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Resource Efficiency of Processing Plants: Monitoring and Improvement

Resource Efficiency of Processing Plants: Monitoring and Improvement

Stefan Krämer, Sebastian Engell

ISBN: 978-3-527-80416-0

Dec 2017

350 pages

$172.99

Description

This monograph provides foundations, methods, guidelines and examples for monitoring and improving resource efficiency during the operation of processing plants and for improving their design.

The measures taken to improve their energy and resource efficiency are strongly influenced by regulations and standards which are covered in Part I of this book. Without changing the actual processing equipment, the way how the processes are operated can have a strong influence on the resource efficiency of the plants and this potential can be exploited with much smaller investments than needed for the introduction of new process technologies. This aspect is the focus of Part II. In Part III we discuss physical changes of the process technology such as heat integration, synthesis and realization of optimal processes, and industrial symbiosis.

The last part deals with the people that are needed to make these changes possible and discusses the path towards a company and sector wide resource efficiency culture.

Written with industrial solutions in mind, this text will benefit practitioners as well as the academic community.

Preface xvii

Part I Resource Efficiency Metrics and Standardised Management Systems 1

1 Energy and Resource Efficiency in the Process Industries 3
Stefan Krämer and Sebastian Engell

1.1 Introduction 3

1.2 Energy and Resources 4

1.2.1 What DoWe Mean by Energy and Resources? 4

1.2.2 Classification of Energy and Resources 5

1.3 Energy and Resource Efficiency 6

1.4 Evaluation of Energy and Resource Efficiency 6

1.5 Evaluation of Energy and Resource Efficiency in Real Time 8

1.6 The Chemical and Process Industry 8

1.6.1 Introduction 8

1.6.2 The Structure of the EU Chemical Industry 9

1.6.3 Energy and Raw Material Use of the Chemical Industry 9

1.7 Recent and Potential Improvements in Energy and Resource Consumption of the Chemical and Process Industries 10

1.8 What Can Be Done to Further Improve the Resource Efficiency of the Process Industry? 11

1.8.1 Make a Plan, Set Targets and Validate the Achievements 11

1.8.2 Measure and Improve Operations 12

1.8.3 Improve the Process 14

1.8.4 Integrate with Other Industrial Sectors and with the Regional Municipal Environment 15

1.8.5 Don’t Forget the People 15

1.9 Conclusions 15

References 16

2 Standards, Regulations and Requirements Concerning Energy and Resource Efficiency 19
Jan U. Lieback, Jochen Buser, David Kroll, Nico Behrendt, and SeánOppermann

