this can be associated with high costs, resulting in sub-optimal produc-tion from the economic point of view. In such possibly conflicting cases, REIs and economic performance indicators should be considered and reported separately, and analysed e.g. in the form of a Pareto curve or Pareto surface.

The REI approach is different for continuous and batch processes, the main difference being the non-stationary character of the indicators in the batch case.

2.4. MORE principles for defining REIs The MORE resource efficiency indicators (REIs) are based on eight principles. It is important to take these principles into account when starting to identify REIs to ensure real-time capability, and to note that the indicators reflect the technical performance as a result of the plant operation. Below, a short summation of the principles is given. They have been published in a detailed level for example in Kalliski et al. (2016) and in MORE (2014a).

Gate-to-gate approach As the entity of interest is a production site, a plant or a process unit, the boundary of the analysis is the limit of the respective entity, as only this can be influenced in real-time.

Indicating technical performance independently of market fluctuations The flows of material and energy are not to be related to real-time economic indicators; technical performance is separated from the economic performance.

Based on material and energy flow analysis The resource efficiency indicators are based on the physical flows and conversion of raw materials and energy to products and flows into the environment as objective characteristics of a production process.

Resource and output specific potential for meaningful aggregation Within the system boundaries, the indicators need to be directionally correct, i.e. improvements of the indicators should demonstrate better process performance. All net flows of raw materials, en-ergy, and products that cross the boundaries of the system under consideration must be deter-mined without aggregation. Based on a material and energy flow analysis, process specific REIs should be defined with respect to the resources and the products. The indicators can be defined either as intensities or as efficien-cies depending on the user preference. The definition of resource intensity is shown below. This version of the indicator simplifies the aggregation over different contributions due to having the same basis (product output). The corresponding indicator defined as efficiency is obtained by in-verting the intensity indicator.


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𝑅𝐸𝐼𝑅𝑃𝑆 = 𝑅𝑒𝑠𝑜𝑢𝑟𝑐𝑒 𝐼𝑛𝑝𝑢𝑡

𝑃𝑟𝑜𝑑𝑢𝑐𝑡 𝑂𝑢𝑡𝑝𝑢𝑡

Such a resource and product specific (RPS) REI by itself does not indicate whether the process is operated well. It must be compared with a reference value obtained from historical or model data in order to evaluate the plant resource efficiency change:

𝑅𝐸𝐼𝑛𝑜𝑟𝑚 = 𝑅𝐸𝐼𝑅𝑃𝑆

𝑅𝐸𝐼𝑅𝑃𝑆,𝑏𝑒𝑠𝑡 𝑐𝑎𝑠𝑒

Considering storage effects To realise “real-time” REI calculations, the choice of the temporal aggregation interval is crucial. The interval should be short enough to allow the derivation of operational decisions. Ideally a hold-up change is considered in the consumption or production figures. Long-term effects such as cata-lyst degradation or fouling must be defined in a suitable manner.

Include environmental impact The impact on the environment must be taken into account separately in order to measure the ecological performance. Emission of pollutants to air, water and soil can be used as separate indica-tors.

Hierarchy of indicators – from the whole production site to a single apparatus Production processes are interconnected. Analysing an individual apparatus may be misleading be-cause resource utilization can be shifted to other units by different local operational policies. Ge-neric resource efficiency indicators must be defined on a scale on which the net effect on the re-source efficiency can be measured through a bottom-up aggregation.

Extensible to life-cycle analysis For reporting and assessment purposes, an extension to a Life Cycle Assessment should be possible using the aggregation scheme and adding a relevant weighting value to feed streams.

These principles have been published in a more detailed level for example in Kalliski et al. (2016) and in MORE (2014a).

2.5. Generic and specific indicators Resource efficiency indicators can be divided into generic and specific indicators. Generic indicators can be applied to every plant and can be aggregated bottom up, whereas specific indicators measure unit specific effects and provide more detailed information on key production steps, such as reaction and purification steps that strongly influence the efficiency of the plant. Such specific indicators can be e.g. the energy re-quired for the purification of a mass unit of product or the selectivity of a reactor.

Generic resource efficiency indicators are indicators which are applicable to each unit of the evaluated pro-duction complex and are suitable for aggregation. The generic nature of these general indicators enables consistent reporting for each production unit on the lowest hierarchical layer as well as a homogeneous aggregation to measure the resource efficiency with respect to final products, or the performance of large units or complete production sites. A typical example of a generic indicator is the energy performance indi-cator (EnPI) according to ISO 50001, which measures how much energy is used per ton of product. Generic indicators are crucial to evaluating the process performance of an aggregated complex of different types of production units, but are also suitable for monitoring and comparing individual production units. Generic indicators can be used for the comparison of different units in a consistent evaluation framework.

In many cases, equipment specific REIs, and REIs which can be used to identify the performance of key pro-duction steps, are required. Specific indicators cannot always be aggregated. Care must be taken to avoid false signals leading to attempts to improve the specific indicators at the expense of poorer overall perfor-mance. Specific indicators provide additional information to identify the causes of less efficient production. Figure 2 presents the plant hierarchy with generic and specific indicators.


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Figure 2: Plant hierarchy with indicators, only generic indicators can be aggregated


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3. Evaluation framework for final REIs

First, an initial list of indicators was defined in the MORE project (2014 a, 2014b).This list was put to the test, both practically and theoretically:

1. First, the industrial partners rated the suggested indicators using their experience and knowledge. The resulting filtered list was the first result of the case studies and of the project

2. Second, the industrial partners implemented some indicators and subjected them to internal tests in order to determine whether they were relevant or not.

In the development stage, the indicators were defined in a large spreadsheet containing different levels of information and REI classifications. This spreadsheet evolved periodically.

These two lists of indicators, and a thorough discussion among the partners at a workshop (as part of the framework for evaluation and revision of the indicators), resulted in the condensed list presented in the paper.

Examples of the indicators which were not included in the final list of REIs are JRC indicators (MORE 2014a) for

Eco-efficiency; measuring Gross Domestic Production (GDP) divided by the sum of Global Warming Potential (GWP) components, such as CO2, NO2, CH4, CFCs, HCFCs and CH3Br

Economic; measuring GDP based on e.g. crude oil consumption or CO2 emissions, or on the basis of a certain product or functional unit

Environmental; for example measuring GWP per fossil fuel usage, water usage or per functional unit

These are well suited for measuring upper level actions or policies, but their scope is too wide for opera-tional level in real time. Other examples of removed indicators are

Advanced Control Performance (ACP); for measuring Actual Obtained Profit (AOP) and the Lost Op-portunity Profit (LOP) due to not having an ACP in service

Energy performance analysis; This is a Solomon benchmarking index for actual per referenced con-sumed energy and for CO2 emissions per refinery activity

These indicators are not within the MORE scope. ACP deals with monetary terms, whereas the MORE ap-proach is based on technical performance, and energy performance analysis deals with benchmarking, and not the individual plant or process, although these indicators were used by some of the industrial partners. Some duplicated indicators were also removed from the final REIs list

There are only a few main generic REIs, providing the basis for a number of other REIs derived from these main REIs. The REI list presented in Section 5.2. of this report is the final list of REIs defined in the MORE project. REIs are located in the database with open access through the web page interface, which is pre-sented in Section 5.1.

3.1. Summary of applicability of the indicators A questionnaire was sent to industrial partners (BASF, INEOS, Lenzing and Petronor Oil) in MORE in order to obtain their feedback regarding the usability, sensibility, applicability and needs for revision of the REIs as a general view of their plant experts. The statistics presenting the overview of the replies are presented in Table 2. Based on the feedback, a few indicators were combined or removed and thus the total number of 57 final REIs presented in Section 5 differs from the 62 presented in Table 2.

None of the REIs were regarded as inapplicable (i.e. for each REI at least one partner considered it to be sensible). Three of the REIs could be used (or are already in use) by all the industrial partners. These REIs were Energy required, Relative Energy Efficiency and Utilities/Raw Materials required. In general it can be concluded that the acceptance and applicability of the proposed REIs are at a high level in all four plants, and their potential and level of acceptance are sufficient to take new REIs into use.


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Table 2: Summary of applicability of REIs -Statistics from the answers to the questionnaire sent to industrial part-ners in MORE

The other sectors, the pulp & paper and sugar industry, did not participate in this specific survey directly.

