Cover
Title Page
1. Copyright
Preface
Part I: Resource Efficiency Metrics and Standardised Management Systems
1. Chapter 1: Energy and Resource Efficiency in the Process Industries
  1. 1.1 Introduction
  2. 1.2 Energy and Resources
  3. 1.3 Energy and Resource Efficiency
  4. 1.4 Evaluation of Energy and Resource Efficiency
  5. 1.5 Evaluation of Energy and Resource Efficiency in Real Time
  6. 1.6 The Chemical and Process Industry
  7. 1.7 Recent and Potential Improvements in Energy and Resource Consumption of the Chemical and Process Industries
  8. 1.8 What Can Be Done to Further Improve the Resource Efficiency of the Process Industry?
  9. 1.9 Conclusions
  10. References
2. Chapter 2: Standards, Regulations and Requirements Concerning Energy and Resource Efficiency
  1. 2.1 Introducing a Long-Term Development
  2. 2.2 Normative Approaches on Energy and Resource Efficiency
  3. 2.3 Achievements of Energy and Resource Management
  4. 2.4 Conclusion
  5. References
3. Chapter 3: Energy and Resource Efficiency Reporting
  1. 3.1 Executive Summary
  2. 3.2 Introduction
  3. 3.3 Obligatory Reporting Mechanisms
  4. 3.4 Voluntary Reporting Mechanisms
  5. 3.5 Other Reporting Mechanisms
  6. 3.6 Summary of the Energy and Resource Efficiency Reporting Requirements
  7. References
4. Chapter 4: Energy Efficiency Audits
  1. 4.1 Introduction
  2. 4.2 Stage 1: Current Energy Status
  3. 4.3 Stage 2: Basic Analysis
  4. 4.4 Stage 3: Detailed Analysis and Collection of Ideas
  5. 4.5 Stage 4: Evaluation and Selection of Measures
  6. 4.6 Stage 5: Realization and Monitoring
  7. 4.7 Extension to Resource Efficiency
  8. 4.8 Closing Remark
  9. References
Part II: Monitoring and Improvement of the Resource Efficiency through Improved Process Operations
1. Chapter 5: Real-Time Performance Indicators for Energy and Resource Efficiency in Continuous and Batch Processing
  1. 5.1 Introduction
  2. 5.2 Real-Time Resource Efficiency Indicators
  3. 5.3 Evaluation of the Suitability of Resource Efficiency Indicators
  4. 5.4 Hierarchical Modelling of Continuous Production Complexes
  5. 5.5 Batch Production
  6. 5.6 Integrated Batch and Continuous Production
  7. 5.7 Conclusions
  8. Appendix: Decomposition of $c05-math-326$
  9. References
2. Chapter 6: Sensing Technology
  1. 6.1 Introduction
  2. 6.2 Sensing: General Considerations and Challenges
  3. 6.3 Energy Saving by Means of Accurate Metering
  4. 6.4 Latest Advancements in Spectroscopy Technology for Process-Monitoring-Based Efficiency
  5. 6.5 Process Analytical Technologies (PAT)
  6. 6.6 Soft Sensors. Access to the “Truth” Distributed Among a Plurality of Simple Sensors
  7. 6.7 MEMS-Based Sensors. Smart Sensors
  8. 6.8 Future Trends in Sensing with Promising Impact on Reliable Process Monitoring
  9. 6.9 European R&D: Driving Forward Sensing Advancements
  10. 6.10 Conclusion
  11. References
3. Chapter 7: Information Technology and Structuring of Information for Resource Efficiency Analysis and Real-Time Reporting
  1. 7.1 Introduction
  2. 7.2 Information Technology in the Process Industries