2.1 Introducing a Long-Term Development 19

2.1.1 Historical Background and Reasoning 19

2.1.2 Relation of CO2 Emissions and Energy Efficiency 20

2.1.3 EU Goals for Energy Efficiency 21

2.1.4 Energy EfficiencyWorldwide 22

2.1.5 Growing EU Concern on Resource Efficiency 23

2.2 Normative Approaches on Energy and Resource Efficiency 24

2.2.1 Management Systems, Aim and Construction 24

2.2.2 From Precursors towards the ISO 50001 25

2.2.3 Basics of ISO 50001 and Dissemination 26

2.2.4 Energy Efficiency Developments in Germany 27

2.2.5 ISO 50001 and ISO 50004 28

2.2.5.1 ISO 50001 28

2.2.5.2 ISO 50004 28

2.2.6 ISO 50003 and Companions ISO 50006 and 50015 29

2.2.7 EN 16247 and ISO 50002 29

2.2.8 New Standards 31

2.2.9 Normative Approaches Regarding Resource Efficiency 32

2.2.10 Perspectives 33

2.3 Achievements of Energy and Resource Management 34

2.3.1 Energy Baseline (EnB) and Energy Performance Indicators (EnPIs), Controlling Efficiency Improvement 34

2.3.2 Developing EnPIs, Measuring and Verification of Energy Performance 34

2.3.3 Hierarchy of Measures 36

2.3.4 Energy and Resource Efficiency in the Context of Energy Management 36

2.3.5 Examples of Measures 37

2.4 Conclusion 38

References 39

3 Energy and Resource Efficiency Reporting 45
Marjukka Kujanpää, Tiina Pajula, and HelenaWessman-Jääskeläinen

3.1 Executive Summary 45

3.2 Introduction 45

3.3 Obligatory Reporting Mechanisms 47

3.3.1 EU Directive on Industrial Emissions (IED) 47

3.3.2 EU Directive on Non-Financial Reporting 48

3.4 Voluntary Reporting Mechanisms 49

3.4.1 Eco-Management and Audit Scheme (EMAS) 49

3.4.2 OECD Guidelines for Multinational Enterprises 49

3.4.3 UN Global Compact 50

3.4.4 Global Reporting Initiative (GRI) 51

3.4.5 Integrated Reporting and the <IR> Framework 52

3.4.6 GHG protocol 54

3.4.7 ISO 14000 Series 54

3.4.8 Environmental Labels 55

3.4.9 Environmental Product Footprint and Organisational Footprint (PEF, OEF) 59

3.5 Other Reporting Mechanisms 59

3.5.1 Key Performance Indicators 59

3.6 Summary of the Energy and Resource Efficiency Reporting Requirements 60

References 61

4 Energy Efficiency Audits 65
GuntherWindecker

4.1 Introduction 65

4.2 Stage 1: Current Energy Status 66

4.3 Stage 2: Basic Analysis 67

4.4 Stage 3: Detailed Analysis and Collection of Ideas 69

4.5 Stage 4: Evaluation and Selection of Measures 72

4.6 Stage 5: Realization and Monitoring 76

4.7 Extension to Resource Efficiency 77

4.8 Closing Remark 77

References 78

Part II Monitoring and Improvement of the Resource Efficiency through Improved Process Operations 79

5 Real-Time Performance Indicators for Energy and Resource Efficiency in Continuous and Batch Processing 81
Benedikt Beisheim,Marc Kalliski, Daniel Ackerschott, Sebastian Engell, and Stefan Krämer

5.1 Introduction 81

5.2 Real-Time Resource Efficiency Indicators 82

5.2.1 Resource Efficiency 82

5.2.2 REI as (Key) Performance Indicators ((K)PI) 83

5.2.3 Real-Time Resource Efficiency Monitoring 84

5.2.4 PrinciplesThat Guide the Definition of Real-Time REI (Adapted from Ref. [10]) 84

5.2.4.1 Gate-to-Gate Approach 85

5.2.4.2 Based on Material and Energy Flow Analysis 85

5.2.4.3 Resource and Output Specific to a Potential for Meaningful Aggregation 85

5.2.4.4 Normalize to the Best Possible Operation 86

5.2.4.5 Consider (Long-Term) Storage Effects 86

5.2.4.6 Include Environmental Impact 86

5.2.4.7 Hierarchy of Indicators – From theWhole Site to a Single Apparatus 87

5.2.4.8 Focus on Technical Performance Independent of Economic Factors 87

5.2.4.9 Extensible to Life-Cycle Analysis (LCA) 87

5.2.5 Extension to LCA and Reporting 87

5.2.6 Real-Time Resource Efficiency Indicators: Generic Indicators 88

5.2.7 Definition of Baselines: Average and Best Cases 88

5.3 Evaluation of the Suitability of Resource Efficiency Indicators 91

5.3.1 Basic Procedure 91

5.3.2 The MORE RACER Evaluation Framework 93

5.3.3 Application of the RACER Framework 95

5.4 Hierarchical Modelling of Continuous Production Complexes 96

5.4.1 Introduction to the Plant Hierarchy 96

5.4.2 Aggregation and Contribution Calculation 98

5.4.2.1 General Performance Deviation 98

5.4.2.2 Aggregation 98

5.4.2.3 Performance Contribution of Lower Levels 99

5.4.2.4 Load Contribution of Lower Levels 100

5.4.2.5 Contribution of Other Factors 101

5.4.2.6 Overall Result 102

5.4.2.7 Illustrative Example 103

5.4.3 Integration of Utility and Energy Provider 105

5.4.4 Product-Oriented REI 106

5.4.5 Simulated Example 107

5.5 Batch Production 112

5.5.1 Batch Resource Efficiency Indicators 113

5.5.1.1 Energy Efficiency 114

5.5.1.2 Material Efficiency 115

5.5.1.3 Water andWaste Efficiency 116

5.5.2 REI for Key Production Phases 116

5.5.2.1 Reaction Efficiency 117

5.5.2.2 Purification Efficiency 117

5.5.3 REI for Plant-Wide Contributions to Resource Efficiency 118

5.5.4 Rules for the Propagation and Aggregation of REI 119

5.5.4.1 Recycled Materials 119

5.5.5 Uniting and Splitting of Batches 119

5.6 Integrated Batch and Continuous Production 122

5.6.1 Transition from Batch to Continuous Production 122

5.6.2 Transition from Continuous to Batch Production 124

5.7 Conclusions 124

Appendix: Decomposition of ΔBDPL 125

References 126

6 Sensing Technology 129
Alejandro Rosales and OonaghMc Nerney

6.1 Introduction 129

6.2 Sensing: General Considerations and Challenges 131

6.2.1 Precision 132

6.2.2 Accuracy 132

6.2.3 The Limitations of Any Measurement Method Due to the Inadequacy of theTheoretical Model for Matching the Real-World Conditions 134