In the MORE industrial case studies (BASF, INEOS, Lenzing and Petronor), real-time REIs that can be effi-ciently used in daily operations were defined. These REIs, applied in the case studies, were taken into ac-count in defining the final list of MORE indicators. More information concerning the industrial cases and the REIs applied can be found in Section 4.

The VTT team evaluated the applicability of the MORE approach to resource efficiency improvement in the pulp & paper industry. In principle for this sector, the indicators for continuous processes were found to be relevant. More information can be found in Section 4.5.

The UVA team evaluated the applicability of the MORE approach to resource efficiency improvement in the sugar industry. More details can be found later in the text in Section 4.6.

Measure N:o Specific *) Explanation

Number of suggested REIs: 62 Total number of REIs in the questionnaire

Total hits -Partners are using: 66 106 % Total sum of the hits of REIs four partners are currently using

Total hits -Partners are, or will

possibly be using:

95 153 % As above, added with the sum of hits of REIs which could be

used by partners in the future

All of the partners are, or will

possibly be using:

3 5 % Number of REIs all four partners are using, or will possibly use

in the future

None of the partners are, or will

possibly be using:

8 13 % Number of REIs which none of four partners are using, or will

possibly use in the future

Find the REI relevant, total hits: 231 373 % Total sum of the "yes" answers of REIs four partners consider

relevant **)

All partners are finding relevant: 49 79 % Number of REIs all four partners found relevant (Answerring

"yes") **)

None of the partners are finding

relevant:

0 0 % Number of REIs which none of four partners found relevant

(for each REI there was at lest one "yes" answer) **)

*) Divided by number of proposed REIs **) Partner could select yes or no, or left the answer as

blanc. Zero "no" answers were given, but some blanc ones.


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4. Case Studies and applied REIs

Within the MORE project the indicators, methods and tools were applied and tested in four industrial case studies at project partners’ plants and in two additional external case studies in the pulp and paper industry and the sugar industry in order to generalise the results.

All the selected case studies reflect the following project objectives:

Define resource efficiency indicators (REIs) for each of the four industry cases

Test, fine-tune and validate the defined REIs

Develop an online decision support tool and start testing its implementation

Investigate commercial, pre-commercial and scientific exploitation paths as part of the exploi-tation strategy and thus launch first investigations with regard to sustainability

In MORE (2015a), it was established that the case studies are a good representation of the chemical and process industry and of the value chain as a whole. The case study results are described in detail in MORE (2017a). The short summaries provided in this document serve the purpose of illustrating the selection and use of the REIs in the different case studies and of explaining the selection of the final REIs which constitute the main project result.

The purpose of the work in other sectors was to identify from the MORE Resource Efficiency Indicators (REIs), selected and defined by the project, those indicators that are applicable to other process industries. This was achieved by evaluating two sectors: the pulp and paper and the sugar industry. One case study was carried out for the pulp and paper industry, and three case studies were reviewed for the sugar indus-try. An important part of the work was the series of workshops for industrial experts within the sector. A workshop for the Industrial Stakeholder Panel (ISP) was also organized in order to collect feedback from experts in different process industries (MORE 2015b).

4.1. Petronor – Efficiency in the hydrogen network of an oil refinery Petrol refineries use fossil feedstock, so they are located at the beginning of the value chain. In general they can provide large sets of standard measurements, but their treatment to systematically extract really relia-ble and meaningful information is complex, and as such requires data reconciliation. This complexity to-gether with the high variability in the feedstock leaves still room for improvement.

The partner Petronor analysed and optimised the efficiency of utilization of hydrogen as a raw material in an oil refinery. H2 is mainly used as a reactant for desulfurization, denitrification and dearomatization of naphtha and diesel, in the presence of other H2-consuming side reactions. Feedstock usually changes every two to three days, and also certain product specifications can vary according to the global management of the refinery; as a consequence, scenarios regarding H2 consumption in individual consumer plants can ex-perience frequent significant changes, and therefore it is of interest to monitor REIs intended for real-time decision-making purposes.

The generic meaningful REI is efficiency in the use of H2 as a resource.

The current system implemented in the refinery covers the following topics:

REIs: A set of REIs measuring the efficiency of the use of hydrogen in the refinery

DATA RECONCILIATION: Provides reliable estimates of measured and unmeasured variables and process parameters

OPTIMAL HIDROGEN NETWORK OPERATION: Provides optimal values for the hydrogen generation, redistribution, losses and operating purities for given plant hydrocarbon loads, available through the PI information system

REAL-TIME OPTIMAL OPERATION: A DMC controller implements optimization policies according to the refinery planning acting on the network and hydrocarbon loads to a subset of consumer and producer plants

The architecture of the system is depicted in Figure 3, showing its main elements.


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Figure 3: Schematic of the developed system at Petronor

After analysing the optimal operating policies, a DMC controller (AspenTech) was designed and commis-sioned in parallel for real-time implementation as shown in Figure 4.

Figure 4: Architecture of the DMC application

In this concept, Petronor uses the generic REI “Raw Material Required” in the forms of

𝑅𝐸𝐼𝑀𝑅1 =𝐹𝑟𝑒𝑠ℎ ℎ𝑦𝑑𝑟𝑜𝑔𝑒𝑛 𝑝𝑟𝑜𝑑𝑢𝑐𝑒𝑑

𝐻𝑦𝑑𝑟𝑜𝑐𝑎𝑟𝑏𝑜𝑛 𝑝𝑟𝑜𝑐𝑒𝑠𝑠𝑒𝑑

𝑅𝐸𝐼𝑀𝑅2 =𝐻𝑦𝑑𝑟𝑜𝑔𝑒𝑛 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑑 𝑖𝑛 𝑡ℎ𝑒 𝑟𝑒𝑎𝑐𝑡𝑜𝑟𝑠

𝐻𝑦𝑑𝑟𝑜𝑔𝑒𝑛 𝑝𝑟𝑜𝑑𝑢𝑐𝑒𝑑

As a second REI “Specific Product Loss” is used, as the minimisation of fuel gas is a target of the DMC con-troller:

𝑅𝐸𝐼𝑆𝑃𝐿 =𝐻𝑦𝑑𝑟𝑜𝑔𝑒𝑛 𝑙𝑜𝑠𝑠𝑒𝑠 𝑡𝑜 𝑓𝑢𝑒𝑙 𝑔𝑎𝑠

𝐻𝑦𝑑𝑟𝑜𝑔𝑒𝑛 𝑝𝑟𝑜𝑑𝑢𝑐𝑒𝑑


17

Petronor also apply the derived REI for “Relative Utility/Raw Material Efficiency” in the forms of

𝑅𝐸𝐼𝑅𝑀𝐸1 =𝑂𝑝𝑡𝑖𝑚𝑎𝑙 ℎ𝑦𝑑𝑟𝑜𝑔𝑒𝑛 𝑝𝑟𝑜𝑑𝑢𝑐𝑒𝑑

𝐴𝑐𝑡𝑢𝑎𝑙 ℎ𝑦𝑑𝑟𝑜𝑔𝑒𝑛 𝑝𝑟𝑜𝑑𝑢𝑐𝑒𝑑

𝑅𝐸𝐼𝑅𝑀𝐸2 =𝐴𝑐𝑡𝑢𝑎𝑙 ℎ𝑦𝑑𝑟𝑜𝑐𝑎𝑟𝑏𝑜𝑛 𝑝𝑟𝑜𝑐𝑒𝑠𝑠𝑒𝑑

𝑂𝑝𝑡𝑖𝑚𝑎𝑙 ℎ𝑦𝑑𝑟𝑜𝑐𝑎𝑟𝑏𝑜𝑛 𝑝𝑟𝑜𝑐𝑒𝑠𝑠𝑒𝑑

4.2. INEOS – Resource efficiency of an integrated complex INEOS in Köln operates a typical integrated petrochemical complex. The Köln petrochemical site is one step further down the value chain. It requires fossil feedstock and many standard measurements are available. Naphtha is challenging to analyse online. INEOS in Köln developed an integrated aggregation and reporting framework for the whole site and for a number of production plants, which has indicated improvement potential.

INEOS is processing mainly naphtha and natural gas as a major feedstock to produce a large number of important base chemicals. The site, located in Cologne, is of high complexity and is characterized by a large number of products and a deep integration of the different plants with respect to products and energy. Large amounts of energy are required for feed conversion and product purification. Only a part of this en-ergy is supplied directly as primary energy (natural gas) or as direct secondary energy (electrical power). A significant portion is generated on site from the by-products of the chemical processes, mainly used as fuels1.

