A systematic approach to profit optimization utilizing strategic solutions and methodologies for the chemical process industryIn 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 xixTable List xxvPreface xxvii1 Concept of Profit Maximization 11.1 Introduction 11.2 Who is This Book Written for? 31.3 What is Profit Maximization and Sweating of Assets All About? 41.4 Need for Profit Maximization in Today's Competitive Market 71.5 Data Rich but Information Poor Status of Today's Process Industries 81.6 Emergence of Knowledge-Based Industries 91.7 How Knowledge and Data Can Be Used to Maximize Profit 9References 102 Big Picture of the Modern Chemical Industry 112.1 New Era of the Chemical Industry 112.2 Transition from a Conventional to an Intelligent Chemical Industry 112.3 How Will Digital Affect the Chemical Industry and Where Can the Biggest Impact Be Expected? 122.3.1 Attaining a New Level of Functional Excellence 122.3.1.1 Manufacturing 132.3.1.2 Supply Chain 142.3.1.3 Sales and Marketing 142.3.1.4 Research and Development 152.4 Using Advanced Analytics to Boost Productivity and Profitability in Chemical Manufacturing 152.4.1 Decreasing Downtime Through Analytics 162.4.2 Increase Profits with Less Resources 172.4.3 Optimizing the Whole Production Process 182.5 Achieving Business Impact with Data 192.5.1 Data's Exponential Growing Importance in Value Creation 192.5.2 Different Links in the Value Chain 202.5.2.1 The Insights Value Chain - Definitions and Considerations 212.6 From Dull Data to Critical Business Insights: The Upstream Processes 222.6.1 Generating and Collecting Relevant Data 222.6.2 Data Refinement is a Two-Step Iteration 232.7 From Valuable Data Analytics Results to Achieving Business Impact: The Downstream Activities 252.7.1 Turning Insights into Action 252.7.2 Developing Data Culture 252.7.3 Mastering Tasks Concerning Technology and Infrastructure as Well as Organization and Governance 25References 263 Profit Maximization Project (PMP) Implementation Steps 273.1 Implementing a Profit Maximization Project (PMP) 273.1.1 Step 1: Mapping the Whole Plant in Monetary Terms 273.1.2 Step 2: Assessment of Current Plant Conditions 273.1.3 Step 3: Assessment of the Base Control Layer of the Plant 283.1.4 Step 4: Assessment of Loss from the Plant 293.1.5 Step 5: Identification of Improvement Opportunity in Plant and Functional Design of PMP Applications 293.1.6 Step 6: Develop an Advance Process Monitoring Framework by Applying the Latest Data Analytics Tools 303.1.7 Step 7: Develop a Real-Time Fault Diagnosis System 303.1.8 Step 8: Perform a Maximum Capacity Test Run 303.1.9 Step 9: Develop and Implement Real-Time APC 313.1.10 Step 10: Develop a Data-Driven Offline Process Model for Critical Process Equipment 313.1.11 Step 11: Optimizing Process Operation with a Developed Model 323.1.12 Step 12: Modeling and Optimization of Industrial Reactors 323.1.13 Step 13: Maximize Throughput of All Running Distillation Columns 333.1.14 Step 14: Apply New Design Methodology for Process Equipment 33References 344 Strategy for Profit Maximization 354.1 Introduction 354.2 How is Operating Profit Defined in CPI? 364.3 Different Ways to Maximize Operating Profit 364.4 Process Cost Intensity 374.4.1 Definition of Process Cost Intensity 374.4.2 Concept of Cost Equivalent (CE) 394.4.3 Cost Intensity for a Total Site 394.5 Mapping the Whole Process in Monetary Terms and Gain Insights 404.6 Case Study of a Glycol Plant 404.7 Steps to Map the Whole Plant in Monetary Terms and Gain Insights 434.7.1 Step 1: Visualize the Plant as a Black Box 434.7.2 Step 2: Data Collection from a Data Historian and Preparation of Cost Data 464.7.3 Step 3: Calculation of Profit Margin 464.7.4 Step 4: Gain Insights from Plant Cost and Profit Data 484.7.5 Step 5: Generation of Production Cost and a Profit Margin Table for One Full