Table of Contents
Cover
Title Page
Copyright
Preface
Part I: Resource Efficiency Metrics and Standardised Management Systems
Chapter 1: Energy and Resource Efficiency in the Process Industries
1.1 Introduction
1.2 Energy and Resources
1.3 Energy and Resource Efficiency
1.4 Evaluation of Energy and Resource Efficiency
1.5 Evaluation of Energy and Resource Efficiency in Real Time
1.6 The Chemical and Process Industry
1.7 Recent and Potential Improvements in Energy and Resource Consumption of the Chemical and Process Industries
1.8 What Can Be Done to Further Improve the Resource Efficiency of the Process Industry?
1.9 Conclusions
References
Chapter 2: Standards, Regulations and Requirements Concerning Energy and Resource Efficiency
2.1 Introducing a Long-Term Development
2.2 Normative Approaches on Energy and Resource Efficiency
2.3 Achievements of Energy and Resource Management
2.4 Conclusion
References
Chapter 3: Energy and Resource Efficiency Reporting
3.1 Executive Summary
3.2 Introduction
3.3 Obligatory Reporting Mechanisms
3.4 Voluntary Reporting Mechanisms
3.5 Other Reporting Mechanisms
3.6 Summary of the Energy and Resource Efficiency Reporting Requirements
References
Chapter 4: Energy Efficiency Audits
4.1 Introduction
4.2 Stage 1: Current Energy Status
4.3 Stage 2: Basic Analysis
4.4 Stage 3: Detailed Analysis and Collection of Ideas
4.5 Stage 4: Evaluation and Selection of Measures
4.6 Stage 5: Realization and Monitoring
4.7 Extension to Resource Efficiency
4.8 Closing Remark
References
Part II: Monitoring and Improvement of the Resource Efficiency through Improved Process Operations
Chapter 5: Real-Time Performance Indicators for Energy and Resource Efficiency in Continuous and Batch Processing
5.1 Introduction
5.2 Real-Time Resource Efficiency Indicators
5.3 Evaluation of the Suitability of Resource Efficiency Indicators
5.4 Hierarchical Modelling of Continuous Production Complexes
5.5 Batch Production
5.6 Integrated Batch and Continuous Production
5.7 Conclusions
Appendix: Decomposition of
References
Chapter 6: Sensing Technology
6.1 Introduction
6.2 Sensing: General Considerations and Challenges
6.3 Energy Saving by Means of Accurate Metering
6.4 Latest Advancements in Spectroscopy Technology for Process-Monitoring-Based Efficiency
6.5 Process Analytical Technologies (PAT)
6.6 Soft Sensors. Access to the “Truth” Distributed Among a Plurality of Simple Sensors
6.7 MEMS-Based Sensors. Smart Sensors
6.8 Future Trends in Sensing with Promising Impact on Reliable Process Monitoring
6.9 European R&D: Driving Forward Sensing Advancements
6.10 Conclusion
References
Chapter 7: Information Technology and Structuring of Information for Resource Efficiency Analysis and Real-Time Reporting