  3. 7.3 Resource Flow Modelling and Structuring of Information
  4. 7.4 From Formulae to Runtime Software
  5. 7.5 Industrial Installations
  6. 7.6 Summary and Conclusions
  7. References
4. Chapter 8: Data Pre-treatment
  1. 8.1 Measurement Errors and Variable Estimation
  2. 8.2 Data Reconciliation
  3. 8.3 Gross Errors Detection and Removal
  4. 8.4 Data Pre-treatment and Steady-State Detection
  5. 8.5 Dynamic Data Reconciliation
  6. 8.6 Conclusions
  7. References
5. Chapter 9: REI-Based Decision Support
  1. 9.1 Introduction
  2. 9.2 Visualization
  3. 9.3 What-If Analysis
  4. 9.4 Optimization
  5. 9.5 Conclusions
  6. References
6. Chapter 10: Advanced Process Control for Maximum Resource Efficiency
  1. 10.1 Introduction
  2. 10.2 The Importance of Constraint Control
  3. 10.3 What is Advanced Process Control?
  4. 10.4 Benefits and Requirements for Success
  5. 10.5 Requirements for success
  6. 10.6 Conclusion
  7. References
7. Chapter 11: Real-Time Optimization (RTO) Systems
  1. 11.1 Introduction
  2. 11.2 RTO Systems
  3. 11.3 Optimization Methods and Tools
  4. 11.4 Application Example: RTO in a Multiple-Effect Evaporation Process
  5. 11.5 Conclusions
  6. References
8. Chapter 12: Demand Side Response (DSR) for Improving Resource Efficiency beyond Single Plants
  1. 12.1 Executive Summary
  2. 12.2 Introduction
  3. 12.3 Structure of this Chapter
  4. 12.4 Motivation
  5. 12.5 Demand Side Response at Large Consumers
  6. 12.6 Valorization
  7. 12.7 Summary and Outlook
  8. References
9. Chapter 13: Energy Efficiency Improvement using STRUCTese™
  1. 13.1 Introduction
  2. References
Part III: Improving Resource Efficiency by Process Improvement
1. Chapter 14: Synthesis of Resource Optimal Chemical Processes
  1. 14.1 Introduction
  2. 14.2 Heuristic Methods
  3. 14.3 Superstructure Optimization Based Method
  4. 14.4 Other Impact Factors on Resource Optimal Chemical Processes
  5. 14.5 Conclusion
  6. References
2. Chapter 15: Optimization-Based Synthesis of Resource-Efficient Utility Systems
  1. 15.1 Introduction
  2. 15.2 Definition of Utility Systems
  3. 15.3 Problem Statement
  4. 15.4 Modelling
  5. 15.5 Solution Methods for Optimal Synthesis of Utility Systems
  6. 15.6 Analysis of Multiple Solutions for Decision Support
  7. 15.7 Industrial Case Study
  8. 15.8 Conclusions for the Utility System Synthesis in Industrial Practice
  9. Acknowledgments
  10. References
3. Chapter 16: A Perspective on Process Integration
  1. 16.1 Overview
  2. 16.2 Introduction
  3. 16.3 Heat Integration
  4. 16.4 Energy and Resource Integration
  5. 16.5 Summary
  6. References
4. Chapter 17: Industrial Symbiosis
  1. 17.1 Syn-Bios and Syn-Ergon
  2. 17.2 Industrial Symbiosis
  3. 17.3 Business Clustering
  4. 17.4 Conclusions
  5. References
Part IV: Company Culture for Resource Efficiency
1. Chapter 18: Organizational Culture for Resource Efficiency
  1. 18.1 Introduction
  2. 18.2 The Basics
  3. 18.3 Implementation
  4. 18.4 Giving It a Meaning
  5. 18.5 Closing Remarks
  6. Acknowledgments
  7. References
2. Index
End User License Agreement