For the purpose of their case study INEOS decided in their internal meetings to monitor the REIs listed in Table 3.

1 In statistical analyses, raw materials for chemicals production such as naphtha are sometimes classed as secondary energy. In MORE we differentiated between raw materials turned into products and energy.


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Table 3: REIs selected by INEOS for monitoring purposes

Indicator Name

Catch Phrase Formula Measurements needed

Energy re-quired (ER)

Specific Energy Consumption 𝑅𝐸𝐼𝐸𝑅,𝐴,𝑘 =

∑ 𝐸𝑖,𝑘𝑛𝐸𝑖=1

∑ 𝑚𝑃,𝑗,𝑘𝑛𝑃𝑗=1

All energy inputs and outputs

and all product streams

Raw Material required

Specific Raw Material Consumption 𝑅𝐸𝐼𝑅,𝑖,𝑘 =

𝑅𝑖,𝑘


The relevant raw material

inputs and all product streams

Utilities re-quired

Utilities/Raw Material required per unit of product (air, water, DI-water)

𝑅𝐸𝐼𝑈,𝑖,𝑘 =𝑈𝑖,𝑘


The relevant utility and all

product streams

Material Yield Overall process yield based on mass flow 𝑅𝐸𝐼𝑌,𝑘 =


∑ 𝑅𝑗,𝑘𝑛𝑃𝑗=1

All raw material inputs and all

product streams

Overall re-source yield

Overall process yield based on weighted flows 𝑅𝐸𝐼𝑅𝑌,𝑘 =

∑ 𝐶𝑃,𝑗𝑚𝑃,𝑗,𝑘 +∑ 𝐶𝐸,𝑖𝐸𝑖,𝑜𝑢𝑡,𝑘𝑛𝐸𝑖=1

𝑛𝑃𝑗=1

∑ 𝐶𝑅,𝑗𝑅𝑗,𝑘 +∑ 𝐶𝐸,𝑖𝐸𝑖,𝑖𝑛,𝑘𝑛𝐸𝑖=1

𝑛𝑅𝑗=1

All energy inputs and outputs, all raw material inputs and all

product streams

Overall Effi-ciency based on Energy Curren-

cy

Energy streams of different nature are weighted by an energy currency which ac-

counts for the different value or exergy of the energy streams, e.g. electrical power has a

higher value compared to steam

𝑅𝐸𝐼𝑂𝑅𝐸,𝑘

=∑ 𝐶𝑈,𝑗𝑈𝑗,𝑖𝑛,𝑘𝑛𝑈𝑗=1 − ∑ 𝐶𝑈,𝑗𝑈𝑗,𝑜𝑢𝑡,𝑘

𝑛𝑈𝑗=1


+∑ 𝐶𝑅,𝑗𝑅𝑗,𝑘𝑛𝑅𝑗=1 − ∑ 𝐶𝑃,𝑗𝑚𝑃,𝑗,𝑘

𝑛𝑝𝑗∈𝑁𝑝,𝑜𝑢𝑡


+∑ 𝐶𝐸,𝑖𝐸𝑖,𝑖𝑛,𝑘𝑛𝐸𝑖=1 − ∑ 𝐶𝐸,𝑖𝐸𝑖,𝑜𝑢𝑡,𝑘

𝑛𝐸𝑖=1


All energy inputs and outputs, all raw material inputs and all

product streams, all utility streams

Waste Mass of waste type per unit of product 𝑅𝐸𝐼𝑊,𝑖,𝑘 =

𝑊𝑖,𝑘


The relevant waste stream

and all product streams

Overall weighted waste

Sum of waste weighted with “waste “curren-cy” per unit of product 𝑅𝐸𝐼𝑂𝑊𝐸,𝑘 =

∑ 𝑊𝑖,𝑘𝐶𝑊,𝑖,𝑘𝑛𝑊𝑖=1


All waste streams and all

product streams

For the formulae in Table 3, the following symbols are used:

𝑅𝐸𝐼𝑋,𝐴,𝑘 as Indicator X from plant k or an RMU

𝐸𝑖,𝑘 as energy inflows or outflows of type i at the gate of Plant k or an RMU

𝑚𝑃,𝑗 as mass of produced product of type j at the gate of Plant k or an RMU

𝑈𝑖,𝑘 as utility inflows or outflows of type i at the gate of Plant k or an RMU

𝑅𝑖,𝑘 as raw material import of type i at the gate of Plant k or an RMU

𝐶𝑙,𝑖 as relevant weight of stream type l (material or energy) (“Energy Currency”) of Type i

𝑊𝑖,𝑘 as waste import or export of type i at the gate of Plant k or an RMU

𝐶𝑊,𝑖 as relevant weight of waste stream (“Waste Currency”) of type i

The above REIs were discussed with plant personnel and agreed to be very useful for real-time process operations. Additionally, INEOS wants to ascertain how much raw material is converted into energy, and minimization of this stream is economically sound. Two examples are shown that support the choice of REIs.

For Acrylonitrile (AN), different energy, raw material or utility-related resource efficiency indicators are displayed in parallel (Figure 5).


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Figure 5: Dashboard prototype for AN plant O17; baselines of the plant units fluctuate with the plant load

An indicator combining energy- and resource-related figures in one indicator was successfully tested for the EO plant. Since the side reaction is highly exothermic, diminishing catalyst selectivity results in a lower ex-ternal energy demand. The resulting decrease in production is more than compensated by the additional energy conversion. As a result, the performance of the process from the energy point of view, when using the standard EnPI, appears to be improving as the catalysts age. The total energy demand of the process diminishes. Instead of evaluating only the external energy demand, the heat of reaction is considered ex-plicitly.

In Figure 6, the new indicator is visualized as well as the standard indicators. Although the standard indica-tor is improving, the new indicator deteriorates with increasing catalyst age.

Figure 6: Selectivity, EnPI and REI for the EO plant


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4.3. BASF – Process monitoring and control of an integrated plant based on re-newable feedstock

The BASF Personal Care and Nutrition GmbH production site in Düsseldorf, Germany, comprises various plants, most of which are operated in batch mode. The plant chosen as a case study within MORE is charac-terized by an upstream section operating in batch mode and a number of continuous processing sections downstream that produce specialty products. The value chain is based on natural and renewable raw mate-rials.

During the MORE project different campaigns with rental analytical systems were realized, testing new online measurements of critical components and conditions. Raman spectroscopy was tested in the reac-tion and purification step, and UV-Vis Spectroscopy was tested in the post-processing step.

REIs were defined and implemented for the BASF plant. Here, a new dashboard was developed and imple-mented indicating mass flow rates, REIs as well as quality parameters of the purification and post pro-cessing step.

The REI visualization was focused on the most promising steps of the BASF case, the distillation and post-processing part (Figure 7).

Figure 7: Dashboard of the REI case study of BASF

The REIs used in the dashboard are

“Material yield” in the derived forms of o Product yield (“Material Yield”) o Material efficiency (“Raw Material Efficiency”)

“Energy required” (steam per mass of product)

“Utilities required” (utilities per mass of product)

The last row of the dashboard (Figure 7) shows throughput, which MORE did not define as a resource effi-ciency indicator but is an important value for plant personnel and can be considered as an REI, as it shows the utilisation of the “resource” plant.


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4.4. Lenzing -Evaporator optimisation of a cellulose plant Lenzing operates a reference factory for producing man-made cellulose fibres using both renewable sources (wood) and fossil resources (chemicals). The main objective of the partner Lenzing was to optimize the specific steam consumption together with the overall cycle cost in a set of evaporator lines. This opti-mization would lead to achieving a significant saving of energy while recovering the highest amount of raw material.

The new evaporator control logic and the new cooling tower control developed in the MORE project were fully implemented in the Distributed Control System (DCS). The DCS runs the operation parameters auto-matically, so there is no need for a Decision Support System (DSS) to improve the energy consumption of a single evaporator.

Further, the DSS for the evaporation load allocation is a visualization of the whole evaporator section, con-taining several evaporators. It displays for the operator the allocation of the evaporators to the different spinbath cycles, the current operating status of every evaporator and the current load of every evaporator and spinbath cycle. It also displays the proposal of the MATLAB optimizer for a more energy efficient load allocation and the possible steam savings that come with the improved allocation. The operator has to manually start the MATLAB optimization script. The optimization step itself takes about 1 minute before a proposal for the improved allocation is presented to the operator.

