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Profit Maximization Techniques for Operating Chemical Plants

Lahiri, Sandip K.

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1. Edition May 2020
416 Pages, Hardcover
Wiley & Sons Ltd

ISBN: 978-1-119-53215-6
John Wiley & Sons

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A systematic approach to profit optimization utilizing strategic solutions and methodologies for the chemical process industry

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.

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
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.