Profit Maximization Techniques for Operating Chemical Plants
John Wiley & Sons Inc (Verlag)
978-1-119-53215-6 (ISBN)
In the ongoing battle to reduce the cost of production and increase profit margin within the chemical process industry, leaders are searching for new ways to deploy profit optimization strategies. Profit Maximization Techniques For Operating Chemical Plants defines strategic planning and implementation techniques for managers, senior executives, and technical service consultants to help increase profit margins.
The book provides in-depth insight and practical tools to help readers find new and unique opportunities to implement profit optimization strategies. From identifying where the large profit improvement projects are to increasing plant capacity and pushing plant operations towards multiple constraints while maintaining continuous improvements—there is a plethora of information to help keep plant operations on budget.
The book also includes information on:
● Take away methods and techniques for identifying and exploiting potential areas to improve profit within the plant
● Focus on latest Artificial Intelligence based modeling, knowledge discovery and optimization strategies to maximize profit in running plant.
● Describes procedure to develop advance process monitoring and fault diagnosis in running plant
● Thoughts on engineering design , best practices and monitoring to sustain profit improvements
● Step-by-step guides to identifying, building, and deploying improvement applications
For leaders and technologists in the industry who want to maximize profit margins, this text provides basic concepts, guidelines, and step-by-step guides specifically for the chemical plant sector.
Sandip Kumar Lahiri, PhD, has over 26 years of experience in operation, process engineering and technical services for the leading petrochemical industries across the globe. He has carried out technical consultancy to leading Fortune 500 petrochemical plants across the globe as a technology advisor and is cited in World Who's Who as a significant contributor and achiever in the chemical industry. He has over 35 technical publications in leading international journals in chemical engineering covering subjects such as modelling and simulation, artificial intelligence, process design, optimization, fault diagnosis, CFD etc. He has authored two books on Applications of Metaheuristics in Process Engineering and Multivariable Predictive Control and holds a US patent on Online Fault Diagnosis in Chemical Plant. His interest includes refinery and petrochemical technology, applications of artificial intelligence in process industries, OPEX and CAPEX optimization, energy and water conservation, sustainable manufacturing, Industry 4.0, process safety and risk management; process design optimization, manufacturing excellence. He holds a position as Vice President, Technology at Haldia Petrochemical Ltd, India. Currently he is engaged with National Institute of Technology, Durgapur, India as Associate Professor.
Figure List xix
Table List xxv
Preface xxvii
1 Concept of Profit Maximization 1
1.1 Introduction 1
1.2 Who is This Book Written for? 3
1.3 What is Profit Maximization and Sweating of Assets All About? 4
1.4 Need for Profit Maximization in Today’s Competitive Market 7
1.5 Data Rich but Information Poor Status of Today’s Process Industries 8
1.6 Emergence of Knowledge-Based Industries 9
1.7 How Knowledge and Data Can Be Used to Maximize Profit 9
References 10
2 Big Picture of the Modern Chemical Industry 11
2.1 New Era of the Chemical Industry 11