Lenzing successfully optimized the specific steam consumption together with the overall cycle cost. Typical-ly for an evaporator line, the operators could already monitor the REI “specific steam consumption” (REI ID001) for the selected time period (Figure 8).

Figure 8: Specific steam consumption of a single evaporator for the selected time period

In order to identify a more efficient performance control for the evaporators Lenzing developed a visualisa-tion of the specific steam consumption over the evaporator capacity for different control value settings (fixed operating points). This modified REI visualisation can be seen in Figure 9.


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Figure 9: Specific steam consumption of a single evaporator for different control values (operating points)

The visualisation of the normalised average costs per time for a single evaporator can be seen in Figure 10. The data evaluation and reconciliation for an REI visualisation of all evaporators is still going on.

Figure 10: Normalised average costs per time for evaporator 38 with the calculated optimal cleaning cycle time and the historical cleaning cycle time

4.5. Pulp & Paper

The pulp and paper industry is part of the bigger industrial segment of forest industry. Existing alongside the pulp and paper industry are e.g. saw mills and the wood-based panels industry, wood construction and the carpentry industry. Here, the focus is on the traditional pulp and paper industry. New products, such as biofuels, biochemicals and biopolymers are being developed from the forest. Traditional forest industry products may be enhanced with smart features or they may be produced using new methods.

Pulp is a lignocellulosic fibrous material prepared from wood, fibre crops, or from recycled paper. Pulp is obtained by chemically or mechanically separating cellulose fibres from raw materials, or by repulping pa-


23

per for recycling (RCF). Pulp is mainly used in paper and paperboard production. It is one of the most abun-dant raw materials worldwide.

Figure 11 shows a multi-structure of the production plant(s) in the pulp and paper industry.

Figure 11: Pulp and paper industry – Multi-product mill (IPPC, 2015)

More information concerning the pulp and paper industry, its processes and production volumes can be found from MORE (2016).

Applicability of the MORE principles was also evaluated. These principles developed in MORE were also found to be relevant for the pulp & paper industry. VTT, the paper mill and its parent company personnel evaluated together the applicability of the listed MORE indicators for the pulp and paper sector. All activi-ties in the mill area were included in the selection process. In principle, all the indicators for continuous processes are relevant expect those related to flare gas. Batch processes have diminished so that they are found nowadays only in certain special cases. Indicators that relate to chemical reactions (e.g. selectivity, conversion rate, reactant efficiency) may apply in certain chemical pulping side processes, but commonly the focus is on wood, fibres and components of wood such lignin, hemicelluloses and cellulose.

The applicability and usability of REIs were discussed in MORE (2016), and the results were similar to those based on the answers to the questionnaire distributed to MORE industrial case partners, which was pre-sented in Section 3.1. The questionnaire setting was rather different due to the earlier phase of the project. The case mill reported utilizing 17 of the listed REIs, which is a high number. Information is not available concerning the extent to which these REIs are applied, for example whether they are online, or which ones are visible from the control room guiding the operators’ actions. The case mill could consider applying an additional 2 REIs in the future. The pulp & paper industry found REIs to be relevant, except that 11 did not apply as such to the pulp & paper sector at the moment, as discussed above. This does not mean they are not relevant for the other industrial fields, or in future biorefineries also in the pulp & paper industry.

The pulp and paper case focus was on identifying the process area in question, specifying the most suitable indicators and readiness to apply the indicators, and on further actions needed. The process area of one paper mill was selected on the basis of resources consumed and on opportunities to influence resource consumption through the actions of the operators, and to target improving the resource efficiency. The possible indicators were first identified and the selected indicators were verified by the modified RACER evaluation tool developed in the MORE project (MORE 2017b, Kalliski et al. 2015).

The resource under focus was electricity consumption at a PGW plant. For example, specific electricity con-sumption for ground pulp and for the PGW (pressure groundwood pulping) main line was found to be im-portant. One focus was also on quality parameters of pulp, such as freeness and tensile strength. These may be seen as constraints on the quality but pulp (fibres) is a complicated product. Complex interactions


24

take place depending on the specific electricity consumption, other parameters in the PGW pulping, wood species, paper machine requirements for the pulp, end product and recipe at the paper mill, as well as many other considerations. The paper mill has a good preparedness to implement the indicators, since the main measurements are already available online.

The following generic REIs were recognized, developed and found to be important, with the main focus on specific electricity consumption (SEC):

In this concept, the REI “Energy required” is used in the forms of

𝑅𝐸𝐼𝐸𝑅,1 =𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑑 𝑖𝑛 𝑔𝑟𝑖𝑛𝑑𝑖𝑛𝑔 𝑎𝑛𝑑 𝑟𝑒𝑓𝑖𝑛𝑖𝑛𝑔

𝐹𝑖𝑛𝑖𝑠ℎ𝑒𝑑 𝑔𝑟𝑜𝑢𝑛𝑑 𝑝𝑢𝑙𝑝

𝑅𝐸𝐼𝐸𝑅,2 =𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑑 𝑖𝑛 𝑃𝐺𝑊 𝑚𝑎𝑖𝑛 𝑙𝑖𝑛𝑒 𝑔𝑟𝑖𝑛𝑑𝑖𝑛𝑔

𝑀𝑎𝑖𝑛 𝑙𝑖𝑛𝑒 𝑔𝑟𝑜𝑢𝑛𝑑 𝑝𝑢𝑙𝑝

𝑅𝐸𝐼𝐸𝑅,3 =𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑑 𝑖𝑛 𝑃𝐺𝑊 𝑟𝑒𝑗𝑒𝑐𝑡 𝑙𝑖𝑛𝑒 𝑟𝑒𝑓𝑖𝑛𝑖𝑛𝑔

𝑅𝑒𝑗𝑒𝑐𝑡 𝑙𝑖𝑛𝑒 𝑝𝑢𝑙𝑝

Quality-related important specific indicators were also recognized and developed. These cannot be found from the final list of REIs. They take into account freeness (SCF) and tensile strength. Freeness is a common way to characterize pulp suspension properties. It determines the drainage properties of the pulp suspen-sion. These are not clear resource efficiency indicators, where e.g. the resources consumed are divided according to product output, but are still closely connected to the resources consumed.

The most important specific indicators, which cannot be tagged with the defined final REIs in MORE, were

𝐼𝐶𝑆𝐹,1 =𝐹𝑟𝑒𝑒𝑛𝑒𝑠𝑠 (𝐶𝑆𝐹)

𝑅𝐸𝐼𝐸𝑅,1

𝐼𝐶𝑆𝐹,2 =𝐹𝑟𝑒𝑒𝑛𝑒𝑠𝑠 (𝐶𝑆𝐹)

𝑅𝐸𝐼𝐸𝑅,2

Measurements for relevant inputs and outputs exist already. Quality parameters are continuously meas-ured with the chosen device for this purpose. Implementation of the REIs is under consideration by paper mill personnel.

Detailed information of the case, the indicators and of the RACER evaluation carried out can be found from MORE (2016).

4.6. Sugar industry Sugar is a basic ingredient in many of the foods and drinks in our diet and has been considered a strategic product by the EU for many years. It is manufactured worldwide from two basic sources: sugarcane and beetroot, but in Europe practically all production comes from beetroot. Today, many European sugar facto-ries are good examples of well-organized and fully automated companies.

The structure of a typical sugar factory is represented in Figure 12.


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Figure 12: Schematic of a sugar factory

KPIs and REIs are used in the sugar industry as a way of supervising production and assessing the efficiency of operations. Nevertheless, they are computed annually, covering different aspects ranging from the use of energy to environmental themes. They are not used to track daily production and implement changes according to values, as in the MORE project. Rather, they are general indicators geared towards other pur-poses. A few examples of KPIs are:

Tonne of sugar / tonne of beet

Energy consumption / tonne of beet (and/or per tonne of sugar or dry pulp)

Mass of residuals / tonne of beet

Air emissions as SO2, CO and NOx in the pulp dryer and in the boilers

OCD water

More information concerning the sugar industry, current technological phase, processes, typical KPIs and REIs used and the markets can be found from MORE (2016).