Year 514.7.6 Step 6: Plot Production Cost and Profit Margin for One Full Year and Gain Insights 514.7.7 Step 7: Calculation of Relative Standard Deviations of each Parameter in order to Understand the Cause of Variability 524.7.8 Step 8: Cost Benchmarking 53Reference 545 Key Performance Indicators and Targets 555.1 Introduction 555.2 Key Indicators Represent Operation Opportunities 565.2.1 Reaction Optimization 565.2.2 Heat Exchanger Operation Optimization 585.2.3 Furnace Operation 585.2.4 Rotating Equipment Operation 595.2.5 Minimizing Steam Let down Flows 595.2.6 Turndown Operation 595.2.7 Housekeeping Aspects 595.3 Define Key Indicators 605.3.1 Process Analysis and Economics Analysis 615.3.2 Understand the Constraints 615.3.3 Identify Qualitatively Potential Area of Opportunities 655.4 Case Study of Ethylene Glycol Plant to Identify the Key Performance Indicator 665.4.1 Methodology 665.4.2 Ethylene Oxide Reaction Section 675.4.2.1 Understand the Process 675.4.2.2 Understanding the Economics of the Process 685.4.2.3 Factors that can Change the Production Cost and Overall Profit Generated from this Section 695.4.2.4 How is Production Cost Related to Process Parameters from the Standpoint of the Cause and Effect Relationship? 695.4.2.5 Constraints 695.4.2.6 Key Parameter Identifications 705.4.3 Cycle Water System 715.4.3.1 Main Purpose 715.4.3.2 Economics of the Process 715.4.3.3 Factors that can Change the Production Cost of this Section 725.4.3.4 Constraints 725.4.3.5 Key Performance Parameters 725.4.4 Carbon Dioxide Removal Section 735.4.4.1 Main Purpose 735.4.4.2 Economics 735.4.4.3 Factors that can Change the Production Cost of this Section 735.4.4.4 Constraints 745.4.4.5 Key Performance Parameters 745.4.5 EG Reaction and Evaporation Section 745.4.5.1 Main Purpose 745.4.5.2 Economics 755.4.5.3 Factors that can Change the Production Cost of this Section 765.4.5.4 Key Performance Parameters 765.4.6 EG Purification Section 765.4.6.1 Main Purpose 765.4.6.2 Economics 775.4.6.3 Key Performance Parameters 775.5 Purpose to Develop Key Indicators 775.6 Set up Targets for Key Indicators 785.7 Cost and Profit Dashboard 785.7.1 Development of Cost and Profit Dashboard to Monitor the Process Performance in Money Terms 785.7.2 Connecting Key Performance Indicators in APC 795.8 It is Crucial to Change the Viewpoints in Terms of Cost or Profit 80References 806 Assessment of Current Plant Status 836.1 Introduction 836.1.1 Data Extraction from a Data Historian 836.1.2 Calculate the Economic Performance of the Section 846.2 Monitoring Variations of Economic Process Parameters 906.3 Determination of the Effect of Atmosphere on the Plant Profitability 906.4 Capacity Variations 916.5 Assessment of Plant Reliability 916.6 Assessment of Profit Suckers and Identification of Equipment for Modeling and Optimization 916.7 Assessment of Process Parameters Having a High Impact on Profit 936.8 Comparison of Current Plant Performance Against Its Design 936.9 Assessment of Regulatory Control System Performance 946.9.1 Basic Assessment Procedure 966.10 Assessment of Advance Process Control System Performance 976.11 Assessment of Various Profit Improvement Opportunities 97References 987 Process Modeling by the Artificial Neural Network 997.1 Introduction 997.2 Problems to Develop a Phenomenological Model for Industrial Processes 1007.3 Types of Process Model 1017.3.1 First Principle-Based Model 1017.3.2 Data-Driven Models 1017.3.3 Grey Model 1017.3.4 Hybrid Model 1017.4 Emergence of Artificial Neural Networks as One of the Promising Data-Driven Modeling Techniques 1067.5 ANN-Based Modeling 1067.5.1 How Does ANN Work? 1067.5.2 Network Architecture 1077.5.3 Back-Propagation Algorithm (BPA) 1077.5.4 Training 1087.5.5 Generalizability 1107.6 Model Development Methodology 1107.6.1 Data Collection and Data Inspection 1107.6.2 Data