2.2 Transition from a Conventional to an Intelligent Chemical Industry 11
2.3 How Will Digital Affect the Chemical Industry and Where Can the Biggest Impact Be Expected? 12
2.3.1 Attaining a New Level of Functional Excellence 12
2.3.1.1 Manufacturing 13
2.3.1.2 Supply Chain 14
2.3.1.3 Sales and Marketing 14
2.3.1.4 Research and Development 15
2.4 Using Advanced Analytics to Boost Productivity and Profitability in Chemical Manufacturing 15
2.4.1 Decreasing Downtime Through Analytics 16
2.4.2 Increase Profits with Less Resources 17
2.4.3 Optimizing the Whole Production Process 18
2.5 Achieving Business Impact with Data 19
2.5.1 Data’s Exponential Growing Importance in Value Creation 19
2.5.2 Different Links in the Value Chain 20
2.5.2.1 The Insights Value Chain – Definitions and Considerations 21
2.6 From Dull Data to Critical Business Insights: The Upstream Processes 22
2.6.1 Generating and Collecting Relevant Data 22
2.6.2 Data Refinement is a Two-Step Iteration 23
2.7 From Valuable Data Analytics Results to Achieving Business Impact: The Downstream Activities 25
2.7.1 Turning Insights into Action 25
2.7.2 Developing Data Culture 25
2.7.3 Mastering Tasks Concerning Technology and Infrastructure as Well as Organization and Governance 25
References 26
3 Profit Maximization Project (PMP) Implementation Steps 27
3.1 Implementing a Profit Maximization Project (PMP) 27
3.1.1 Step 1: Mapping the Whole Plant in Monetary Terms 27
3.1.2 Step 2: Assessment of Current Plant Conditions 27
3.1.3 Step 3: Assessment of the Base Control Layer of the Plant 28
3.1.4 Step 4: Assessment of Loss from the Plant 29
3.1.5 Step 5: Identification of Improvement Opportunity in Plant and Functional Design of PMP Applications 29
3.1.6 Step 6: Develop an Advance Process Monitoring Framework by Applying the Latest Data Analytics Tools 30
3.1.7 Step 7: Develop a Real-Time Fault Diagnosis System 30
3.1.8 Step 8: Perform a Maximum Capacity Test Run 30
3.1.9 Step 9: Develop and Implement Real-Time APC 31
3.1.10 Step 10: Develop a Data-Driven Offline Process Model for Critical Process Equipment 31
3.1.11 Step 11: Optimizing Process Operation with a Developed Model 32
3.1.12 Step 12: Modeling and Optimization of Industrial Reactors 32
3.1.13 Step 13: Maximize Throughput of All Running Distillation Columns 33
3.1.14 Step 14: Apply New Design Methodology for Process Equipment 33
References 34
4 Strategy for Profit Maximization 35
4.1 Introduction 35
4.2 How is Operating Profit Defined in CPI? 36
4.3 Different Ways to Maximize Operating Profit 36
4.4 Process Cost Intensity 37
4.4.1 Definition of Process Cost Intensity 37
4.4.2 Concept of Cost Equivalent (CE) 39
4.4.3 Cost Intensity for a Total Site 39
4.5 Mapping the Whole Process in Monetary Terms and Gain Insights 40
4.6 Case Study of a Glycol Plant 40
4.7 Steps to Map the Whole Plant in Monetary Terms and Gain Insights 43
4.7.1 Step 1: Visualize the Plant as a Black Box 43
4.7.2 Step 2: Data Collection from a Data Historian and Preparation of Cost Data 46
4.7.3 Step 3: Calculation of Profit Margin 46
4.7.4 Step 4: Gain Insights from Plant Cost and Profit Data 48
4.7.5 Step 5: Generation of Production Cost and a Profit Margin Table for One Full Year 51
4.7.6 Step 6: Plot Production Cost and Profit Margin for One Full Year and Gain Insights 51
4.7.7 Step 7: Calculation of Relative Standard Deviations of each Parameter in order to Understand the Cause of Variability 52
4.7.8 Step 8: Cost Benchmarking 53
Reference 54
5 Key Performance Indicators and Targets 55
5.1 Introduction 55
5.2 Key Indicators Represent Operation Opportunities 56