The UVA team evaluated the applicability of the MORE approach to resource efficiency improvement in the sugar industry. For this purpose, the production structure was analysed as well as the current practice for process supervision. The structure of sugar factories corresponds to that of a typical process industry, with relevant diversity in the type of process involved: chemical reactions, diffusion, evaporation, crystallization, drying, storage, heating, electricity cogeneration, boiling, filtering, etc., operating both in continuous and batch modes. A conclusion of the study was that the general principles and the REIs developed for MORE can be applied, with simple adaptations, to the sugar industry, with those linked to batch processes being the ones in need of more refinement. Moreover, the sugar factories would strongly benefit from the use of such indicators and the associated methodology that provides a more frequent and efficient supervision of production than the post-campaign analysis currently performed, allowing the implementation of on-line corrections.

Sugar factories share a lot of processes that are typically found in process industries and, as such, present similar problems when defining supervision strategies. The MORE project has defined some guidelines or principles that can be applied when designing REIs and they are fully applicable to the sugar industry case. In addition, when defining REIs one should consider:

The temporal range

Delays between input of raw materials/energy and the final product

Storage of intermediate products

Many REIs can be applied to either the equipment or plant level


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A set of REIs could be defined to measure the efficiency of operation with regard to several aspects, such as use of energy, or waste. These are listed below:

In this concept, the generic REI “Energy required” can be used in the form of

REI 1 Consumed energy/sugar kJ/kg

𝐽1 =∫ 𝐸𝑐(𝑡)𝑑𝑡𝑇

0

∫ 𝑊𝑎𝑧(𝑡)𝑑𝑡𝑇

0

Where Ec stands for the energy consumed per unit time and Waz is the flow of sugar produced. This REI gives an indication of the energy spent in the production of a kilogram of sugar. It may be use-ful, but it depends on beet quality, weather, etc.

The derived generic REI “Specific Resource Loss” can be used in the form of

REI 2 Lost energy/sugar kJ/kg

𝐽2 =∫ 𝐸𝑑(𝑡)𝑑𝑡𝑇

0

∫ 𝑊𝑠𝑢𝑔𝑎𝑟(𝑡)𝑑𝑡𝑇

0

Where Ed is the flow of energy lost and W is the flow of sugar. This REI gives an indication of the en-ergy lost in the production of a kilogram of sugar. It may be useful, but it depends on beet quality, weather, etc.

The generic REI “Utilities/Raw Material required” can be used in the form of

REI 3 Raw materials or utilities used /sugar kg/kg

az

n

i i

W

UJ

u

13

Where Ui stand for any amount of consumed raw materials and utilities under consideration. This REI gives an indication of the amount of raw materials (e.g. auxiliary products) and utilities con-sumed in the production of a kilogram of sugar: chemicals, air, water, etc. It may be defined for each of them separately, because their aggregation, unless made in economic terms, is not very meaningful. It also depends on external factors such as beet quality.

The form of the next REI is derived from the generic REI “Material Yield”. The difference is that it accounts for sugar content in beet instead of the total mass of beet:

REI 4 Sugar production/sugar in beet %

100

)(

)(

0

cos

4

T

T

o

az

dttonpolarizatiW

dttW

J

Here Wcos is the inflow of beet and polarization is a measure of the saccharose content of beet. This REI gives an indication of the efficiency in the conversion of the saccharose contained in the beet to sugar crystals . This is more useful, as it depends more on factory operation than on external fac-tors.


27

The derived generic REI “Relative Energy Efficiency” can be used in the form of

REI 5 Optimal energy consumption/actual %

100,

,5

MJnconsumptioenergyActual

MJnconsumptioenergyMinimalJ

In order to avoid dependence on the factors previously mentioned, this REI considers energy con-sumption with respect to the best possible value computed in the current operating conditions with respect to raw materials, weather, etc. It provides a measure of the existing improvement margin in the process operation.

Similar types of REIs can be applied to any of the main variables that characterize sugar production, for example, steam consumption, sugar produced etc. These derived generic REIs “Relative Utility/Raw Materi-al Efficiency” can be used for example in the form:

100/,

/,6

htnconsumptiosteamActual

htnconsumptiosteamMinimalJ

100/,productionsugar

/,7

htActual

htproductionsugarMaximalJ

In the same way as J4, the following REIs (REI 8 and 9) can be defined in the diffusion section to measure the sugar in the juice with respect to the saccharose content of the beet, and the energy consumed per tonne of juice extracted.

The form of the next REI is derived from the generic REI “Material Yield”. The difference is that it accounts for sugar content in beet rather than for the total mass of beet

REI 8. Saccharose in the juice/Saccharose in beet %

100

)(

)(

0

8

T

T

o

dttonpolarizatibeetsbeetsflow

dttonpolarizatijuicejuiceflow

J

The generic REI “Energy required” can be used in the form of

REI 9. Energy consumption/t of juice extracted kJ/t

100

)(

)(

0

9

T

T

o

dttjuiceflow

dttEc

J

There are also specific REIs designed to measure particular aspects of each process unit which cannot be tagged with the defined final REIs in MORE. For example, for the batch crystallizers, one can define:

REI 10 Efficiency of the operation, independent of the juice quality

𝐽10 =

𝑆𝑢𝑔𝑎𝑟 𝑝𝑒𝑟 𝑏𝑎𝑡𝑐ℎ𝐵𝑎𝑡𝑐ℎ 𝑡𝑖𝑚𝑒

𝑆𝑡𝑒𝑎𝑚 𝑝𝑒𝑟 𝑏𝑎𝑡𝑐ℎ∙

1

𝐵𝑟𝑖𝑥𝑠𝑦𝑟𝑢𝑝100 ∙

𝑃𝑢𝑟𝑖𝑡𝑦𝑠𝑦𝑟𝑢𝑝100

This gives the energy efficiency of a batch in the crystallizers, corrected as a function of syrup purity and Brix value.


28

In addition to the “technical” REIs, the economy of the process can be considered directly if prices for the different flows are included. This facilitates aggregation of heterogeneous quantities, but makes the index again dependent on prices and not on technical operating decisions. One example of this could be (not within the defined final REIs in MORE):

REI 11 Economic benefit per sugar € /kg

𝐽11 =∫ [𝛿 ∙ 𝑊𝑎𝑧(𝑡) − 𝛽 ∙ 𝐸𝑐(𝑡) − 𝛾 ∙ 𝑊𝑐𝑜𝑠(𝑡)]𝑑𝑡𝑇

0

∫ 𝑊𝑎𝑧(𝑡)𝑑𝑡𝑇

0

This gives the benefits per unit of sugar produced, assigning prices to sugar, energy and beets, not counting other factors such as personnel or infrastructure costs.

These REIs provide valuable information on the performance of the factory operation in utilization of the resources and, if computed on a shorter time scale, could be used not only for monitoring but also for tak-ing corrective actions in real-time in order to improve production efficiency.

Some sugar industry case studies were performed and analysed in which the MORE methodology for esti-mating reliable values for the process variables using data reconciliation (DR) for computing REIs was being used successfully, as well as other cases in sugar plants were analysed which had indicated an interest in considering these indicators to support plant-wide efficiency actions. The application of these methods on a plant-wide scale will bring clear benefits to the sugar industry.

Examples of these case studies were: optimization of steam distribution in the factory, providing recom-mendations on its best possible usage; the cogeneration system and the balance between electricity, steam and production and the synchronization between the evaporation and sugar end sections, which is very important for avoiding bottlenecks in the operation and maximizing the flow of beets processed.

The on-line computation of REIs would require the implementation of a data reconciliation layer able to provide reliable information concerning the main measured variables and estimations of other ones, so that the indicators could be computed. Then, the REIs could be used as cost functions to be optimized in different operation problems.


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5. Final MORE real-time resource efficiency indicators (REIs)

In this section the final real-rime resource efficiency indicators (REIs) are presented, as well as the open access web interface for the REI database. The development stages for introducing these final REIs were described in Section 3. With real-time REIs the effect of technical improvements and operational policies can be measured and actions can be derived for real-time or near real-time plant performance improve-ments.

A step-by-step guidebook has also been developed to support the European process industry in gaining better knowledge of production processes, leading to improvements in resource efficiency. The step-by-step procedure helps the user to identify and implement real-time or near real-time REIs suitable for moni-toring plant operations and guiding managers and operators towards improved resource efficiency (MORE 2017b).