List of Illustrations

Chapter 1: Energy and Resource Efficiency in the Process Industries
1. Figure 1.1 Improvement of production in general terms.
2. Figure 1.2 Qualitative visualization of the required effort and of the benefit of solutions for the improvement of process operations.
Chapter 2: Standards, Regulations and Requirements Concerning Energy and Resource Efficiency
1. Figure 2.1 Energy intensity of an economy as level of primary energy per GDP between 1990 and 2012 normalized on the year 2011 [23].
2. Figure 2.2 Timeline of EnMS standardization projects.
3. Figure 2.3 Timeline of the development of EN 16001 and ISO 50001.
4. Figure 2.4 Overview of current standardization activities around the ISO 5001 series.
5. Figure 2.5 The ISO 17743 and ISO 20366 families.
Chapter 4: Energy Efficiency Audits
1. Figure 4.1 Five-stage process.
2. Figure 4.2 Cooling water pump case study: piping and instrumentation diagram.
3. Figure 4.3 Pump cooling water load frequency distribution and pump characteristic curves.
4. Figure 4.4 Waterfall chart of pump losses.
Chapter 5: Real-Time Performance Indicators for Energy and Resource Efficiency in Continuous and Batch Processing
1. Figure 5.1 Definition of hierarchical resource managed units with REI attached.
2. Figure 5.2 Simple definition of a resource managed unit.
3. Figure 5.3 Evaluation of proposed REI using the RACER framework.
4. Figure 5.4 Aggregation hierarchy for production complexes.
5. Figure 5.5 Extension of a resource managed unit for aggregation purposes.
6. Figure 5.6 Graphical explanation of the different performance measurements.
7. Figure 5.7 Simple production complex consisting of two plants.
8. Figure 5.8 Extension of the aggregation concepts with a utility provider.
9. Figure 5.9 Sketch of the production complex for the illustrative example.
10. Figure 5.10 Change of energy and product flows and plant performance over time.
11. Figure 5.11 Performance of the complex and the corresponding baseline (a) and plant-performance-induced deviation from the baseline (b).
12. Figure 5.12 Visualization of load and performance contribution to the deviation from the baseline of a production complex.
13. Figure 5.13 Power plant performance and electricity production over time.
14. Figure 5.14 Performance and load contribution integrating the power plant within the balance domain.
15. Figure 5.15 Product-oriented REI for each product, PP outside the balancing domain.
16. Figure 5.16 Product-oriented REI for P2 and P4, with and without PP inside the balancing domain.
17. Figure 5.17 Simplified batch balance for REI calculation.
18. Figure 5.18 Batch distillation with removal of raw material residue $c05-math-230$ and excess water $c05-math-231$ .
19. Figure 5.19 Process structure with parallel production of two intermediates that are merged to the final batch.
20. Figure 5.20 Process for two products based on homogeneous splitting.
21. Figure 5.21 Membrane separation process, where both phases yield a product.
22. Figure 5.22 Buffer tank between batch and continuously operated section (a), timing of transfer operations to and from buffer tank (b).
23. Figure 5.23 Resource load $c05-math-322$ per product $c05-math-323$ in the buffer tank for different transfer times (solid line: 2 min/10 min, dotted line: 10 min/2 min) for the same two batches with different efficiencies.
Chapter 6: Sensing Technology
1. Figure 6.1 PAT as the source of information, artificial intelligence (or advanced computing) as the tool for taking optimal decisions towards data-driven actions to optimize productive actions in relation to the Industry 4.0 paradigm.
2. Figure 6.2 Typical structure of a hyperspectral imager.
3. Figure 6.3 A representation of a typical hypercube. Each plane is related to a specific wavelength, while the false colour gradation informs about the signal (reflectance or transmittance) intensity.
4. Figure 6.4 Raman effect takes place almost synchronously with the excitation pulse. However, for this example, the fluorescence signal reaches the maximum around 300 ps later and remains for a long.
5. Figure 6.5 Spectra of a calcite mineral sample with luminescent impurities. These impurities are often rare earths or transition metals embedded within the crystal. The associated photoluminescence is strong but relatively long-lived (µs–ms range) and therefore effectively rejected by time gating. Comparison between time-gated and CW 532 nm systems, and a CW 1064 nm system [21].