5.2.1 Reaction Optimization 56
5.2.2 Heat Exchanger Operation Optimization 58
5.2.3 Furnace Operation 58
5.2.4 Rotating Equipment Operation 59
5.2.5 Minimizing Steam Let down Flows 59
5.2.6 Turndown Operation 59
5.2.7 Housekeeping Aspects 59
5.3 Define Key Indicators 60
5.3.1 Process Analysis and Economics Analysis 61
5.3.2 Understand the Constraints 61
5.3.3 Identify Qualitatively Potential Area of Opportunities 65
5.4 Case Study of Ethylene Glycol Plant to Identify the Key Performance Indicator 66
5.4.1 Methodology 66
5.4.2 Ethylene Oxide Reaction Section 67
5.4.2.1 Understand the Process 67
5.4.2.2 Understanding the Economics of the Process 68
5.4.2.3 Factors that can Change the Production Cost and Overall Profit Generated from this Section 69
5.4.2.4 How is Production Cost Related to Process Parameters from the Standpoint of the Cause and Effect Relationship? 69
5.4.2.5 Constraints 69
5.4.2.6 Key Parameter Identifications 70
5.4.3 Cycle Water System 71
5.4.3.1 Main Purpose 71
5.4.3.2 Economics of the Process 71
5.4.3.3 Factors that can Change the Production Cost of this Section 72
5.4.3.4 Constraints 72
5.4.3.5 Key Performance Parameters 72
5.4.4 Carbon Dioxide Removal Section 73
5.4.4.1 Main Purpose 73
5.4.4.2 Economics 73
5.4.4.3 Factors that can Change the Production Cost of this Section 73
5.4.4.4 Constraints 74
5.4.4.5 Key Performance Parameters 74
5.4.5 EG Reaction and Evaporation Section 74
5.4.5.1 Main Purpose 74
5.4.5.2 Economics 75
5.4.5.3 Factors that can Change the Production Cost of this Section 76
5.4.5.4 Key Performance Parameters 76
5.4.6 EG Purification Section 76
5.4.6.1 Main Purpose 76
5.4.6.2 Economics 77
5.4.6.3 Key Performance Parameters 77
5.5 Purpose to Develop Key Indicators 77
5.6 Set up Targets for Key Indicators 78
5.7 Cost and Profit Dashboard 78
5.7.1 Development of Cost and Profit Dashboard to Monitor the Process Performance in Money Terms 78
5.7.2 Connecting Key Performance Indicators in APC 79
5.8 It is Crucial to Change the Viewpoints in Terms of Cost or Profit 80
References 80
6 Assessment of Current Plant Status 83
6.1 Introduction 83
6.1.1 Data Extraction from a Data Historian 83
6.1.2 Calculate the Economic Performance of the Section 84
6.2 Monitoring Variations of Economic Process Parameters 90
6.3 Determination of the Effect of Atmosphere on the Plant Profitability 90
6.4 Capacity Variations 91
6.5 Assessment of Plant Reliability 91
6.6 Assessment of Profit Suckers and Identification of Equipment for Modeling and Optimization 91
6.7 Assessment of Process Parameters Having a High Impact on Profit 93
6.8 Comparison of Current Plant Performance Against Its Design 93
6.9 Assessment of Regulatory Control System Performance 94
6.9.1 Basic Assessment Procedure 96
6.10 Assessment of Advance Process Control System Performance 97
6.11 Assessment of Various Profit Improvement Opportunities 97
References 98
7 Process Modeling by the Artificial Neural Network 99
7.1 Introduction 99
7.2 Problems to Develop a Phenomenological Model for Industrial Processes 100
7.3 Types of Process Model 101
7.3.1 First Principle-Based Model 101
7.3.2 Data-Driven Models 101
7.3.3 Grey Model 101
7.3.4 Hybrid Model 101
7.4 Emergence of Artificial Neural Networks as One of the Promising Data-Driven Modeling Techniques 106
7.5 ANN-Based Modeling 106
7.5.1 How Does ANN Work? 106
7.5.2 Network Architecture 107
7.5.3 Back-Propagation Algorithm (BPA) 107
7.5.4 Training 108
7.5.5 Generalizability 110
7.6 Model Development Methodology 110
7.6.1 Data Collection and Data Inspection 110
7.6.2 Data Pre-processing and Data Conditioning 110
