Sandip K. Lahiri - Profit Maximization Techniques for Operating Chemical Plants

1 Cover

2 Figure List

3 Table List

4 Preface Overview of Contents

5 1 Concept of Profit Maximization1.1 Introduction 1.2 Who is This Book Written for? 1.3 What is Profit Maximization and Sweating of Assets All About? 1.4 Need for Profit Maximization in Today's Competitive Market 1.5 Data Rich but Information Poor Status of Today's Process Industries 1.6 Emergence of Knowledge‐Based Industries 1.7 How Knowledge and Data Can Be Used to Maximize Profit References

6 2 Big Picture of the Modern Chemical Industry2.1 New Era of the Chemical Industry 2.2 Transition from a Conventional to an Intelligent Chemical Industry 2.3 How Will Digital Affect the Chemical Industry and Where Can the Biggest Impact Be Expected? 2.4 Using Advanced Analytics to Boost Productivity and Profitability in Chemical Manufacturing 2.5 Achieving Business Impact with Data 2.6 From Dull Data to Critical Business Insights: The Upstream Processes 2.7 From Valuable Data Analytics Results to Achieving Business Impact: The Downstream Activities References

7 3 Profit Maximization Project (PMP) Implementation Steps3.1 Implementing a Profit Maximization Project (PMP) References

8 4 Strategy for Profit Maximization4.1 Introduction 4.2 How is Operating Profit Defined in CPI? 4.3 Different Ways to Maximize Operating Profit 4.4 Process Cost Intensity 4.5 Mapping the Whole Process in Monetary Terms and Gain Insights 4.6 Case Study of a Glycol Plant 4.7 Steps to Map the Whole Plant in Monetary Terms and Gain Insights Reference

9 5 Key Performance Indicators and Targets 5.1 Introduction 5.2 Key Indicators Represent Operation Opportunities 5.3 Define Key Indicators 5.4 Case Study of Ethylene Glycol Plant to Identify the Key Performance Indicator 5.5 Purpose to Develop Key Indicators 5.6 Set up Targets for Key Indicators 5.7 Cost and Profit Dashboard 5.8 It is Crucial to Change the Viewpoints in Terms of Cost or Profit References

10 6 Assessment of Current Plant Status 6.1 Introduction 6.2 Monitoring Variations of Economic Process Parameters 6.3 Determination of the Effect of Atmosphere on the Plant Profitability 6.4 Capacity Variations 6.5 Assessment of Plant Reliability 6.6 Assessment of Profit Suckers and Identification of Equipment for Modeling and Optimization 6.7 Assessment of Process Parameters Having a High Impact on Profit 6.8 Comparison of Current Plant Performance Against Its Design 6.9 Assessment of Regulatory Control System Performance 6.10 Assessment of Advance Process Control System Performance 6.11 Assessment of Various Profit Improvement Opportunities References

11 7 Process Modeling by the Artificial Neural Network7.1 Introduction 7.2 Problems to Develop a Phenomenological Model for Industrial Processes 7.3 Types of Process Model 7.4 Emergence of Artificial Neural Networks as One of the Promising Data‐Driven Modeling Techniques 7.5 ANN‐Based Modeling 7.6 Model Development Methodology 7.7 Application of ANN Modeling Techniques in the Chemical Process Industry 7.8 Case Study: Application of the ANN Modeling Technique to Develop an Industrial Ethylene Oxide Reactor Model 7.9 Matlab Code to Generate the Best ANN Model References Appendix 7.1 Matlab Code to Generate the Best ANN Model

12 8 Optimization of Industrial Processes and Process Equipment8.1 Meaning of Optimization in an Industrial Context 8.2 How Can Optimization Increase Profit? 8.3 Types of Optimization 8.4 Different Methods of Optimization 8.5 Brief Historical Perspective of Heuristic‐based Non‐traditional Optimization Techniques 8.6 Genetic Algorithm 8.7 Differential Evolution 8.8 Simulated Annealing 8.9 Case Study: Application of the Genetic Algorithm Technique to Optimize the Industrial Ethylene Oxide Reactor 8.10 Strategy to Utilize Data‐Driven Modeling and Optimization Techniques to Solve Various Industrial Problems and Increase Profit References Appendix 8.1 Matlab Code for GA Optimization of an EO Reactor Case Study