6. Figure 6.6 An affordable off-the-shelf miniaturized near infrared spectrophotometer that operates on the basis of a MEMS Fabry–Perot tuneable filter .
Chapter 7: Information Technology and Structuring of Information for Resource Efficiency Analysis and Real-Time Reporting
1. Figure 7.1 Functional hierarchy of production-oriented IT systems according to IEC 62264.
2. Figure 7.2 Resource managed unit.
3. Figure 7.3 From functional view to an abstract information model.
4. Figure 7.4 Three-tier architecture.
5. Figure 7.5 From data point-oriented systems to context-centred information management.
6. Figure 7.6 Typical workflow to develop comprehensive REI applications using a resource flow information model.
7. Figure 7.7 Step 1 – specifying a Type Model.
8. Figure 7.8 Step 2 – modelling a site- or plant-specific resource flow structure.
9. Figure 7.9 Step 3 – linking properties to RMUs and resource flows.
10. Figure 7.10 Production scheme of first example of an industrial installation.
11. Figure 7.11 Schematic overview of the monitored process part.
12. Figure 7.15 Dashboard of the REI case study example 1.
13. Figure 7.12 Block hierarchy of first example.
14. Figure 7.13 Steam flow with attached measurement property.
15. Figure 7.14 Steam flow type with attached property calculating the energy value.
16. Figure 7.16 Modern HMI elements.
17. Figure 7.17 Extract of the modelled site-wide resource flow model.
18. Figure 7.18 Extract of the Type Model of example 2.
19. Figure 7.19 Extract of the site-wide resource flow model of example 2.
20. Figure 7.20 Screenshot of the REI dashboard of example 2 (yield overview).
21. Figure 7.21 Screenshot of the REI dashboard example 2.
Chapter 8: Data Pre-treatment
1. Figure 8.1 The “control” pyramid.
2. Figure 8.2 Distribution of measurements around a certain value following a Gaussian distribution.
3. Figure 8.3 Flow measurements in a liquid junction.
4. Figure 8.4 A heat exchanger with temperature measurements.
5. Figure 8.5 Mixing of two liquid streams.
6. Figure 8.6 Mixing of two liquid streams with a different set of measurements.
7. Figure 8.7 EcosimPro window for selection of boundary variables (partition).
8. Figure 8.8 Non-Gaussian distribution of the measurements around the process variable.
9. Figure 8.9 Steps to be followed in statistical methods.
10. Figure 8.10 F-distribution and the corresponding critical value of 2.8588 for a level of significance of 5% and degrees of freedom 4 and 37.
11. Figure 8.11 Shapes of different cost functions: LS Least Squares, Fair function (c = 15) and Welch (c = 2.9846).
12. Figure 8.12 Process signal in a transient and moving average.
Chapter 9: REI-Based Decision Support
1. Figure 9.1 Visualization of different levels of decision support.
2. Figure 9.2 Structure of well-organized dashboard configurations for Western users.
3. Figure 9.3 Efficiency bars included into the plant structure diagram.
4. Figure 9.4 Sankey diagram for materials (a) and energy (b).
5. Figure 9.5 Sankey diagram with highlighted raw material feed A with a measured flow rate of 100 kg/min and a theoretical minimum of 80 kg/min to produce the same amount of product.
6. Figure 9.6 Bullet chart representation with current value, direction of movement, historical variability and relation to target value.
7. Figure 9.7 Stacked area plot.
8. Figure 9.8 Stacked bar chart for batch applications.
9. Figure 9.9 Sparklines for three REI in different setups.
10. Figure 9.10 Difference chart to reference: light areas are losses compared to reference, and dark areas are gains compared to reference.
11. Figure 9.11 Aggregated tile plot with colour according to the efficiency.
12. Figure 9.12 Flow sheet of the sugar plant for the production of food-grade sugar from sugar beets.
13. Figure 9.13 Dashboard concept for the sugar plant application case.
14. Figure 9.14 Sequence of a what-if analysis.
15. Figure 9.15 Cooling tower array as an example for the what-if analysis approach.
16. Figure 9.16 Exemplary results of the cooling tower example.
17. Figure 9.17 Distillation column as an example of multicriteria optimization.
18. Figure 9.18 Representations of a three-dimensional Pareto front.