Pre-processing and Data Conditioning 1107.6.2.1 Outlier Detection and Replacement 1127.6.2.2 Univariate Approach to Detect Outliers 1127.6.2.3 Multivariate Approach to Detect Outliers 1127.6.3 Selection of Relevant Input-Output Variables 1137.6.4 Align Data 1137.6.5 Model Parameter Selection, Training, and Validation 1137.6.6 Model Acceptance and Model Tuning 1157.7 Application of ANN Modeling Techniques in the Chemical Process Industry 1157.8 Case Study: Application of the ANN Modeling Technique to Develop an Industrial Ethylene Oxide Reactor Model 1167.8.1 Origin of the Present Case Study 1167.8.2 Problem Definition of the Present Case Study 1177.8.3 Developing the ANN-Based Reactor Model 1197.8.4 Identifying Input and Output Parameters 1197.8.5 Data Collection 1207.8.6 Neural Regression 1217.8.7 Results and Discussions 1227.9 Matlab Code to Generate the Best ANN Model 124References 1258 Optimization of Industrial Processes and Process Equipment 1318.1 Meaning of Optimization in an Industrial Context 1318.2 How Can Optimization Increase Profit? 1328.3 Types of Optimization 1338.3.1 Steady-State Optimization 1338.3.2 Dynamic Optimization 1338.4 Different Methods of Optimization 1348.4.1 Classical Method 1348.4.2 Gradient-Based Methods of Optimization 1348.4.3 Non-traditional Optimization Techniques 1358.5 Brief Historical Perspective of Heuristic-based Non-traditional Optimization Techniques 1368.6 Genetic Algorithm 1388.6.1 What is Genetic Algorithm? 1388.6.2 Foundation of Genetic Algorithms 1388.6.3 Five Phases of Genetic Algorithms 1408.6.3.1 Initial Population 1408.6.3.2 Fitness Function 1408.6.3.3 Selection 1408.6.3.4 Crossover 1408.6.3.5 Termination 1418.6.4 The Problem Definition 1418.6.5 Calculation Steps of GA 1418.6.5.1 Step 1: Generating Initial Population by Creating Binary Coding 1418.6.5.2 Step 2: Evaluation of Fitness 1428.6.5.3 Step 3: Selecting the Next Generation's Population 1428.6.6 Advantages of GA Against Classical Optimization Techniques 1448.7 Differential Evolution 1458.7.1 What is Differential Evolution (DE)? 1458.7.2 Working Principle of DE 1458.7.3 Calculation Steps Performed in DE 1458.7.4 Choice of DE Key Parameters (NP, F, and CR) 1458.7.5 Stepwise Calculation Procedure for DE implementation 1468.8 Simulated Annealing 1498.8.1 What is Simulated Annealing? 1498.8.2 Procedure 1498.8.3 Algorithm 1508.9 Case Study: Application of the Genetic Algorithm Technique to Optimize the Industrial Ethylene Oxide Reactor 1518.9.1 Conclusion of the Case Study 1528.10 Strategy to Utilize Data-Driven Modeling and Optimization Techniques to Solve Various Industrial Problems and Increase Profit 153References 1559 Process Monitoring 1599.1 Need for Advance Process Monitoring 1599.2 Current Approaches to Process Monitoring and Diagnosis 1609.3 Development of an Online Intelligent Monitoring System 1619.4 Development of KPI-Based Process Monitoring 1619.5 Development of a Cause and Effect-Based Monitoring System 1639.6 Development of Potential Opportunity-Based Dash Board 1639.6.1 Development of Loss and Waste Monitoring Systems 1649.6.2 Development of a Cost-Based Monitoring System 1659.6.3 Development of a Constraints-Based Monitoring System 1669.7 Development of Business Intelligent Dashboards 1669.8 Development of Process Monitoring System Based on Principal Component Analysis 1679.8.1 What is a Principal Component Analysis? 1689.8.2 Why Do We Need to Rotate the Data? 1699.8.3 How Do We Generate Principal Components? 1709.8.4 Steps to Calculating the Principal Components 1709.9 Case Study for Operational State Identification and Monitoring Using PCA 1719.9.1 Case Study 1: Monitoring a Reciprocating Reclaim Compressor 171References 17410 Fault Diagnosis 17710.1 Challenges to the Chemical Industry 17710.2 What is Fault Diagnosis? 