7.1 Introduction
7.2 Information Technology in the Process Industries
7.3 Resource Flow Modelling and Structuring of Information
7.4 From Formulae to Runtime Software
7.5 Industrial Installations
7.6 Summary and Conclusions
References
Chapter 8: Data Pre-treatment
8.1 Measurement Errors and Variable Estimation
8.2 Data Reconciliation
8.3 Gross Errors Detection and Removal
8.4 Data Pre-treatment and Steady-State Detection
8.5 Dynamic Data Reconciliation
8.6 Conclusions
References
Chapter 9: REI-Based Decision Support
9.1 Introduction
9.2 Visualization
9.3 What-If Analysis
9.4 Optimization
9.5 Conclusions
References
Chapter 10: Advanced Process Control for Maximum Resource Efficiency
10.1 Introduction
10.2 The Importance of Constraint Control
10.3 What is Advanced Process Control?
10.4 Benefits and Requirements for Success
10.5 Requirements for success
10.6 Conclusion
References
Chapter 11: Real-Time Optimization (RTO) Systems
11.1 Introduction
11.2 RTO Systems
11.3 Optimization Methods and Tools
11.4 Application Example: RTO in a Multiple-Effect Evaporation Process
11.5 Conclusions
References
Chapter 12: Demand Side Response (DSR) for Improving Resource Efficiency beyond Single Plants
12.1 Executive Summary
12.2 Introduction
12.3 Structure of this Chapter
12.4 Motivation
12.5 Demand Side Response at Large Consumers
12.6 Valorization
12.7 Summary and Outlook
References
Chapter 13: Energy Efficiency Improvement using STRUCTese™
13.1 Introduction
References
Part III: Improving Resource Efficiency by Process Improvement
Chapter 14: Synthesis of Resource Optimal Chemical Processes
14.1 Introduction
14.2 Heuristic Methods
14.3 Superstructure Optimization Based Method
14.4 Other Impact Factors on Resource Optimal Chemical Processes
14.5 Conclusion
References
Chapter 15: Optimization-Based Synthesis of Resource-Efficient Utility Systems
15.1 Introduction
15.2 Definition of Utility Systems
15.3 Problem Statement
15.4 Modelling
15.5 Solution Methods for Optimal Synthesis of Utility Systems
15.6 Analysis of Multiple Solutions for Decision Support
15.7 Industrial Case Study
15.8 Conclusions for the Utility System Synthesis in Industrial Practice
Acknowledgments
References
Chapter 16: A Perspective on Process Integration
16.1 Overview
16.2 Introduction
16.3 Heat Integration
16.4 Energy and Resource Integration
16.5 Summary
References
Chapter 17: Industrial Symbiosis
17.1 Syn-Bios and Syn-Ergon
17.2 Industrial Symbiosis
17.3 Business Clustering
17.4 Conclusions
References
Part IV: Company Culture for Resource Efficiency
Chapter 18: Organizational Culture for Resource Efficiency
18.1 Introduction
18.2 The Basics
18.3 Implementation
18.4 Giving It a Meaning
18.5 Closing Remarks
Acknowledgments
References
Index
End User License Agreement
Pages
xvii
xviii
xix
xx
xxi
xxii
xxiii
xxiv
1
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
345
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
471
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
495
496
497
498
499
500
501
502
503
504
Guide
Cover
Table of Contents
Preface
Part I: Resource Efficiency Metrics and Standardised Management Systems
Begin Reading
List of Illustrations
Chapter 1: Energy and Resource Efficiency in the Process Industries
Figure 1.1 Improvement of production in general terms.
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
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].
Figure 2.2 Timeline of EnMS standardization projects.
Figure 2.3 Timeline of the development of EN 16001 and ISO 50001.
Figure 2.4 Overview of current standardization activities around the ISO 5001 series.
Figure 2.5 The ISO 17743 and ISO 20366 families.
Chapter 4: Energy Efficiency Audits
Figure 4.1 Five-stage process.
Figure 4.2 Cooling water pump case study: piping and instrumentation diagram.
Figure 4.3 Pump cooling water load frequency distribution and pump characteristic curves.
Figure 4.4 Waterfall chart of pump losses.
Chapter 5: Real-Time Performance Indicators for Energy and Resource Efficiency in Continuous and Batch Processing
Figure 5.1 Definition of hierarchical resource managed units with REI attached.
Figure 5.2 Simple definition of a resource managed unit.
Figure 5.3 Evaluation of proposed REI using the RACER framework.
Figure 5.4 Aggregation hierarchy for production complexes.
Figure 5.5 Extension of a resource managed unit for aggregation purposes.
Figure 5.6 Graphical explanation of the different performance measurements.
Figure 5.7 Simple production complex consisting of two plants.
Figure 5.8 Extension of the aggregation concepts with a utility provider.
Figure 5.9 Sketch of the production complex for the illustrative example.