5.1. Web interface for the REI database An easy to use, open access web interface tool for searching the best REIs for your plant’s purposes is available online. The work-flow is indicated below:

Analysis page: Enter the data about your process into the analysis form (Figure 13)

o Select the processing type:

Continuous

Batch

Integrated batch and continuous operation

o Is real-time optimization intended?

o What are the relevant factors for the considered process:

Material

Energy

Environmental impact

In case environmental contributions need to be considered an additional set of checkboxes asks for detailed information

Evaluation page: A list of the resulting REIs is shown (Figure 14) and a detailed overview of the REI information of interest can be accessed via a clickable link

Result page: The Set-card for the chosen indicator is displayed (Figure 14). This can be accessed by the clickable link to the REI in question in the Evaluation page or in the Complete REI list page

Complete REI list page: This page provides the user with the entire list and is searchable via the search bar

The REI database can be accessed via the web interface: http://more.bci.tu-dortmund.de/

http://more.bci.tu-dortmund.de/


30

Figure 13: REIs can be searched in the Analysis page by entering data of your process and of your interests


31

Figure 14: By clicking Submit in the Analysis page, the Evaluation page shows the search results. A detailed over-view is shown in the Result page by clicking the link. The parent REI can be accessed in the Result page, if the REI in question is derived from the parent REI


32

5.2. Final list of REIs The indicator database compiled consists of 16 main indicators that are listed below in the form of indicator set-cards compiling the most important information. Furthermore, 41 derived indicators are briefly listed underneath.

The database can be accessed at: http://more.bci.tu-dortmund.de/

Name (Abbreviation): Energy required (ER) ID: IN001

Formula: 𝐸𝑅 =

∑ 𝐸𝑖𝑖

𝑚𝑝

Efficiency category: Energy

Direction of improvement (Type): Lower (intensity)

Measurements required: 𝐸𝑖: energy amount from source i

𝑚𝑝: product mass

𝑖: energy sources

Description: Energy required per unit of product

Energy Performance Indicator according to ISO 50001

Name (Abbreviation): Utilities/Raw Material required (MR) ID: IN003

Formula: 𝑀𝑅 =

𝑈

𝑚𝑝

Efficiency category: Material


Measurements required: 𝑈: required utility


Description: Utilities/Raw Material required per unit of product (air, water, DI-water)

Name (Abbreviation): Unconverted Raw Material (URM) ID: IN005

Formula: 𝑈𝑅𝑀 =

∑ 𝑚𝑅,𝑖,𝑖𝑛 −𝑚𝑅,𝑖,𝑐𝑜𝑛𝑣𝑒𝑟𝑡𝑒𝑑𝑖

∑ 𝑚𝑅,𝑖,𝑖𝑛𝑖


Direction of improvement (Type): Higher (efficiency)

Measurements required: 𝑖: reactants

𝑚𝑅,𝑖,𝑖𝑛: reactant mass of species i introduced to the process

𝑚𝑅,𝑖,𝑐𝑜𝑛𝑣𝑒𝑟𝑡𝑒𝑑: converted reactant mass of species i

Description: Material losses in the process

http://more.bci.tu-dortmund.de/


33

Name (Abbreviation): Material Yield (MY) ID: IN006

Formula: 𝑀𝑌 =

∑ 𝑚𝑃,𝑗,𝑜𝑢𝑡𝑗

∑ 𝑚𝑅,𝑖,𝑖𝑛𝑖



Measurements required: 𝑖: reactants 𝑗: products


𝑚𝑃,𝑗,𝑜𝑢𝑡: product output of product j

Description: Overall process yield based on mass flow, also "mass conversion"

Name (Abbreviation): Overall Resource Yield (ORY) ID: IN007

Formula: 𝑂𝑅𝑌 =

∑ 𝐶𝑃,𝑗𝑚𝑃,𝑗,𝑜𝑢𝑡𝑗 + ∑ 𝐶𝐸,𝑡𝐸𝑡,𝑜𝑢𝑡𝑡

∑ 𝐶𝑅,𝑖𝑚𝑅,𝑖,𝑖𝑛𝑖 + ∑ 𝐶𝐸,𝑡𝐸𝑡,𝑖𝑛𝑡

Efficiency category: Material / Energy


Measurements required: 𝑖: reactants 𝑗: products 𝑡: energy type 𝐶: weighting factor

𝐸𝑡,𝑖𝑛/𝑜𝑢𝑡: energy amount of type t introduced/created



Description: Overall process yield based on weighted flows

Name (Abbreviation): Specific Product Loss (SPL) ID: IN008

Formula: 𝑆𝑃𝐿 =

∑ 𝑚𝑃,𝑙𝑜𝑠𝑠,𝑗𝑗

∑ 𝑚𝑃,𝑗,𝑜𝑢𝑡𝑗



Measurements required: 𝑗: products


𝑚𝑃,𝑙𝑜𝑠𝑠,𝑗: losses of product j

Description: Loss of valuable product specific to the amount of product produced


34

Name (Abbreviation): Overall Weighted Waste (OWW) ID: IN011

Formula: 𝑂𝑊𝑊 =

∑ 𝐶𝑖𝑚𝑊,𝑖𝑖

𝑚𝑝



Measurements required: 𝑖: waste type 𝐶: weighting factor

𝑚𝑊,𝑖: waste mass of species i


Description: Waste based on waste “currency” per unit of product

Name (Abbreviation): Specific Catalyst loss (SCL) ID: IN016

Formula: 𝑆𝑃𝐿 =𝑚𝑐𝑎𝑡,𝑙𝑜𝑠𝑠𝑚𝑝



Measurements required: 𝑚𝑝: product mass

𝑚𝑐𝑎𝑡,𝑙𝑜𝑠𝑠: mass of catalyst lost

Description: Process performance with respect to catalyst losses

Name (Abbrevia-tion):

Overall Efficiency based on Energy Currency (OEEC)

ID: IN025

Formula: 𝑂𝐸𝐸𝐶

=∑ (𝐸𝐶𝑖𝑚𝑖,𝑖𝑛)𝑖 − ∑ (𝐸𝐶𝑗𝑚𝑗,𝑜𝑢𝑡)𝑗 + 𝐸𝑒𝑙𝐸𝐶𝑒𝑙 + 𝑄𝑆𝑡𝑒𝑎𝑚𝐸𝐶𝑆𝑡𝑒𝑎𝑚

𝑚𝑝

Efficiency category: Material / Energy


Measurements required:

𝑖: waste type 𝐸𝐶: weighting factor Energy currency

𝑚𝑖,𝑖𝑛: mass of ingoing species i

𝑚𝑖,𝑖𝑜𝑢𝑡: mass of outgoing species i


Description: Energy streams of different nature are weighted by an energy currency which accounts for the different value of the energy streams (e.g. electrical power has a higher value compared to steam (exergy)).


35

Name (Abbrevia-tion):

Overall Operation Effi-ciency based on Utility Currency (OEUC)

ID: IN026

Formula: 𝑂𝐸𝑈𝐶

=∑ (𝑈𝐶𝑖𝑀𝑖)𝑖 + ∑ (𝐸𝑒𝑙(𝑡) ∗ 𝑈𝐶𝑒𝑙 + ∑ 𝑈𝐶𝑖

𝑛𝑠𝑖=1 ∗ 𝑄𝑖(𝑡))

𝑡𝑜𝑝𝑡=0

𝑡𝑜𝑝

Efficiency category: Material /Energy


Measurements required: 𝑀𝑖: fixed utilities required to start

𝑄𝑖: utility streams

𝑈𝐶: weighting factor utility currency

𝐸𝑒𝑙: electrical energy 𝑛𝑠: number of utility streams

𝑡𝑜𝑝: total operation time

Description: Total use of utilities of different nature (streams, catalysers, cleaning products, etc.) per unit of time, which are weighted by utility currency, accounting for their different costs.