19. Figure 9.19 Example process for the multicriteria optimization of resource efficiency.
20. Figure 9.20 Multicriterial optimization results of the combined cooling towers and the extractive distillation process.
Chapter 10: Advanced Process Control for Maximum Resource Efficiency
1. Figure 10.1 Simple distillation column.
2. Figure 10.2 Graphical representation of multiple process constraints.
3. Figure 10.4 Dynamic model matrix for the depropanizer column.
4. Figure 10.3 Levels of process control and optimization.
5. Figure 10.5 Block diagram for model predictive control.
6. Figure 10.6 Simplified process flow diagram of the NMP butadiene extraction process.
7. Figure 10.7 Improved control of impurities in a 1,3-butadiene product.
8. Figure 10.8 Benefit estimation from reduced distillation column fractionation.
Chapter 11: Real-Time Optimization (RTO) Systems
1. Figure 11.1 Diagram of a typical DCS and information system.
2. Figure 11.2 Basic concepts in MPC. Evolution of one controlled variable (PV) computed with the model as function of the values of a manipulated variable (MV).
3. Figure 11.3 Improving control allows moving the set point to a better operating point.
4. Figure 11.4 (a) The economic optimizer fixes the SP of the MPC controller and the final resting values of the MVs. (b) Hierarchical structure of the control and optimization layers in a MPC controller.
5. Figure 11.5 A CSTR where the reaction A → B takes place, showing a RTO module that generates optimal set points for the MPC. The MPC commands the process acting on the set points of two flow control loops of the basic control layer.
6. Figure 11.6 Scheme of a typical RTO application showing the main elements involved.
7. Figure 11.7 Two iterations of the Newton–Raphson method for solving f(x) = 0.
8. Figure 11.8 Scheme of an evaporation plant with standard instrumentation.
9. Figure 11.9 Schema of a cooling tower.
10. Figure 11.10 Identified patterns with experimental data. (a) Flow coefficient. (b) Tower performance.
11. Figure 11.11 Evolution of the main input and output variables during a year of operation. (a) Evaporated water and (b) steam consumption.
12. Figure 11.12 Evolution of the resource efficiency indicators. (a) REI₁. (b) REI₂.
Chapter 12: Demand Side Response (DSR) for Improving Resource Efficiency beyond Single Plants
1. Figure 12.1 Transformation of the Grid, source ABB.
2. Figure 12.2 History of demand side response [7–13].
3. Figure 12.3 Impact of energy efficiency (constant improvement) related to a fixed plan (Gantt chart).
4. Figure 12.4 Energy systems and production planning must be better coordinated.
5. Figure 12.5 Energy portfolio example from the steel industry.
6. Figure 12.6 Various load management strategies: No load management, peak shaving, load shedding and load shifting.
7. Figure 12.7 Illustrative consumer surplus approach according to [10].
8. Figure 12.8 Production schedules not considering (a) and considering (b) electricity prices.
Chapter 13: Energy Efficiency Improvement using STRUCTese™
1. Figure 13.1 Closing the awareness gap is a challenge.
2. Figure 13.2 Simple monitoring with lack of transparency.
3. Figure 13.3 Energy Management Cycle: STRUCTese^TM follows a PDCA-cycle in accordance with the standards for EnMS.
4. Figure 13.4 STRUCTese^TM workflow: Energy Efficiency Check and Energy Efficiency Management.
5. Figure 13.5 STRUCTese^TM energy scope.
6. Figure 13.6 Typical results of the analysis phase – energy distribution of the plant (a) and main steam consumers (b).
7. Figure 13.7 Optimization levels considered in the Energy Efficiency Check .
8. Figure 13.8 Typical energy savings portfolio.
9. Figure 13.9 Energy loss cascade (ISBL: inside battery limits, OSBL: outside battery limits).
10. Figure 13.10 Concept of best demonstrated practice, consideration of suboptimal operation and partial load.
11. Figure 13.11 Development energy efficiency over time.
12. Figure 13.12 Integrated energy efficiency management tool addressing all levels of the organization.
13. Figure 13.13 Reduction of specific energy consumption for a polymer plant over several years.
14. Figure 13.14 Steam loss cascades from 2006 to 2011 for a polymer plant.
15. Figure 13.15 Exemplary energy efficiency online monitor – several energy levels, best demonstrated practice and loss codes are shown for the main utilities.