Figure 5.10 Change of energy and product flows and plant performance over time.
Figure 5.11 Performance of the complex and the corresponding baseline (a) and plant-performance-induced deviation from the baseline (b).
Figure 5.12 Visualization of load and performance contribution to the deviation from the baseline of a production complex.
Figure 5.13 Power plant performance and electricity production over time.
Figure 5.14 Performance and load contribution integrating the power plant within the balance domain.
Figure 5.15 Product-oriented REI for each product, PP outside the balancing domain.
Figure 5.16 Product-oriented REI for P2 and P4, with and without PP inside the balancing domain.
Figure 5.17 Simplified batch balance for REI calculation.
Figure 5.18 Batch distillation with removal of raw material residue and excess water .
Figure 5.19 Process structure with parallel production of two intermediates that are merged to the final batch.
Figure 5.20 Process for two products based on homogeneous splitting.
Figure 5.21 Membrane separation process, where both phases yield a product.
Figure 5.22 Buffer tank between batch and continuously operated section (a), timing of transfer operations to and from buffer tank (b).
Figure 5.23 Resource load per product 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
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.
Figure 6.2 Typical structure of a hyperspectral imager.
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.
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.
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].
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
Figure 7.1 Functional hierarchy of production-oriented IT systems according to IEC 62264.
Figure 7.2 Resource managed unit.
Figure 7.3 From functional view to an abstract information model.
Figure 7.4 Three-tier architecture.
Figure 7.5 From data point-oriented systems to context-centred information management.
Figure 7.6 Typical workflow to develop comprehensive REI applications using a resource flow information model.
Figure 7.7 Step 1 – specifying a Type Model.
Figure 7.8 Step 2 – modelling a site- or plant-specific resource flow structure.
Figure 7.9 Step 3 – linking properties to RMUs and resource flows.
Figure 7.10 Production scheme of first example of an industrial installation.
Figure 7.11 Schematic overview of the monitored process part.
Figure 7.15 Dashboard of the REI case study example 1.
Figure 7.12 Block hierarchy of first example.
Figure 7.13 Steam flow with attached measurement property.
Figure 7.14 Steam flow type with attached property calculating the energy value.
Figure 7.16 Modern HMI elements.
Figure 7.17 Extract of the modelled site-wide resource flow model.
Figure 7.18 Extract of the Type Model of example 2.
Figure 7.19 Extract of the site-wide resource flow model of example 2.
Figure 7.20 Screenshot of the REI dashboard of example 2 (yield overview).
Figure 7.21 Screenshot of the REI dashboard example 2.
Chapter 8: Data Pre-treatment
Figure 8.1 The “control” pyramid.
Figure 8.2 Distribution of measurements around a certain value following a Gaussian distribution.
Figure 8.3 Flow measurements in a liquid junction.
Figure 8.4 A heat exchanger with temperature measurements.
Figure 8.5 Mixing of two liquid streams.
Figure 8.6 Mixing of two liquid streams with a different set of measurements.
Figure 8.7 EcosimPro window for selection of boundary variables (partition).
Figure 8.8 Non-Gaussian distribution of the measurements around the process variable.
Figure 8.9 Steps to be followed in statistical methods.
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.
Figure 8.11 Shapes of different cost functions: LS Least Squares, Fair function (c = 15) and Welch (c = 2.9846).
Figure 8.12 Process signal in a transient and moving average.
Chapter 9: REI-Based Decision Support
Figure 9.1 Visualization of different levels of decision support.
Figure 9.2 Structure of well-organized dashboard configurations for Western users.
Figure 9.3 Efficiency bars included into the plant structure diagram.
Figure 9.4 Sankey diagram for materials (a) and energy (b).
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.
Figure 9.6 Bullet chart representation with current value, direction of movement, historical variability and relation to target value.
Figure 9.7 Stacked area plot.
Figure 9.8 Stacked bar chart for batch applications.
Figure 9.9 Sparklines for three REI in different setups.