7.6.2.1 Outlier Detection and Replacement 112
7.6.2.2 Univariate Approach to Detect Outliers 112
7.6.2.3 Multivariate Approach to Detect Outliers 112
7.6.3 Selection of Relevant Input–Output Variables 113
7.6.4 Align Data 113
7.6.5 Model Parameter Selection, Training, and Validation 113
7.6.6 Model Acceptance and Model Tuning 115
7.7 Application of ANN Modeling Techniques in the Chemical Process Industry 115
7.8 Case Study: Application of the ANN Modeling Technique to Develop an Industrial Ethylene Oxide Reactor Model 116
7.8.1 Origin of the Present Case Study 116
7.8.2 Problem Definition of the Present Case Study 117
7.8.3 Developing the ANN-Based Reactor Model 119
7.8.4 Identifying Input and Output Parameters 119
7.8.5 Data Collection 120
7.8.6 Neural Regression 121
7.8.7 Results and Discussions 122
7.9 Matlab Code to Generate the Best ANN Model 124
References 125
8 Optimization of Industrial Processes and Process Equipment 131
8.1 Meaning of Optimization in an Industrial Context 131
8.2 How Can Optimization Increase Profit? 132
8.3 Types of Optimization 133
8.3.1 Steady-State Optimization 133
8.3.2 Dynamic Optimization 133
8.4 Different Methods of Optimization 134
8.4.1 Classical Method 134
8.4.2 Gradient-Based Methods of Optimization 134
8.4.3 Non-traditional Optimization Techniques 135
8.5 Brief Historical Perspective of Heuristic-based Non-traditional Optimization Techniques 136
8.6 Genetic Algorithm 138
8.6.1 What is Genetic Algorithm? 138
8.6.2 Foundation of Genetic Algorithms 138
8.6.3 Five Phases of Genetic Algorithms 140
8.6.3.1 Initial Population 140
8.6.3.2 Fitness Function 140
8.6.3.3 Selection 140
8.6.3.4 Crossover 140
8.6.3.5 Termination 141
8.6.4 The Problem Definition 141
8.6.5 Calculation Steps of GA 141
8.6.5.1 Step 1: Generating Initial Population by Creating Binary Coding 141
8.6.5.2 Step 2: Evaluation of Fitness 142
8.6.5.3 Step 3: Selecting the Next Generation’s Population 142
8.6.6 Advantages of GA Against Classical Optimization Techniques 144
8.7 Differential Evolution 145
8.7.1 What is Differential Evolution (DE)? 145
8.7.2 Working Principle of DE 145
8.7.3 Calculation Steps Performed in DE 145
8.7.4 Choice of DE Key Parameters (NP, F, and CR) 145
8.7.5 Stepwise Calculation Procedure for DE implementation 146
8.8 Simulated Annealing 149
8.8.1 What is Simulated Annealing? 149
8.8.2 Procedure 149
8.8.3 Algorithm 150
8.9 Case Study: Application of the Genetic Algorithm Technique to Optimize the Industrial Ethylene Oxide Reactor 151
8.9.1 Conclusion of the Case Study 152
8.10 Strategy to Utilize Data-Driven Modeling and Optimization Techniques to Solve Various Industrial Problems and Increase Profit 153
References 155
9 Process Monitoring 159
9.1 Need for Advance Process Monitoring 159
9.2 Current Approaches to Process Monitoring and Diagnosis 160
9.3 Development of an Online Intelligent Monitoring System 161
9.4 Development of KPI-Based Process Monitoring 161
9.5 Development of a Cause and Effect-Based Monitoring System 163
9.6 Development of Potential Opportunity-Based Dash Board 163
9.6.1 Development of Loss and Waste Monitoring Systems 164
9.6.2 Development of a Cost-Based Monitoring System 165
9.6.3 Development of a Constraints-Based Monitoring System 166
9.7 Development of Business Intelligent Dashboards 166
9.8 Development of Process Monitoring System Based on Principal Component Analysis 167
9.8.1 What is a Principal Component Analysis? 168
9.8.2 Why Do We Need to Rotate the Data? 169
9.8.3 How Do We Generate Principal Components? 170
9.8.4 Steps to Calculating the Principal Components 170