16. Figure 13.16 Exemplary daily energy protocol.
17. Figure 13.17 Online monitor.
18. Figure 13.18 Development of GHG emissions and energy consumption.
Chapter 14: Synthesis of Resource Optimal Chemical Processes
1. Figure 14.1 Converting natural resources to various products.
2. Figure 14.2 Growth rates of global consumptions of fossil fuels from 2004 to 2014 [3].
3. Figure 14.3 Illustration of pinch technology targeting utility consumption: (a) hot and cold composite curves; (b) grand composite curve.
4. Figure 14.4 Illustrative mass flows of a mixer, a separator and a reactor.
5. Figure 14.5 Illustrative heat exchange between a cold stream and a hot stream.
6. Figure 14.6 Summary of cost components of a chemical plant. TBM, total bare module cost; DPI, total direct permanent investment; TDC, total depreciable capital; TPI, total permanent investment; TCI, total investment; DMC, direct manufacturing costs; COM, cost of manufacture; FC, fixed costs; TPC, total production cost.
Chapter 15: Optimization-Based Synthesis of Resource-Efficient Utility Systems
1. Figure 15.1 Workflow of a project for the optimization of utility systems. The steps of this work flow are described in the sections of this chapter (section numbers in brackets).
2. Figure 15.2 A chemical site is composed of a process system and a utility system. The utility system is connected to the public energy market and to the process system, and provides the final energy for the process system.
3. Figure 15.3 Levels of decision in optimization of utility systems: Structure, sizing and operation.
4. Figure 15.4 Graphical illustration of the trade-off between modelling complexity and decision levels with increasing computational effort. The trade-off is abstractly visualized (dotted line); however, no strict mathematical relationship exists.
5. Figure 15.5 Pattern of non-zero coefficients in the aggregated matrix of the matrices $c015-math-010$ for MILP problems: “simple problem” (a), “complicating variables” (b) or “complicating constraints” (c).
6. Figure 15.6 Accuracy measure (▵TAC) for time-series aggregation in the domain of the objective function, as difference in total annual cost (TAC) between an aggregated synthesis problem (b) and an operation problem (c). The large original synthesis problem (a) is unsolvable or requires high computational effort.
7. Figure 15.7 Flow diagram of the algorithm for automated superstructure and model generation. Voll et al. 2013 [15].
8. Figure 15.8 Flow diagram of the successive algorithm for automated superstructure generation and optimization of utility systems' synthesis problems. Voll et al. 2013 [15].
9. Figure 15.9 Flow diagram of an evolutionary algorithm.
10. Figure 15.10 Energy conversion hierarchy.
11. Figure 15.11 Flowchart representing the algorithm for generation of rational decision options.
12. Figure 15.12 Schematic plant layout of the industrial case study. The plant is divided by a public road in two sites. Site B is not connected to the existing cooling network on Site A.
13. Figure 15.13 Monthly averaged demand profiles for electricity, heating and cooling (stacked bar chart). Additionally, peak loads for winter (PW) and summer (PS) are considered.
14. Figure 15.14 Optimal solution of the industrial case study, $c015-math-018$ . Selected equipment (white) and spare equipment (dashed) of the superstructure built by the successive superstructure expansion. The electricity demand is not shown. B, Boiler; CHP, CHP engine; CC, Compression chiller; AC, Absorption chiller; H, Heating demand; C, Cooling demand.
15. Figure 15.15 Pareto front regarding total investments and cumulated energy demand (CED). The Pareto-optimal solutions are clustered in five groups of similar structure (I – V). Additionally, the net-present-value-(NPV)-optimal solution is shown.
16. Figure 15.16 10th best solution of the industrial case study, $c015-math-019$ . Selected equipment (white) and spare equipment (dashed) of the superstructure built by the successive superstructure expansion. The electricity demand is not shown. B, Boiler; CHP, CHP engine; CC, compression chiller; AC, absorption chiller; H, heating demand; C, cooling demand.