6.2.4 Sampling: The Nature of the Interaction Between the Bodies to be Measured and theMeasurement Instrument is a Key Consideration for Inline Monitoring 135

6.3 Energy Saving by Means of Accurate Metering 136

6.4 Latest Advancements in Spectroscopy Technology for Process-Monitoring-Based Efficiency 137

6.4.1 Introduction and State of the Art 137

6.4.2 Hyperspectral Imaging 138

6.4.3 Time-Gated Raman 139

6.5 Process Analytical Technologies (PAT) 142

6.6 Soft Sensors. Access to the “Truth” Distributed Among a Plurality of Simple Sensors 146

6.7 MEMS-Based Sensors. Smart Sensors 147

6.8 Future Trends in Sensing with Promising Impact on Reliable Process Monitoring 148

6.8.1 Quantum Cascade Lasers (QCLs) 149

6.8.2 Graphene-Based Sensors 150

6.9 European R&D: Driving Forward Sensing Advancements 151

6.10 Conclusion 152

References 154

7 Information Technology and Structuring of Information for Resource Efficiency Analysis and Real-Time Reporting 159
Udo Enste

7.1 Introduction 159

7.2 Information Technology in the Process Industries 159

7.3 Resource Flow Modelling and Structuring of Information 163

7.3.1 Resource Managed Units 163

7.3.2 3-Tier Information Modelling Approach 164

7.4 From Formulae to Runtime Software 167

7.4.1 Recommended System Architecture – Building Context Awareness 167

7.4.2 REI Application Design Process 168

7.5 Industrial Installations 171

7.5.1 Example 1: Batch-Continuous-Process 171

7.5.2 Example 2: Integrated Chemical Production Complex 175

7.6 Summary and Conclusions 178

References 179

8 Data Pre-treatment 181
Cesar de Prada and Daniel Sarabia

8.1 Measurement Errors and Variable Estimation 182

8.2 Data Reconciliation 188

8.3 Gross Errors Detection and Removal 193

8.3.1 StatisticalMethods for Gross Errors Detection 195

8.3.2 Robust M-Estimators 202

8.4 Data Pre-treatment and Steady-State Detection 205

8.5 Dynamic Data Reconciliation 208

8.6 Conclusions 209

References 210

9 REI-Based Decision Support 211
Marc Kalliski, Benedikt Beisheim, Daniel Ackerschott, Stefan Krämer, and Sebastian Engell

