Details

Resource Efficiency of Processing Plants


Resource Efficiency of Processing Plants

Monitoring and Improvement
1. Aufl.

von: Stefan Krämer, Sebastian Engell

142,99 €

Verlag: Wiley-VCH
Format: PDF
Veröffentl.: 14.12.2017
ISBN/EAN: 9783527804146
Sprache: englisch
Anzahl Seiten: 350

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Beschreibungen

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 3Stefan 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 19Jan 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 45Marjukka 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 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 65GuntherWindecker 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 81Benedikt 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 ?BDPL 125 References 126 6 Sensing Technology 129Alejandro 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 159Udo 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 181Cesar 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 211Marc 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 239André 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 265Cesar 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 293Iiro 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 317Guido 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 347Minbo 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 373Björn 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 403Ivan Kantor, Nasibeh Pouransari, and François Maréchal 16.1 Overview 403 16.2 Introduction 404 16.3 Heat Integration 405 16.3.1 Determining ?Tmin 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 441Greet 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 473Klaus 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
Dr. Stefan Kramer is Energy Manager at the petrochemical site of INEOS in Koln, Germany. He joined INEOS in 2004 as an Advanced Control Engineer, later took over a group of APC and DCS Engineers and in his current role is head of a team for energy management and energy optimization. Operating the site wide energy management system, making sure that the power generation and distribution is operated in a commercially optimal way, and coordinating energy and resource efficiency projects is part of his responsibilities. In two EU-funded research projects, the EU FP 7 project MORE and currently the EU Horizon 2020 SPIRE project CoPro, both dealing with resource efficiency, he acts as Industrial Application Coordinator. Stefan Kramer also co-leads the topic ?Energy Efficiency? in the pan-INEOS ?Carbon and Energy Network?. He is the former chairman of the NAMUR working group on Process Dynamics and Operation and currently the chairman of the NAMUR working group on Energy Efficiency and member of a sister working group in VIK. Stefan Kramer received his PhD at Technische Universitat Dortmund (Germany), where he still teaches Batch Process Operation. Stefan Kramer managed to build a reputation in the area of process control and energy efficiency and keeps publishing practical and scientific contributions in the areas of process modelling, process control, energy management and energy and resource efficiency. Prof. Dr. Sebastian Engell holds the Chair of Process Dynamics and Operations in the Department of Biochemical and Chemical Engineering at Technische Universitat Dortmund (Germany) since 1990. Professor Engell is an internationally renowned scientist in the field of process control and process operations and has published more than 400 scientific papers. He has been involved in several cooperative projects with industry, among others the EU FP 7 projects DYMASOS and MORE, and currently coordinates the EU Horizon 2020 SPIRE project CoPro ? Improved energy and resource efficiency by better coordination of production in the process industries. Professor Engell is a recipient of a European Research Council Advanced Investigator Grant and Fellow of the International Federation of Automatic Control. He received best paper awards from Journal of Process Control in 2007 and Computers and Chemical Engineering in 2016. He also edited the book ?Logistics of Chemical Production Processes? published by Wiley-VCH.

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