13 9 Process Monitoring9.1 Need for Advance Process Monitoring 9.2 Current Approaches to Process Monitoring and Diagnosis 9.3 Development of an Online Intelligent Monitoring System 9.4 Development of KPI‐Based Process Monitoring 9.5 Development of a Cause and Effect‐Based Monitoring System 9.6 Development of Potential Opportunity‐Based Dash Board 9.7 Development of Business Intelligent Dashboards 9.8 Development of Process Monitoring System Based on Principal Component Analysis 9.9 Case Study for Operational State Identification and Monitoring Using PCA References

14 10 Fault Diagnosis10.1 Challenges to the Chemical Industry 10.2 What is Fault Diagnosis? 10.3 Benefit of a Fault Diagnosis System 10.4 Decreasing Downtime Through a Fault Diagnosis Type Data Analytics 10.5 User Perspective to Make an Effective Fault Diagnosis System 10.6 How Are Fault Diagnosis Systems Made? 10.7 A Case Study to Build a Robust Fault Diagnosis System 10.8 Building an ANN Model for Fault Diagnosis of an EO Reactor 10.9 Integrated Robust Fault Diagnosis System 10.10 Advantages of a Fault Diagnosis System References

15 11 Optimization of an Existing Distillation Column11.1 Strategy to Optimize the Running Distillation Column 11.2 Increase the Capacity of a Running Distillation Column 11.3 Capacity Diagram 11.4 Capacity Limitations of Distillation Columns 11.5 Vapour Handling Limitations 11.6 Liquid Handling Limitations 11.7 Other Limitations and Considerations 11.8 Understanding the Stable Operation Zone (Zhu, 2013) 11.9 Case Study to Develop a Capacity Diagram References

16 12 New Design Methodology12.1 Need for New Design Methodology 12.2 Case Study of the New Design Methodology for a Distillation Column 12.3 New Intelligent Methodology for Designing a Distillation Column 12.4 Problem Description of the Case Study 12.5 Solution Procedure Using the New Design Methodology 12.6 Calculations of the Total Cost 12.7 Search Optimization Variables 12.8 Operational and Hydraulic Constraints 12.9 Particle Swarm Optimization 12.10 Simulation and PSO Implementation 12.11 Results and Analysis 12.12 Advantages of PSO 12.13 Advantages of New Methodology over the Traditional Approach (Lahiri, 2014) 12.14 Conclusion Nomenclature References

17 13 Genetic Programing for Modeling of Industrial Reactors13.1 Potential Impact of Reactor Optimization on Overall Profit 13.2 Poor Knowledge of Reaction Kinetics of Industrial Reactors 13.3 ANN as a Tool for Reactor Kinetic Modeling 13.4 Conventional Methods for Evaluating Kinetics 13.5 What is Genetic Programming? 13.6 Background of Genetic Programming (Searson et al., 2011) 13.7 Genetic Programming at a Glance (Koza, 1992; Koza and Rice, 1992; Koza et al., 1999) 13.8 Example Genetic Programming Run 13.9 Case Studies References

18 14 Maximum Capacity Test Run and Debottlenecking Study14.1 Introduction 14.2 Understanding Different Safety Margins in Process Equipment 14.3 Strategies to Exploit the Safety Margin 14.4 Capacity Expansion versus Efficiency Reduction 14.5 Maximum Capacity Test Run: What is it All About? 14.6 Objective of a Maximum Capacity Test Run 14.7 Bottlenecks of Different Process Equipment 14.8 Key Steps to Carry Out a Maximum Capacity Test Run in a Commercial Running Plant 14.9 Scope and Phases of a Detailed Improvement Study 14.10 Scope and Limitations of MCTR

19 15 Loss Assessment15.1 Different Losses from the System 15.2 Strategy to Reduce the Losses and Wastages 15.3 Money Loss Audit 15.4 Product or Utility Losses

20 16 Advance Process Control16.1 What is Advance Process Control? 16.2 Why is APC Necessary to Improve Profit? 16.3 Why APC is Preferred over Normal PID Regulatory Control (Lahiri, 2017c) 16.4 Position of APC in the Control Hierarchy (Lahiri, 2017c) 16.5 Which are the Plants where Implementations of APC were Proven Very Profitable? 16.6 How do Implementations of APC Increase Profit? 16.7 How does APC Extract Benefits? 16.8 Application of APC in Oil Refinery, Petrochemical, Fertilizer and Chemical Plants and Related Benefits 16.9 Steps to Execute an APC Project (Lahiri, 2017d) 16.10 How Can an Effective Functional Design Be Done? References