Figure 9.10 Difference chart to reference: light areas are losses compared to reference, and dark areas are gains compared to reference.
Figure 9.11 Aggregated tile plot with colour according to the efficiency.
Figure 9.12 Flow sheet of the sugar plant for the production of food-grade sugar from sugar beets.
Figure 9.13 Dashboard concept for the sugar plant application case.
Figure 9.14 Sequence of a what-if analysis.
Figure 9.15 Cooling tower array as an example for the what-if analysis approach.
Figure 9.16 Exemplary results of the cooling tower example.
Figure 9.17 Distillation column as an example of multicriteria optimization.
Figure 9.18 Representations of a three-dimensional Pareto front.
Figure 9.19 Example process for the multicriteria optimization of resource efficiency.
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
Figure 10.1 Simple distillation column.
Figure 10.2 Graphical representation of multiple process constraints.
Figure 10.4 Dynamic model matrix for the depropanizer column.
Figure 10.3 Levels of process control and optimization.
Figure 10.5 Block diagram for model predictive control.
Figure 10.6 Simplified process flow diagram of the NMP butadiene extraction process.
Figure 10.7 Improved control of impurities in a 1,3-butadiene product.
Figure 10.8 Benefit estimation from reduced distillation column fractionation.
Chapter 11: Real-Time Optimization (RTO) Systems
Figure 11.1 Diagram of a typical DCS and information system.
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).
Figure 11.3 Improving control allows moving the set point to a better operating point.
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.
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.
Figure 11.6 Scheme of a typical RTO application showing the main elements involved.
Figure 11.7 Two iterations of the Newton–Raphson method for solving f (x ) = 0.
Figure 11.8 Scheme of an evaporation plant with standard instrumentation.
Figure 11.9 Schema of a cooling tower.
Figure 11.10 Identified patterns with experimental data. (a) Flow coefficient. (b) Tower performance.
Figure 11.11 Evolution of the main input and output variables during a year of operation. (a) Evaporated water and (b) steam consumption.
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
Figure 12.1 Transformation of the Grid, source ABB.
Figure 12.2 History of demand side response [7–13].
Figure 12.3 Impact of energy efficiency (constant improvement) related to a fixed plan (Gantt chart).
Figure 12.4 Energy systems and production planning must be better coordinated.
Figure 12.5 Energy portfolio example from the steel industry.
Figure 12.6 Various load management strategies: No load management, peak shaving, load shedding and load shifting.
Figure 12.7 Illustrative consumer surplus approach according to [10].
Figure 12.8 Production schedules not considering (a) and considering (b) electricity prices.
Chapter 13: Energy Efficiency Improvement using STRUCTese™
Figure 13.1 Closing the awareness gap is a challenge.
Figure 13.2 Simple monitoring with lack of transparency.
Figure 13.3 Energy Management Cycle: STRUCTeseTM follows a PDCA-cycle in accordance with the standards for EnMS.
Figure 13.4 STRUCTeseTM workflow: Energy Efficiency Check and Energy Efficiency Management.
Figure 13.5 STRUCTeseTM energy scope.
Figure 13.6 Typical results of the analysis phase – energy distribution of the plant (a) and main steam consumers (b).
Figure 13.7 Optimization levels considered in the Energy Efficiency Check .
Figure 13.8 Typical energy savings portfolio.
Figure 13.9 Energy loss cascade (ISBL: inside battery limits, OSBL: outside battery limits).
Figure 13.10 Concept of best demonstrated practice, consideration of suboptimal operation and partial load.
Figure 13.11 Development energy efficiency over time.
Figure 13.12 Integrated energy efficiency management tool addressing all levels of the organization.
Figure 13.13 Reduction of specific energy consumption for a polymer plant over several years.
Figure 13.14 Steam loss cascades from 2006 to 2011 for a polymer plant.
Figure 13.15 Exemplary energy efficiency online monitor – several energy levels, best demonstrated practice and loss codes are shown for the main utilities.