Name (Abbreviation): Component Material Efficiency (ME) ID: IN033

Formula: 𝑀𝐸 =

∑ 𝑚𝑝,𝑠𝑡𝑜𝑖𝑐,𝑘𝑝

𝑚𝑘,𝑖𝑛



Measurements required: 𝑚𝑝,𝑠𝑡𝑜𝑖𝑐,𝑘: stoichiometric mass equivalent of the reactant k incorporated into the

product material (formula given at the end of this appendix)

𝑚𝑘,𝑖𝑛: mass of reactant k introduced

Description: stoichiometric mass equivalent of raw material k in product p per mass of k introduced

Name (Abbreviation): Component Material Input (MI) ID: IN034

Formula: 𝑀𝐼 =𝑚𝑘,𝑖𝑛𝑚𝑝



Measurements required: 𝑚𝑝: product mass

𝑚𝑘,𝑖𝑛: mass of reactant k introduced

Description: mass of raw material k introduced per mass of product obtained


36

Name (Abbreviation): Overall Resource Efficiency (ORE) ID: IN035

Formula: 𝑂𝑅𝐸𝑗 =

∑ 𝑚𝑝,𝑖𝑖

𝑟𝑗

Efficiency category: Material or Energy


Measurements required: 𝑚𝑝,𝑖: mass of product i

𝑟𝑗: resource used of resource entity j

Description: Sequence-dependent resource consumption and other overhead efficiency

Name (Abbreviation): Separation Yield (SY) ID: IN038

Formula: 𝑆𝑌 =

𝑚𝐶,𝑟𝑒𝑠𝑖𝑑 +𝑚𝑊,𝑟𝑒𝑠𝑖𝑑𝑚𝐶,𝑓𝑒𝑒𝑑 +𝑚𝑊,𝑟𝑒𝑠𝑖𝑑,𝑚𝑎𝑥

with 𝑚𝑊,𝑟𝑒𝑠𝑖𝑑 ≤ 𝑚𝑊,𝑟𝑒𝑠𝑖𝑑,𝑚𝑎𝑥



Measurements required: 𝑚𝐶,𝑟𝑒𝑠𝑖𝑑: mass of valuable component C in the product

𝑚𝑊,𝑟𝑒𝑠𝑖𝑑: mass of water in the product

𝑚𝐶,𝑓𝑒𝑒𝑑: mass of valuable component C fed to the separation unit

𝑚𝑊,𝑟𝑒𝑠𝑖𝑑,𝑚𝑎𝑥: maximum of residual water allowed in product formulation

Description: Mass of valuable component C plus water obtained from purification per theoretical maximum.

Name (Abbreviation): Gaseous Emissions (GE) ID: IN044

Formula: 𝐺𝐸 =

∑ 𝑚𝑖,𝑔𝑒𝑖

𝑚𝑝



Measurements required: 𝑚𝑖,𝑔𝑒: mass or volume of gaseous emissions of type i


Description: Sum of gas emission per unit of product


37

Name (Abbreviation): Global warming potential (GWP) ID: IN056

Formula: 𝐺𝑊𝑃𝑡𝑜𝑡 =∑𝐶𝐺𝑊𝑃,𝑖 𝑚𝑖𝑖

Efficiency category: Environmental

Direction of improvement (Type): Lower

Measurements required: 𝑚𝑖: mass of emission i

𝐶𝐺𝑊𝑃,𝑖: GWP weight for material i

Description: Mass of CO2 equivalent (eq) for selected functional unit

Derived Indicators:

ID Abbreviation Indicator Parent indicator

IN002 REE Relative Energy Efficiency IN001

IN004 RME Relative Utility/Raw Material Efficiency IN003

IN009 SRL Specific Resource Loss IN007

IN010 W Waste IN011

IN012 TOC Total Organic Carbon IN011

IN013 VOC Volatile Organic Compounds IN011

IN014 S Selectivity IN033

IN015 C Conversion IN005

IN017 CHPE CHP Efficiency IN007

IN018 OPPE Overall Power Plant Efficiency IN007

IN019 FE Furnace Efficiency IN007

IN020 LFG Loss to Flare Gas IN007

IN021 RLFG Resource Loss to Flare Gas IN007

IN022 FEL Flare Energy Loss IN007

IN023 PLFG Product loss to Flare Gas IN007

IN024 OEE Overall Enthalpy Efficiency IN025

IN027 CTE Cooling Tower Efficiency IN007

IN028 CEE Cooling Energy Efficiency IN031

IN029 EEE Electrical Energy Efficiency IN031

IN030 HEE Heating Energy Efficiency IN031

IN031 TEE Total Energy Efficiency IN024

IN032 HP Heat Product IN025

IN036 PEE Purification Energy Efficiency IN031

IN037 RE Reactant Efficiency IN006

IN039 TME Total Material Efficiency IN007

IN040 TRE Total Reactant Efficiency IN037

IN041 WP Waste Production IN010

IN042 TWP Total Waste Production IN041

IN043 WU Water Usage IN007

IN045 BP By-Products IN007


38

ID Abbreviation Indicator Parent indicator

IN046 MWV Materials used by weight or volume G4-EN1 IN006

IN047 ECO Energy consumption within the organization G4-EN3 IN007

IN048 EI Energy intensity G4-EN5 IN007

IN049 TW Total water withdrawal by source G4-EN8 IN003

IN050 TWW Total weight of waste by type and disposal method G4-EN23 IN010

IN051 TMC Total material consumption IN007

IN052 TEC Total energy consumption IN006

IN053 TMO Total material output IN007

IN054 TEO Total energy output IN006

IN055 PO Processed output IN007

IN057 FPE Finished products efficiency performance IN007

𝑚𝑘,𝑠𝑡𝑜𝑖𝑐,𝑝 is the mass of reactant 𝑘 that is incorporated into valuable product and 𝑚𝑖𝑛,𝑘 is the mass of sub-

stance 𝑘 fed to the batch. The material efficiency improves with the selectivity of the reaction (∑ 𝑚𝑘,𝑠𝑡𝑜𝑖𝑐,𝑝𝑝

increases) and the minimization of material losses (𝑚𝑖𝑛,𝑘 decreases). For an arbitrary reaction according to Equation (A2-1), 𝑚𝑘,𝑠𝑡𝑜𝑖𝑐,𝑝 is calculated by Equation (A2-3). The required coefficients 𝜈𝑘,𝑝 are obtained by

decomposition into the ideal net formation reactions of the products, see Equation (A2-2),

∑ 𝜈𝑘𝑅𝑘 𝑖⏟ 𝑟𝑒𝑎𝑐𝑡𝑎𝑛𝑡𝑠

→ ∑ 𝜈𝑝𝑃𝑝 𝑝⏟ 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑠

+ ∑ 𝜈𝑡𝑊𝑡 𝑡⏟ 𝑤𝑎𝑠𝑡𝑒

(A2-1)

∑ 𝜈𝑘,𝑗𝑅𝑘 𝑘 → 𝜈𝑝𝑃𝑝 ∀ 𝑝 with 𝜈𝑘 = ∑ 𝜈𝑘,𝑝𝑝 (A2-2)

𝑚𝑘,𝑠𝑡𝑜𝑖𝑐,𝑝 = 𝑛𝑘,𝑠𝑡𝑜𝑖𝑐,𝑝𝑀𝑘 = 𝑛𝑝 |𝜈𝑘,𝑝|

𝜈𝑝𝑀𝑘 = 𝑚𝑝

|𝜈𝑘,𝑝| 𝑀𝑘

𝜈𝑝 𝑀𝑝∀ 𝑘, 𝑝 (A2-3)

Here, Mk and Mp are the molecular weights and νk,p and νp are the stoichiometric coefficients. In cases in

which the raw material is converted into multiple products or into an additional undesired side product, the atom efficiency is taken into account.


39

6. Summary

Existing Key Performance indicators (KPIs) can rarely support day-to-day decision making processes in plant operations. With real-time Resource Efficiency Indicators (REIs), the effects of technical improvements and operational policies can be measured and actions can be derived for real-time or near real-time plant per-formance improvements.

In the first stage of the REI development process an initial list of indicators was defined (MORE 2014 a, 2014b). This list was put to the test, both practically and theoretically, by MORE partners and by industrial case studies, resulting in a filtered list of indicators. Next, the industrial partners implemented some indica-tors and put them to internal tests in order to determine whether they were relevant or not. These lists of REIs and a thorough discussion among the partners resulted in the condensed list of final REIs presented in this paper. Additionally, the applicability of the MORE approach to resource efficiency improvement and the applicability of REIs defined in MORE were evaluated in other industries by carrying out feasibility stud-ies in the pulp & paper sector (VTT team), and in the sugar industry sector (UVA team).

There are only a few main generic REIs, providing the basis for a number of derived REIs. The REI list pre-sented in this report, in Section 5.2., is the final list of REIs defined in the MORE project. REIs are located in the database with open access through a web page interface, which is presented in Section 5.1.