17. Figure 15.17 Selected near-optimal solutions of the industrial case study: (a) 2nd best solution, (b) 3rd best solution, (c) 7th best solution, (d) 10th best solution. The x-axis shows the sizing of the equipment. On the y-axis, the equipment is listed sorted by technology. B: Boiler, CHP: CHP engine, CC: compression chiller, AC: absorption chiller, E: existing equipment, N: new equipment.
Chapter 16: A Perspective on Process Integration
1. Figure 16.1 The process energy system.
2. Figure 16.2 The process unit operation.
3. Figure 16.3 One hot and one cold stream in counter-current heat exchange.
4. Figure 16.4 Hot and cold composite curves with utility requirements and heat recovery potential.
5. Figure 16.5 Grand composite curve (GCC) for the example composite curves.
6. Figure 16.6 Composite and Grand Composite Curves of the process after heat integration.
7. Figure 16.7 Composite Curves after improvement potentials.
8. Figure 16.8 Principles of MVR and heat pumping.
9. Figure 16.9 Energy integration representation of rotary steam cooker, exhibiting the interaction of resources, heat, electricity and support materials.
10. Figure 16.10 Pareto frontier showing potential solutions having an economic and environmental trade-off.
11. Figure 16.11 Summary of SPI and TSI for the three process subsystems of the site.
12. Figure 16.12 Composite curves of process system A from black-box to simple-model analysis with SPI (100% corresponds to the present total site consumption).
13. Figure 16.13 Composite curves of process system B from black-box to detailed-model analysis with SPI (100% corresponds to the present total site consumption).
14. Figure 16.14 Composite curves of process system C with black-box analysis (100% corresponds to the present total site consumption).
15. Figure 16.15 Systematic improvement of CC and GCC of the total site by combining different energy requirement levels (100% corresponds to the present total site consumption).
16. Figure 16.16 TSI for the process systems with and without pressure modification.
17. Figure 16.17 TSI for the process systems with and without MVR or heat pumps [6].
18. Figure 16.18 Site utility integration and optimization for three different scenarios.
Chapter 17: Industrial Symbiosis
1. Figure 17.1 Individual companies join forces to manage and grow cluster activities.
2. Figure 17.2 A wide range of cluster activities benefitting from umbrella management.
3. Figure 17.3 Book series on eco-industrial parks, from a legal, economic, spatial and technical perspective.
4. Figure 17.4 District networks using process waste heat, case Kuurne Flanders (Belgium).
5. Figure 17.5 Cluster activities driving sustainability management.
6. Figure 17.6 Integration of the sharing and the caring economy.
7. Figure 17.7 Industrial competitiveness and corporate responsibility as pillars of the Europe 2020 strategy.
8. Figure 17.8 Corporate sustainability at the heart of the People–Planet–Profit triangle.
9. Figure 17.9 Cluster activities driving responsibility management.
10. Figure 17.10 Indicative energy system superstructure and technology measures for low-carbon business parks.
11. Figure 17.11 Symbiosis in industry goes by many names.
12. Figure 17.12 Textbook example of eco-industrial parks: Kalundborg (Denmark).
13. Figure 17.13 Petrochemistry symbiosis with horticulture, OCAP case Port of Rotterdam (Netherlands).
14. Figure 17.14 Second life platform for waste and resources, case International Synergies & NISP (UK).
15. Figure 17.15 Flemish decree on carbon neutrality for business parks.
16. Figure 17.16 From corporate over concerted towards circular responsibilities in decade strides.
17. Figure 17.17 LESTS pentagon to visualise inter-firm collaboration.
18. Figure 17.18 Steel and petrochemical symbiosis: case Marseille (France).
19. Figure 17.19 Triple helix, university–industry–government collaboration for a sustainable society.
20. Figure 17.20 SET Plan to advance development and deployment of low-carbon technologies.
21. Figure 17.21 Business improvement district, case Technology Park Ghent (Belgium) and Wase Wind cooperative, Flanders (Belgium).
22. Figure 17.22 Energy integration at petrochemical site level, case Lavera (France).
23. Figure 17.23 Energy integration at the chemical cluster level, case Stenungsund (Sweden).
24. Figure 17.24 Outline of the circular economy .