9.1 Introduction 211

9.2 Visualization 213

9.2.1 Principles of Human–Machine Interface Engineering 213

9.2.2 REI Visualization Concepts 215

9.2.2.1 Indicators Included in Plant Structure 215

9.2.2.2 Sankey Diagrams 215

9.2.2.3 Bullet Chart 216

9.2.2.4 Stacked Bars and Stacked Area Plots 217

9.2.2.5 Difference Charts and Sparklines 218

9.2.2.6 Aggregated Tiles 220

9.2.2.7 Selection of Visualization Elements for Efficient Concepts 220

9.2.3 Process Monitoring 221

9.2.3.1 Dashboard Concept for the Sugar Plant Case Study 223

9.3 What-If Analysis 224

9.3.1 Introduction 224

9.3.2 Requirements 225

9.3.2.1 Graphical Guidance 225

9.3.2.2 Flexibility 225

9.3.2.3 Analysis of Results 226

9.3.2.4 Visual Feedback 226

9.3.2.5 Scenario Database 226

9.3.3 Exemplary Application 226

9.4 Optimization 229

9.4.1 Introduction 229

9.4.2 Requirements 230

9.4.2.1 Real-Time Performance 231

9.4.2.2 Analysis of Optima 231

9.4.2.3 Multicriterial Optimization 231

9.4.3 Exemplary Application 232

9.5 Conclusions 235

References 236

10 Advanced Process Control for Maximum Resource Efficiency 239
André Kilian

10.1 Introduction 239

10.2 The Importance of Constraint Control 239

10.2.1 Operating Strategy for a Simple Depropanizer Column: Motivating Example 240

10.2.2 Graphical Representation of Constraints 244

10.2.3 Additive Nature of Constraint Give-Away 245

10.2.4 The Need for Closed-Loop Optimization 246

10.3 What is Advanced Process Control? 247

10.3.1 The Control Pyramid 247

10.3.2 Common Features of MPC Technologies 249

10.4 Benefits and Requirements for Success 254

10.4.1 Achieving Financial Benefits 254

10.4.2 Justification and Benefit Estimation 256

10.5 Requirements for success 258

10.6 Conclusion 262

References 263

11 Real-Time Optimization (RTO) Systems 265
Cesar de Prada and José L. Pitarch

11.1 Introduction 265

11.2 RTO Systems 268

11.3 OptimizationMethods and Tools 274

11.3.1 Non-Linear Programming 275

11.3.1.1 KKT Optimality Conditions 276

11.3.1.2 Sequential Quadratic Programming (SQP) 277

11.3.1.3 Interior Point (IP) Methods 278

11.3.2 Software and Practice 279

11.3.3 Dynamic Optimization 280

11.4 Application Example: RTO in a Multiple-Effect Evaporation Process 281

11.4.1 Steady-State Modelling 283

11.4.2 Experimental Customization 285

11.4.2.1 Data Reconciliation 286

11.4.2.2 Proposed Procedure 286

11.4.3 Optimal Operation 289

11.4.4 Some Experimental Results 290

11.5 Conclusions 291

References 291

12 Demand Side Response (DSR) for Improving Resource Efficiency beyond Single Plants 293
Iiro Harjunkoski, Lennart Merkert, and Jan Schlake

12.1 Executive Summary 293

12.2 Introduction 293

12.2.1 Trends 294

12.2.2 Demand Side Response to Stabilize the Electricity Grid 295

12.2.3 History of Demand Side Response 296

12.3 Structure of this Chapter 297

12.4 Motivation 297

12.4.1 Demand for Flexibility and Alternatives to Demand Side Response 299

12.4.1.1 Increase Flexibility via Additional Energy Storage Capacity 299

12.4.1.2 Increase Flexibility via Additional Conventional Power Plants 299

12.4.1.3 Increase Flexibility through Active Control of Renewable Energy Sources 299

12.4.1.4 Increase Flexibility through an Increased Grid Capacity 300

12.4.1.5 Increase Flexibility through Alternative Market Options 300

12.4.2 Types of Demand Side Response Measures 300

12.4.3 Market Drivers and Market Barriers 300

12.5 Demand Side Response at Large Consumers 301

12.5.1 Energy Efficiency (EE) 301

12.5.1.1 Example: Use of More Energy-Efficient Pumps 301

12.5.2 Load Management – Energy Demand Changes by Enhanced Planning Capability 304

12.5.3 DSR Triggers 304

12.5.3.1 Utility Trigger and Price Changes 305

12.5.3.2 Energy Shortage 305

12.5.3.3 Energy Portfolio Optimization 305

12.5.4 Types of Demand Side Response 306

12.5.4.1 Peak Shaving 309

12.5.4.2 Load Shedding 309

12.5.4.3 Load Shifting 309

12.5.4.4 Ancillary Services 309

12.6 Valorization 310

12.6.1 Industrial Examples of Demand Side Response 311

12.6.2 Example: Steel Production 312

12.7 Summary and Outlook 313

References 314

13 Energy Efficiency Improvement using STRUCTeseTM 317
Guido Dünnebier,Matthias Böhm, Christian Drumm, Felix Hanisch, and Gerhard Then

13.1 Introduction 318

13.1.1 STRUCTeseTM Management System 321

13.1.2 Energy Efficiency Check and Improvement Plan 323

13.1.3 Energy Loss Cascade and Performance Indicators 327

13.1.4 Online Monitoring and Daily Energy Protocol 336

13.1.5 Implementation Results 338

13.1.6 Open Issues and Research Topics 341

References 343

Part III Improving Resource Efficiency by Process Improvement 345

14 Synthesis of Resource Optimal Chemical Processes 347
Minbo Yang, Jian Gong, and Fengqi You

14.1 Introduction 347

14.1.1 Background and Motivation 347

14.1.2 Resource Optimal Chemical Processes 349

14.2 Heuristic Methods 350

14.2.1 Pinch Technology for Resource Network Integration 350

14.2.2 Other Heuristic Methods for Process Synthesis 352

14.3 Superstructure Optimization Based Method 353

14.3.1 Superstructure Generation 353

14.3.2 Data Extraction 355

14.3.3 MathematicalModel Formulation 356

14.3.3.1 Mass Balance Constraints 356

14.3.3.2 Energy Balance Constraints 358

14.3.3.3 Economic Evaluation Constraints 360

14.3.3.4 Objective Function 361

14.3.4 Solution Methods 362

14.3.5 Applications of Synthesis of Resource Optimal Chemical

Processes 363

14.3.6 Hybrid Methods 364

14.4 Other Impact Factors on Resource Optimal Chemical Processes 365

14.4.1 Environmental Factors 365

14.4.2 Social Factors 366

14.4.3 Uncertainty 366

14.5 Conclusion 366

References 367

15 Optimization-Based Synthesis of Resource-Efficient Utility Systems 373
Björn Bahl, Maike Hennen, Matthias Lampe, Philip Voll, and André Bardow