9.9 Case Study for Operational State Identification and Monitoring Using PCA 171
9.9.1 Case Study 1: Monitoring a Reciprocating Reclaim Compressor 171
References 174
10 Fault Diagnosis 177
10.1 Challenges to the Chemical Industry 177
10.2 What is Fault Diagnosis? 178
10.3 Benefit of a Fault Diagnosis System 179
10.3.1 Characteristic of an Automated Fault Diagnosis System 180
10.4 Decreasing Downtime Through a Fault Diagnosis Type Data Analytics 180
10.5 User Perspective to Make an Effective Fault Diagnosis System 181
10.6 How Are Fault Diagnosis Systems Made? 183
10.6.1 Principal Component-Based Approach 184
10.6.2 Artificial Neural Network-Based Approach 184
10.7 A Case Study to Build a Robust Fault Diagnosis System 185
10.7.1 Challenges to a Build Fault Diagnosis of an Ethylene Oxide Reactor System 187
10.7.2 PCA-Based Fault Diagnosis of an EO Reactor System 187
10.7.3 Acquiring Historic Process Data Sets to Build a PCA Model 188
10.7.4 Criteria of Selection of Input Parameters for PCA 189
10.7.5 How PCA Input Data is Captured in Real Time 191
10.7.6 Building the Model 192
10.7.6.1 Calculations of the Principal Components 192
10.7.6.2 Calculations of Hotelling’s T2 192
10.7.6.3 Calculations of the Residual 193
10.7.7 Creation of a PCA Plot for Training Data 193
10.7.8 Creation of Hotelling’s T2 Plot for the Training Data 194
10.7.9 Creation of a Residual Plot for the Training Data 194
10.7.10 Creation of an Abnormal Zone in the PCA Plot 194
10.7.11 Implementing the PCA Model in Real Time 194
10.7.12 Detecting Whether the Plant is Running Normally or Abnormally on a Real-Time Basis 195
10.7.13 Use of a PCA Plot During Corrective Action in Real Time 197
10.7.14 Validity of a PCA Model 198
10.7.14.1 Time-Varying Characteristic of an EO Catalyst 198
10.7.14.2 Capturing the Efficiency of the PCA Model Using the Residual Plot 199
10.7.15 Quantitive Decision Criteria Implemented for Retraining of an Ethylene Oxide (EO) Reactor PCA Model 200
10.7.16 How Retraining is Practically Executed 200
10.8 Building an ANN Model for Fault Diagnosis of an EO Reactor 200
10.8.1 Acquiring Historic Process Data Sets to Build an ANN Model 200
10.8.2 Identification of Input and Output Parameters 201
10.8.3 Building of an ANN-Based EO Reactor Model 201
10.8.3.1 Complexity of EO Reactor Modeling 201
10.8.3.2 Model Building 202
10.8.4 Prediction Performance of an ANN Model 203
10.8.5 Utilization of an ANN Model for Fault Detection 203
10.8.6 How Do PCA Input Data Relate to ANN Input/Output Data? 204
10.8.7 Retraining of an ANN Model 206
10.9 Integrated Robust Fault Diagnosis System 206
10.10 Advantages of a Fault Diagnosis System 208
References 208
11 Optimization of an Existing Distillation Column 209
11.1 Strategy to Optimize the Running Distillation Column 209
11.1.1 Strategy 209
11.2 Increase the Capacity of a Running Distillation Column 210
11.3 Capacity Diagram 211
11.4 Capacity Limitations of Distillation Columns 212
11.5 Vapour Handling Limitations 214
11.5.1 Flow Regimes – Spray and Froth 214
11.5.2 Entrainment 215
11.5.3 Tray Flooding 215
11.5.4 Ultimate Capacity 217
11.6 Liquid Handling Limitations 217
11.6.1 Downcomer Flood 217
11.6.2 Downcomer Residence Time 217
11.6.3 Downcomer Froth Back-Up% 219
11.6.4 Downcomer Inlet Velocity 220
11.6.5 Weir liquid loading 221
11.6.6 Downcomer Sizing Criteria 221
11.7 Other Limitations and Considerations 221
11.7.1 Weeping 221
11.7.2 Dumping 222
11.7.3 Tray Turndown 222
11.7.4 Foaming 223
11.8 Understanding the Stable Operation Zone 223
11.9 Case Study to Develop a Capacity Diagram 224