List of Tables

Chapter 2: Standards, Regulations and Requirements Concerning Energy and Resource Efficiency
1. Table 2.1 Success stories of energy checks and EnMS
Chapter 3: Energy and Resource Efficiency Reporting
1. Table 3.1 Aspects of the environmental category in GRI guidelines [6]
2. Table 3.2 GRI indicators related to resource efficiency [6]
3. Table 3.3 Sector-specific GRI indicators related to resource efficiency [17, 18]
4. Table 3.4 The reporting requirements set in ISO 14000 standards relevant to resource efficiency
Chapter 4: Energy Efficiency Audits
1. Table 4.1 Case study calculated power consumption
2. Table 4.2 Example of life-cycle cost saving potentials
3. Table 4.3 Comparison of evaluated power saving potentials
Chapter 5: Real-Time Performance Indicators for Energy and Resource Efficiency in Continuous and Batch Processing
1. Table 5.1 List of generic indicators for continuous processes
2. Table 5.2 Influenceable and non-influenceable factors in chemical plants
3. Table 5.3 Values for the analysis of the plant complex in Figure 5.7
Chapter 6: Sensing Technology
1. Table 6.1 Summary of the novel sensing technologies covered in this chapter
Chapter 8: Data Pre-treatment
1. Table 8.1 Measured values of the mixing streams example
2. Table 8.2 Measured and reconciled values of the mixing streams example
3. Table 8.3 Measured and reconciled values of the mixing streams example with leakage
4. Table 8.4 Measured and reconciled values of the mixing streams example with a gross error
5. Table 8.5 Sets of process measurements free from gross errors/reconciled values
6. Table 8.6 Residuals and their corresponding normalized value.
7. Table 8.7 Covariance matrix of the normalized residuals of the data sets free from gross errors
8. Table 8.8 Results of the data reconciliation after removal of the term with gross error in the cost function
9. Table 8.9 Results of the data reconciliation with a gross error using M-estimators
Chapter 9: REI-Based Decision Support
1. Table 9.1 Comprehensive overview of the visualization methods introduced
Chapter 10: Advanced Process Control for Maximum Resource Efficiency
1. Table 10.1 Steady-state model gains for a depropanizer
Chapter 12: Demand Side Response (DSR) for Improving Resource Efficiency beyond Single Plants
1. Table 12.1 Barriers and drivers
Chapter 14: Synthesis of Resource Optimal Chemical Processes
1. Table 14.1 Example of process steps for property difference
Chapter 16: A Perspective on Process Integration
1. Table 16.1 Multi-Level energy requirement definition [6].
2. Table 16.2 Summary of comparisons between SPI and TSI
3. Table 16.3 Summary of site utility integration for three scenarios

Resource Efficiency of Processing Plants

Monitoring and Improvement

Edited by Stefan Krämer and Sebastian Engell

The Editors

Dr.-Ing. Stefan Krämer

INEOS Köln GmbH

Alte Str. 201

50769 Köln

Germany

Prof. Dr.-Ing. Sebastian Engell

Technische Universität Dortmund

Department of Biochemical and Chemical Engineering

Process Dynamics and Operations Group

Emil-Figge-Str. 70

44221 Dortmund

Germany

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INEOS in Köln, Oliver Brenneisen

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Print ISBN: 978-3-527-34074-3

ePDF ISBN: 978-3-527-80414-6

ePub ISBN: 978-3-527-80416-0

Mobi ISBN: 978-3-527-80417-7

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