Figure 13.16 Exemplary daily energy protocol.
Figure 13.17 Online monitor.
Figure 13.18 Development of GHG emissions and energy consumption.
Chapter 14: Synthesis of Resource Optimal Chemical Processes
Figure 14.1 Converting natural resources to various products.
Figure 14.2 Growth rates of global consumptions of fossil fuels from 2004 to 2014 [3].
Figure 14.3 Illustration of pinch technology targeting utility consumption: (a) hot and cold composite curves; (b) grand composite curve.
Figure 14.4 Illustrative mass flows of a mixer, a separator and a reactor.
Figure 14.5 Illustrative heat exchange between a cold stream and a hot stream.
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
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).
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.
Figure 15.3 Levels of decision in optimization of utility systems: Structure, sizing and operation.
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.
Figure 15.5 Pattern of non-zero coefficients in the aggregated matrix of the matrices for MILP problems: “simple problem” (a), “complicating variables” (b) or “complicating constraints” (c).
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.
Figure 15.7 Flow diagram of the algorithm for automated superstructure and model generation. Voll et al . 2013 [15].
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].
Figure 15.9 Flow diagram of an evolutionary algorithm.
Figure 15.10 Energy conversion hierarchy.
Figure 15.11 Flowchart representing the algorithm for generation of rational decision options.
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.
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.
Figure 15.14 Optimal solution of the industrial case study, . 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.
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.
Figure 15.16 10th best solution of the industrial case study, . 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.
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
Figure 16.1 The process energy system.
Figure 16.2 The process unit operation.
Figure 16.3 One hot and one cold stream in counter-current heat exchange.
Figure 16.4 Hot and cold composite curves with utility requirements and heat recovery potential.
Figure 16.5 Grand composite curve (GCC) for the example composite curves.
Figure 16.6 Composite and Grand Composite Curves of the process after heat integration.
Figure 16.7 Composite Curves after improvement potentials.
Figure 16.8 Principles of MVR and heat pumping.
Figure 16.9 Energy integration representation of rotary steam cooker, exhibiting the interaction of resources, heat, electricity and support materials.
Figure 16.10 Pareto frontier showing potential solutions having an economic and environmental trade-off.
Figure 16.11 Summary of SPI and TSI for the three process subsystems of the site.
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).
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).
Figure 16.14 Composite curves of process system C with black-box analysis (100% corresponds to the present total site consumption).
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).
Figure 16.16 TSI for the process systems with and without pressure modification.
Figure 16.17 TSI for the process systems with and without MVR or heat pumps [6].
Figure 16.18 Site utility integration and optimization for three different scenarios.
Chapter 17: Industrial Symbiosis
Figure 17.1 Individual companies join forces to manage and grow cluster activities.
Figure 17.2 A wide range of cluster activities benefitting from umbrella management.
Figure 17.3 Book series on eco-industrial parks, from a legal, economic, spatial and technical perspective.
Figure 17.4 District networks using process waste heat, case Kuurne Flanders (Belgium).
Figure 17.5 Cluster activities driving sustainability management.
Figure 17.6 Integration of the sharing and the caring economy.
Figure 17.7 Industrial competitiveness and corporate responsibility as pillars of the Europe 2020 strategy.
Figure 17.8 Corporate sustainability at the heart of the People–Planet–Profit triangle.
Figure 17.9 Cluster activities driving responsibility management.
Figure 17.10 Indicative energy system superstructure and technology measures for low-carbon business parks.
Figure 17.11 Symbiosis in industry goes by many names.
Figure 17.12 Textbook example of eco-industrial parks: Kalundborg (Denmark).
Figure 17.13 Petrochemistry symbiosis with horticulture, OCAP case Port of Rotterdam (Netherlands).
Figure 17.14 Second life platform for waste and resources, case International Synergies & NISP (UK).
Figure 17.15 Flemish decree on carbon neutrality for business parks.
Figure 17.16 From corporate over concerted towards circular responsibilities in decade strides.