Final REIs were found to be relevant by MORE industrial partners representing the chemical industry; none of the partners marked any of the indicators as non-relevant. This result is a very strong indication that the REIs are in fact relevant, although not necessarily useful directly for all industrial plants. Partners were found to be able to use, could possibly use, or were are already using the proposed REIs as such in 95 cases. They are already using the REIs in 66 cases. Three of the REIs could be used by all industrial partners. These are generic REIs IN001 ER “Energy required” and IN003 MR “Utilities/Raw Material required”, and the de-rived REI IN002 REE “Relative Energy Efficiency”. Bearing in mind the fact that only four plants provided answers, the result can be regarded as a rather good one. In the pulp & paper sector, in principle, the indi-cators for continuous processes were found to be relevant and applicable, except few of them did not cur-rently apply in the pulp & paper sector as such (e.g. those related to flare gas, catalysts and selectivity). In the sugar industry, the production structure corresponds to that of a typical process industry. As such, most of the REIs developed for MORE could be applied, with minor adjustments, to the sugar industry, with those linked to batch processes being the ones in need of more refinement.

Petronor, in its case study, applied the generic REIs IN003 MR “Raw material required” for fresh hydrogen produced with respect to hydrocarbon processed and for hydrogen consumed in the reactors with respect to hydrogen produced, and IN008 SPL “Specific Product Loss” for minimisation of lost hydrogen to fuel gas. Petronor also applied the derived REI IN004 RME “Relative Utility/Raw Material Efficiency” for optimal hy-drogen produced with respect to actual hydrogen produced and for actual hydrocarbon processed with respect to optimal hydrocarbon processed.

For the purpose of their case study INEOS decided to monitor the generic REIs IN001 ER “Energy Required”, IN003 MR “Raw Material Required”, IN003 MR “Utilities Required”, IN006 MY “Material Yield”, IN007 ORY “Overall Resource Yield”, IN025 OEEC “Overall Efficiency based on Energy Currency”, IN010 W “Waste” and IN011 OWW “Overall weighted waste”. These REIs were discussed with plant personnel and were agreed to be very useful for real-time process operations. Additionally, INEOS wanted to find out how much raw ma-terial is converted into energy, and whether minimizing this stream is economically realistic. Two examples are selected to support the choice of REIs. These are different REIs for an AN plant related to energy, raw material or utility, and an indicator combining energy- and resource-related figures into one indicator for an EO plant.

REIs were defined and implemented for the BASF plant. The visualization was focused on the most promis-ing steps of the BASF case, which are the distillation and post-processing part. The REIs used in the dash-board were IN006 MY “Material Yield” (product yield), the derived form IN004 RME “Raw Material Efficien-


40

cy” (material efficiency), IN001 ER “Energy Required” (steam per mass of product) and IN003 MR “Utilities Required” (utilities per mass of product).

Lenzing successfully optimized the specific steam consumption together with the overall cycle cost. Typical-ly for an evaporator line, the operators could already monitor the REI IN001 ER “Energy required” (specific steam consumption). In order to identify a more efficient performance control for the evaporators Lenzing developed a visualisation of the specific steam consumption over the evaporator capacity for different con-trol value settings.

In the pulp and paper case, three generic REIs were recognized, developed and found to be important, with the main focus on specific electricity consumption (SEC), based on IN001 ER “Energy Required”. They fo-cused on electricity consumption in grinding and/or in refining with respect to finished ground pulp, main line ground pulp, or reject line pulp. Quality-related important specific indicators were also recognized and developed which cannot be found from the final list of REIs. They take into account freeness (SCF) and ten-sile strength.

For the sugar industry, a set of REIs could be defined to measure the efficiency of operation. For generic REIs these could be two indicators based on IN001 ER “Energy Required” (consumed energy per sugar and energy consumption per juice extracted), IN008 SPL “Specific Resource Loss” (Lost energy per sugar), IN003 MR “Utilities/Raw Material required” (raw materials or utilities used per sugar) and IN006 MY “Material Yield” (Sugar production per sugar in beet, or saccharose in the juice per saccharose in beet ). The derived generic REIs could be IN002 REE “Relative Energy Efficiency” (Optimal energy consumption per actual con-sumption), or two indicators for IN004 RME “Relative Utility/Raw Material Efficiency” (minimum steam consumption per actual consumption and maximum sugar production per actual). There are also specific REIs designed to measure particular aspects which cannot be tagged with the defined final REIs in MORE.


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7. References

CEPI Confederation of European Paper Industries. 2016. Key Statistics 2015 European pulp & paper indus-try. http://www.cepi.org/statistics/keystatistics2015

European Commission. 2016. Resource Efficiency. http://ec.europa.eu/environment/resource_efficiency/ [Cited 30.8.2016]

Fitz-Gibbon C. T. 1990. Performance indicators, BERA Dialogues (2), ISBN 978-1-85359-092-4

IPPC. 2015. Best Available Techniques (BAT) Reference Document for the Production of Pulp, Paper and Board. ISBN 978-92-79-48167-3 (PDF)

Kalliski, M., Beisheim, B., Krahé D., Enste, U., Krämer, S. & Engell, S. 2016. Real-time resource efficiency indicators, Material and energy efficient plant operation. atp edition 1-2/2016

Kalliski M, Engell S. 2016. Real-time resource efficiency indicators for monitoring and optimization of batch-processing plants. The Canadian Journal of Chemical Engineering. DOI: 10.1002/cjce.22717

Kalliski M., Krahé D., Beisheim B., Krämer S. and Engell S. 2015. Resource efficiency indicators for real-time monitoring and optimization of integrated chemical production plants. Computer Aided Chemical Engineer-ing 37, 1949-1954. ISBN 978-0-444-63429-0. http://dx.doi.org/10.1016/B978-0-444-63576-1.50019-4

Minnich, C., Hardy, S., and Krämer, S. 2016. Stopping the babylonian confusion: An updated nomenclature for process analyzers in PAT applications. Chemie Ingenieur Technik, 88 (6), 694–697, doi: 10.1002/cite.201500188

MORE EU project. 2017a. D5.2 Report of conclusions about the use of REIs and the DSS to improve efficien-cy in the selected sites Project deliverable. http://www.more-nmp.eu/outcomes/deliverables/

MORE EU project. 2017b. D6.6. Step-by-step guidebook on “How to succeed in the identification and im-plementation of real-time or near real-time Resource Efficiency Indicators for your plant or industrial sec-tor”. Project deliverable. http://www.more-nmp.eu/outcomes/deliverables/

MORE EU project. 2016. D1.5 Description of generally applicable and sector-specific indicators for the pro-cess industries, with two feasibility studies. Project deliverable. http://www.more-nmp.eu/wp-content/uploads/2016/09/MORE_D1-5_Description_indicators_final.pdf

MORE EU project. 2015a. D5.1 Document describing the choice of the demonstration cases. Project deliv-erable. http://www.more-nmp.eu/wp-content/uploads/2015/05/MORE-D5.1-Document-describing-the-choice-of-the-demonstration-cases.pdf

MORE EU project. 2015b. More Project started work on generalisation of Resource Efficiency Indicators to other industries. Project Communication. http://www.more-nmp.eu/wp-content/uploads/2015/11/MORE_Press-release_2nd-ISP-Meeting-Sept-2015_final.pdf

MORE EU project. 2014a. D1.2 Set of real-time resource efficiency indicators for continuously operated integrated plants. Project deliverable. http://www.more-nmp.eu/wp-content/uploads/2016/02/D1.2_Set-of-real-time-REIs-for-continuously-operated-integrated-plants_public.pdf

MORE EU project. 2014b. D1.3 Set of real-time resource efficiency indicators for batch processing plants. Project deliverable. http://www.more-nmp.eu/wp-content/uploads/2014/11/MORE-D1.3-Set-of-real-time-resource-efficiency-indicators-for-batch-processing-plants.pdf

NAMUR 2017. Resource Efficiency Indicators for monitoring and improving resource efficiency in processing plants. NAMUR Recommendation NE 162. Planned Edition 2017

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http://www.more-nmp.eu/wp-content/uploads/2016/09/MORE_D1-5_Description_indicators_final.pdf

http://www.more-nmp.eu/wp-content/uploads/2015/05/MORE-D5.1-Document-describing-the-choice-of-the-demonstration-cases.pdf

http://www.more-nmp.eu/wp-content/uploads/2015/05/MORE-D5.1-Document-describing-the-choice-of-the-demonstration-cases.pdf

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http://www.more-nmp.eu/wp-content/uploads/2015/11/MORE_Press-release_2nd-ISP-Meeting-Sept-2015_final.pdf

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