11.9.1 Calculation of Capacity Limits 224
11.9.1.1 Spray Limit 224
11.9.1.2 Vapor Flooding Limit 226
11.9.1.3 Downcomer Backup Limit 226
11.9.1.4 Maximum Liquid Loading Limit 227
11.9.1.5 Minimum Liquid Loading Limit 227
11.9.1.6 Minimum Vapor Loading Limit 228
11.9.2 Plotting a Capacity Diagram 228
11.9.3 Insights from the Capacity Diagram 229
11.9.4 How Can the Capacity Diagram Be Used for Profit Maximization? 229
References 230
12 New Design Methodology 231
12.1 Need for New Design Methodology 231
12.2 Case Study of the New Design Methodology for a Distillation Column 231
12.2.1 Traditional Way to Design a Distillation Column 231
12.2.2 Background of the Distillation Column Design 232
12.3 New Intelligent Methodology for Designing a Distillation Column 234
12.4 Problem Description of the Case Study 237
12.5 Solution Procedure Using the New Design Methodology 237
12.6 Calculations of the Total Cost 238
12.7 Search Optimization Variables 239
12.8 Operational and Hydraulic Constraints 239
12.9 Particle Swarm Optimization 241
12.9.1 PSO Algorithm 241
12.10 Simulation and PSO Implementation 242
12.11 Results and Analysis 243
12.12 Advantages of PSO 245
12.13 Advantages of New Methodology over the Traditional Approach 246
12.14 Conclusion 248
Nomenclature 248
References 250
Appendix 12.1 251
13 Genetic Programing for Modeling of Industrial Reactors 259
13.1 Potential Impact of Reactor Optimization on Overall Profit 259
13.2 Poor Knowledge of Reaction Kinetics of Industrial Reactors 259
13.3 ANN as a Tool for Reactor Kinetic Modeling 260
13.4 Conventional Methods for Evaluating Kinetics 260
13.5 What is Genetic Programming? 261
13.6 Background of Genetic Programming 262
13.7 Genetic Programming at a Glance 263
13.7.1 Preparatory Steps of Genetic Programming 264
13.7.2 Executional Steps of Genetic Programming 264
13.7.3 Creating an Individual 267
13.7.4 Fitness Test 268
13.7.5 The Genetic Operations 269
13.7.6 User Decisions 271
13.7.7 Computing Resources 272
13.8 Example Genetic Programming Run 272
13.8.1 Preparatory Steps 273
13.8.2 Step-by-Step Sample Run 274
13.8.3 Selection, Crossover, and Mutation 275
13.9 Case Studies 277
13.9.1 Case Study 1 277
13.9.2 Case Study 2 278
13.9.3 Case Study 3 279
13.9.4 Case Study 4 280
References 281
14 Maximum Capacity Test Run and Debottlenecking Study 283
14.1 Introduction 283
14.2 Understanding Different Safety Margins in Process Equipment 283
14.3 Strategies to Exploit the Safety Margin 284
14.4 Capacity Expansion versus Efficiency Reduction 285
14.5 Maximum Capacity Test Run: What is it All About? 286
14.6 Objective of a Maximum Capacity Test Run 287
14.7 Bottlenecks of Different Process Equipment 288
14.7.1 Functional Bottleneck 288
14.7.2 Reliability Bottleneck 288
14.7.3 Safety Interlock Bottleneck 290
14.8 Key Steps to Carry Out a Maximum Capacity Test Run in a Commercial Running Plant 291
14.8.1 Planning 291
14.8.2 Discussion with Technical People 296
14.8.3 Risk and Opportunity 296
14.8.4 Dos and Don’ts 297
14.8.5 Simulations 298
14.8.6 Preparations 299
14.8.7 Management of Change 299
14.8.8 Execution 300
14.8.9 Data Collections 300
14.8.10 Critical Observations 302
14.8.11 Report Preparations 303
14.8.12 Detailed Simulations and Assembly of All Observations 303
14.8.13 Final Report Preparation 304
14.9 Scope and Phases of a Detailed Improvement Study 304
14.9.1 Improvement Scoping Study 305
14.9.2 Detail Feasibility Study 305
14.9.3 Retrofit Design Phase 305
14.10 Scope and Limitations of MCTR 306
14.10.1 Scope 306
14.10.2 Two Big Benefits of Doing MCTR 306
14.10.3 Limitations of MCTR 306