17810.3 Benefit of a Fault Diagnosis System 17910.3.1 Characteristic of an Automated Fault Diagnosis System 18010.4 Decreasing Downtime Through a Fault Diagnosis Type Data Analytics 18010.5 User Perspective to Make an Effective Fault Diagnosis System 18110.6 How Are Fault Diagnosis Systems Made? 18310.6.1 Principal Component-Based Approach 18410.6.2 Artificial Neural Network-Based Approach 18410.7 A Case Study to Build a Robust Fault Diagnosis System 18510.7.1 Challenges to a Build Fault Diagnosis of an Ethylene Oxide Reactor System 18710.7.2 PCA-Based Fault Diagnosis of an EO Reactor System 18710.7.3 Acquiring Historic Process Data Sets to Build a PCA Model 18810.7.4 Criteria of Selection of Input Parameters for PCA 18910.7.5 How PCA Input Data is Captured in Real Time 19110.7.6 Building the Model 19210.7.6.1 Calculations of the Principal Components 19210.7.6.2 Calculations of Hotelling's T² 19210.7.6.3 Calculations of the Residual 19310.7.7 Creation of a PCA Plot for Training Data 19310.7.8 Creation of Hotelling's T² Plot for the Training Data 19410.7.9 Creation of a Residual Plot for the Training Data 19410.7.10 Creation of an Abnormal Zone in the PCA Plot 19410.7.11 Implementing the PCA Model in Real Time 19410.7.12 Detecting Whether the Plant is Running Normally or Abnormally on a Real-Time Basis 19510.7.13 Use of a PCA Plot During Corrective Action in Real Time 19710.7.14 Validity of a PCA Model 19810.7.14.1 Time-Varying Characteristic of an EO Catalyst 19810.7.14.2 Capturing the Efficiency of the PCA Model Using the Residual Plot 19910.7.15 Quantitive Decision Criteria Implemented for Retraining of an Ethylene Oxide (EO) Reactor PCA Model 20010.7.16 How Retraining is Practically Executed 20010.8 Building an ANN Model for Fault Diagnosis of an EO Reactor 20010.8.1 Acquiring Historic Process Data Sets to Build an ANN Model 20010.8.2 Identification of Input and Output Parameters 20110.8.3 Building of an ANN-Based EO Reactor Model 20110.8.3.1 Complexity of EO Reactor Modeling 20110.8.3.2 Model Building 20210.8.4 Prediction Performance of an ANN Model 20310.8.5 Utilization of an ANN Model for Fault Detection 20310.8.6 How Do PCA Input Data Relate to ANN Input/Output Data? 20410.8.7 Retraining of an ANN Model 20610.9 Integrated Robust Fault Diagnosis System 20610.10 Advantages of a Fault Diagnosis System 208References 20811 Optimization of an Existing Distillation Column 20911.1 Strategy to Optimize the Running Distillation Column 20911.1.1 Strategy 20911.2 Increase the Capacity of a Running Distillation Column 21011.3 Capacity Diagram 21111.4 Capacity Limitations of Distillation Columns 21211.5 Vapour Handling Limitations 21411.5.1 Flow Regimes - Spray and Froth 21411.5.2 Entrainment 21511.5.3 Tray Flooding 21511.5.4 Ultimate Capacity 21711.6 Liquid Handling Limitations 21711.6.1 Downcomer Flood 21711.6.2 Downcomer Residence Time 21711.6.3 Downcomer Froth Back-Up% 21911.6.4 Downcomer Inlet Velocity 22011.6.5 Weir liquid loading 22111.6.6 Downcomer Sizing Criteria 22111.7 Other Limitations and Considerations 22111.7.1 Weeping 22111.7.2 Dumping 22211.7.3 Tray Turndown 22211.7.4 Foaming 22311.8 Understanding the Stable Operation Zone 22311.9 Case Study to Develop a Capacity Diagram 22411.9.1 Calculation of Capacity Limits 22411.9.1.1 Spray Limit 22411.9.1.2 Vapor Flooding Limit 22611.9.1.3 Downcomer Backup Limit 22611.9.1.4 Maximum Liquid Loading Limit 22711.9.1.5 Minimum Liquid Loading Limit 22711.9.1.6 Minimum Vapor Loading Limit 22811.9.2 Plotting a Capacity Diagram 22811.9.3 Insights from the Capacity Diagram 22911.9.4 How Can the Capacity Diagram Be Used for Profit Maximization? 