15.1 Introduction 373

15.2 Definition of Utility Systems 375

15.3 Problem Statement 375

15.4 Modelling 377

15.4.1 Model Complexity 377

15.4.1.1 Time Representation 378

15.4.1.2 Part-Load Performance 379

15.4.2 Decomposition 380

15.4.3 Time-Series Aggregation 381

15.5 Solution Methods for Optimal Synthesis of Utility Systems 382

15.5.1 Superstructure-Based Optimal Synthesis of Utility Systems 383

15.5.2 Superstructure-Free Optimal Synthesis of Utility Systems 385

15.6 Analysis of Multiple Solutions for Decision Support 387

15.6.1 Multi-objective Optimization 388

15.6.2 Near-Optimal Solutions 388

15.6.3 Optimization under Uncertainty 390

15.7 Industrial Case Study 390

15.7.1 Description of the Case Study 391

15.7.2 Economically Optimal Solution 393

15.7.3 Multi-objective Optimization 394

15.7.4 Near-Optimal Solutions 395

15.8 Conclusions for the Utility System Synthesis in Industrial

Practice 397

Acknowledgments 398

References 398

16 A Perspective on Process Integration 403
Ivan Kantor, Nasibeh Pouransari, and François Maréchal

16.1 Overview 403

16.2 Introduction 404

16.3 Heat Integration 405

16.3.1 Determining ΔTmin 406

16.3.2 Composite and Grand Composite Curves 409

16.3.3 Identifying Penalising Heat Exchangers 411

16.3.4 Improving the Heat Recovery Targets 412

16.3.5 Caste Study I: Application of Advanced Heat Integration Technologies 413

16.4 Energy and Resource Integration 416

16.4.1 Multi-Level Energy Requirement Definition 418

16.4.2 Problem Formulation 419

16.4.3 Heat Cascade 420

16.4.4 Mass Integration 420

16.4.5 Electricity 423

16.4.6 Transportation 424

16.4.7 Investment and Operating Costs 425

16.4.8 Alternative Objectives 428

16.4.9 Caste Study II: Site-Scale Integration and Multi-Level Energy Requirement Definition 430

16.4.9.1 Single Process Integration (SPI) 430

16.4.9.2 Total Site Integration (TSI) 432

16.4.9.3 Heat Recovery Improvement Potentials 432

16.4.9.4 Integration and Optimization of Energy Conversion Units 435

16.5 Summary 437

References 439

17 Industrial Symbiosis 441
Greet Van Eetvelde

17.1 Syn-Bios and Syn-Ergon 441

17.1.1 Economies of Scale and Scope 441

17.1.2 Economies in Transition 444

17.1.3 Low-Carbon Economies 447

17.2 Industrial Symbiosis 449

17.2.1 State of the Art – IS Practice 450

17.2.1.1 IS Parks 450

17.2.1.2 IS Technologies 451

17.2.1.3 IS Services 453

17.2.1.4 IS Policies 454

17.2.2 State of the Art - IS Research 454

17.2.3 Innovation Potential 458

17.2.4 The EU Perspective 460

17.3 Business Clustering 460

17.3.1 Business Parks and Park Management 461

17.3.2 Total Site Integration and Site Management 462

17.3.3 Cross-Sectorial Clustering and Cluster Management 464

17.4 Conclusions 467

References 467

Part IV Company Culture for Resource Efficiency 471

18 Organizational Culture for Resource Efficiency 473
Klaus Goldbeck and Stefan Krämer

18.1 Introduction 473

18.2 The Basics 474

18.2.1 Trust and Motivation 474

18.2.2 Justice and Fairness 476

18.2.3 Strokes 477

18.2.4 Orientation 479

18.3 Implementation 479

18.3.1 Differentiation 479

18.3.2 The Principles 480

18.3.3 The Desired Result 481

18.3.4 The Integration 485

18.3.5 The Standard 486

18.3.6 The Measures 486

18.3.7 The Rules 487

18.3.8 The Performance 488

18.3.9 Resistance 488

18.3.10 Incentives 489

18.3.11 Feedback Loops 491

18.4 Giving It a Meaning 491

18.5 Closing Remarks 492

Acknowledgments 493

References 493

Index 495