15 Loss Assessment 309
15.1 Different Losses from the System 309
15.2 Strategy to Reduce the Losses andWastages 309
15.3 Money Loss Audit 310
15.4 Product or Utility Losses 312
15.4.1 Loss in the Drain 312
15.4.2 Loss Due to Vent and Flaring 313
15.4.3 Utility Loss 314
15.4.4 Heat Loss Assessment for the Fired Heater 314
15.4.5 Heat Loss Assessment for the Distillation Column 315
15.4.6 Heat Loss Assessment for Steam Leakage 316
15.4.7 Heat Loss Assessment for Condensate Loss 317
16 Advance Process Control 319
16.1 What is Advance Process Control? 319
16.2 Why is APC Necessary to Improve Profit? 320
16.3 Why APC is Preferred over Normal PID Regulatory Control 322
16.4 Position of APC in the Control Hierarchy 324
16.5 Which are the Plants where Implementations of APC were Proven Very Profitable? 327
16.6 How do Implementations of APC Increase Profit? 328
16.7 How does APC Extract Benefits? 330
16.8 Application of APC in Oil Refinery, Petrochemical, Fertilizer and Chemical Plants and Related Benefits 334
16.9 Steps to Execute an APC Project 336
16.9.1 Step 1: Preliminary Cost –Benefit Analysis 336
16.9.2 Step 2: Assessment of Base Control Loops 337
16.9.3 Step 3: Functional Design of the Controller 337
16.9.4 Step 4: Conduct the Plant Step Test 338
16.9.5 Step 5: Generate a Process Model 338
16.9.6 Step 6: Commission the Online Controller 338
16.9.7 Step 7: Online APC Controller Tuning 339
16.10 How Can an Effective Functional Design Be Done? 339
16.10.1 Step 1: Define Process Control Objectives 340
16.10.2 Step 2: Identification of Process Constraints 342
16.10.3 Step 3: Define Controller Scope 343
16.10.4 Step 4: Variable Selection 344
16.10.5 Step 5: Rectify Regulatory Control Issues 346
16.10.6 Step 6: Explore the Scope of Inclusions of Inferential Calculations 347
16.10.7 Step 7: Evaluate Potential Optimization Opportunity 347
16.10.8 Step 8: Define LP or QP Objective Function 348
References 349
17 150 Ways and Best Practices to Improve Profit in Running Chemical Plant 351
17.1 Best Practices Followed in Leading Process Industries Around the World 351
17.2 Best Practices Followed in a Steam and Condensate System 351
17.3 Best Practices Followed in Furnaces and Boilers 355
17.4 Best Practices Followed in Pumps, Fans, and Compressor 357
17.5 Best Practices Followed in Illumination Optimization 359
17.6 Best Practices in Operational Improvement 359
17.7 Best Practices Followed in Air and Nitrogen Header 360
17.8 Best Practices Followed in Cooling Tower and CoolingWater 361
17.9 Best Practices Followed inWater Conservation 362
17.10 Best Practices Followed in Distillation Column and Heat Exchanger 363
17.11 Best Practices in Process Improvement 364
17.12 Best Practices in Flare Gas Reduction 365
17.13 Best Practices in Product or Energy Loss Reduction 365
17.14 Best Practices to Monitor Process Control System Performance 366
17.15 Best Practices to Enhance Plant Reliability 367
17.16 Best Practices to Enhance Human Resource 368
17.17 Best Practices to Enhance Safety, Health, and the Environment 368
17.18 Best Practices to Use New Generation Digital Technology 369
17.19 Best Practices to Focus a Detailed Study and R&D Effort 370
Index 373
| Erscheinungsdatum | 03.06.2020 |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Maße | 170 x 241 mm |
| Gewicht | 862 g |
| Themenwelt | Naturwissenschaften ► Chemie ► Technische Chemie |
| Technik | |
| ISBN-10 | 1-119-53215-9 / 1119532159 |
| ISBN-13 | 978-1-119-53215-6 / 9781119532156 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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