Figure 17.17 LESTS pentagon to visualise inter-firm collaboration.
Figure 17.18 Steel and petrochemical symbiosis: case Marseille (France).
Figure 17.19 Triple helix, university–industry–government collaboration for a sustainable society.
Figure 17.20 SET Plan to advance development and deployment of low-carbon technologies.
Figure 17.21 Business improvement district, case Technology Park Ghent (Belgium) and Wase Wind cooperative, Flanders (Belgium).
Figure 17.22 Energy integration at petrochemical site level, case Lavera (France).
Figure 17.23 Energy integration at the chemical cluster level, case Stenungsund (Sweden).
Figure 17.24 Outline of the circular economy .
List of Tables
Chapter 2: Standards, Regulations and Requirements Concerning Energy and Resource Efficiency
Table 2.1 Success stories of energy checks and EnMS
Chapter 3: Energy and Resource Efficiency Reporting
Table 3.1 Aspects of the environmental category in GRI guidelines [6]
Table 3.2 GRI indicators related to resource efficiency [6]
Table 3.3 Sector-specific GRI indicators related to resource efficiency [17, 18]
Table 3.4 The reporting requirements set in ISO 14000 standards relevant to resource efficiency
Chapter 4: Energy Efficiency Audits
Table 4.1 Case study calculated power consumption
Table 4.2 Example of life-cycle cost saving potentials
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
Table 5.1 List of generic indicators for continuous processes
Table 5.2 Influenceable and non-influenceable factors in chemical plants
Table 5.3 Values for the analysis of the plant complex in Figure 5.7
Chapter 6: Sensing Technology
Table 6.1 Summary of the novel sensing technologies covered in this chapter
Chapter 8: Data Pre-treatment
Table 8.1 Measured values of the mixing streams example
Table 8.2 Measured and reconciled values of the mixing streams example
Table 8.3 Measured and reconciled values of the mixing streams example with leakage
Table 8.4 Measured and reconciled values of the mixing streams example with a gross error
Table 8.5 Sets of process measurements free from gross errors/reconciled values
Table 8.6 Residuals and their corresponding normalized value.
Table 8.7 Covariance matrix of the normalized residuals of the data sets free from gross errors
Table 8.8 Results of the data reconciliation after removal of the term with gross error in the cost function
Table 8.9 Results of the data reconciliation with a gross error using M-estimators
Chapter 9: REI-Based Decision Support
Table 9.1 Comprehensive overview of the visualization methods introduced
Chapter 10: Advanced Process Control for Maximum Resource Efficiency
Table 10.1 Steady-state model gains for a depropanizer
Chapter 12: Demand Side Response (DSR) for Improving Resource Efficiency beyond Single Plants
Table 12.1 Barriers and drivers
Chapter 14: Synthesis of Resource Optimal Chemical Processes
Table 14.1 Example of process steps for property difference
Chapter 16: A Perspective on Process Integration
Table 16.1 Multi-Level energy requirement definition [6].
Table 16.2 Summary of comparisons between SPI and TSI
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
Cover
INEOS in Köln, Oliver Brenneisen
All books published by Wiley-VCH are carefully produced. Nevertheless, authors, editors, and publisher do not warrant the information contained in these books, including this book, to be free of errors. Readers are advised to keep in mind that statements, data, illustrations, procedural details or other items may inadvertently be inaccurate.
Library of Congress Card No.: applied for
British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library.
Bibliographic information published by the Deutsche Nationalbibliothek
The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at http://dnb.d-nb.de.
© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Boschstr. 12, 69469 Weinheim, Germany
All rights reserved (including those of translation into other languages). No part of this book may be reproduced in any form – by photoprinting, microfilm, or any other means – nor transmitted or translated into a machine language without written permission from the publishers. Registered names, trademarks, etc. used in this book, even when not specifically marked as such, are not to be considered unprotected by law.
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
oBook ISBN: 978-3-527-80415-3
Cover Design Adam-Design, Weinheim, Germany