229References 23012 New Design Methodology 23112.1 Need for New Design Methodology 23112.2 Case Study of the New Design Methodology for a Distillation Column 23112.2.1 Traditional Way to Design a Distillation Column 23112.2.2 Background of the Distillation Column Design 23212.3 New Intelligent Methodology for Designing a Distillation Column 23412.4 Problem Description of the Case Study 23712.5 Solution Procedure Using the New Design Methodology 23712.6 Calculations of the Total Cost 23812.7 Search Optimization Variables 23912.8 Operational and Hydraulic Constraints 23912.9 Particle Swarm Optimization 24112.9.1 PSO Algorithm 24112.10 Simulation and PSO Implementation 24212.11 Results and Analysis 24312.12 Advantages of PSO 24512.13 Advantages of New Methodology over the Traditional Approach 24612.14 Conclusion 248Nomenclature 248References 250Appendix 12.1 25113 Genetic Programing for Modeling of Industrial Reactors 25913.1 Potential Impact of Reactor Optimization on Overall Profit 25913.2 Poor Knowledge of Reaction Kinetics of Industrial Reactors 25913.3 ANN as a Tool for Reactor Kinetic Modeling 26013.4 Conventional Methods for Evaluating Kinetics 26013.5 What is Genetic Programming? 26113.6 Background of Genetic Programming 26213.7 Genetic Programming at a Glance 26313.7.1 Preparatory Steps of Genetic Programming 26413.7.2 Executional Steps of Genetic Programming 26413.7.3 Creating an Individual 26713.7.4 Fitness Test 26813.7.5 The Genetic Operations 26913.7.6 User Decisions 27113.7.7 Computing Resources 27213.8 Example Genetic Programming Run 27213.8.1 Preparatory Steps 27313.8.2 Step-by-Step Sample Run 27413.8.3 Selection, Crossover, and Mutation 27513.9 Case Studies 27713.9.1 Case Study 1 27713.9.2 Case Study 2 27813.9.3 Case Study 3 27913.9.4 Case Study 4 280References 28114 Maximum Capacity Test Run and Debottlenecking Study 28314.1 Introduction 28314.2 Understanding Different Safety Margins in Process Equipment 28314.3 Strategies to Exploit the Safety Margin 28414.4 Capacity Expansion versus Efficiency Reduction 28514.5 Maximum Capacity Test Run: What is it All About? 28614.6 Objective of a Maximum Capacity Test Run 28714.7 Bottlenecks of Different Process Equipment 28814.7.1 Functional Bottleneck 28814.7.2 Reliability Bottleneck 28814.7.3 Safety Interlock Bottleneck 29014.8 Key Steps to Carry Out a Maximum Capacity Test Run in a Commercial Running Plant 29114.8.1 Planning 29114.8.2 Discussion with Technical People 29614.8.3 Risk and Opportunity 29614.8.4 Dos and Don'ts 29714.8.5 Simulations 29814.8.6 Preparations 29914.8.7 Management of Change 29914.8.8 Execution 30014.8.9 Data Collections 30014.8.10 Critical Observations 30214.8.11 Report Preparations 30314.8.12 Detailed Simulations and Assembly of All Observations 30314.8.13 Final Report Preparation 30414.9 Scope and Phases of a Detailed Improvement Study 30414.9.1 Improvement Scoping Study 30514.9.2 Detail Feasibility Study 30514.9.3 Retrofit Design Phase 30514.10 Scope and Limitations of MCTR 30614.10.1 Scope 30614.10.2 Two Big Benefits of Doing MCTR 30614.10.3 Limitations of MCTR 30615 Loss Assessment 30915.1 Different Losses from the System 30915.2 Strategy to Reduce the Losses andWastages 30915.3 Money Loss Audit 31015.4 Product or Utility Losses 31215.4.1 Loss in the Drain 31215.4.2 Loss Due to Vent and Flaring 31315.4.3 Utility Loss 31415.4.4 Heat Loss Assessment for the Fired Heater 31415.4.5 Heat Loss Assessment for the Distillation Column 31515.4.6 Heat Loss Assessment for Steam Leakage 31615.4.7 Heat Loss Assessment for Condensate Loss 31716 Advance Process Control 31916.1 What is Advance Process Control? 31916.2 Why is APC Necessary to Improve Profit? 32016.3 Why APC is Preferred over Normal PID Regulatory Control 32216.4 Position of APC in the Control Hierarchy 32416.5 Which are the Plants where Implementations of APC were Proven Very Profitable? 32716.6 How do Implementations of APC Increase Profit? 32816.7 How does APC Extract Benefits? 33016.8 Application of APC in Oil Refinery, Petrochemical, Fertilizer and Chemical Plants and Related Benefits 33416.9 Steps to Execute an APC Project 33616.9.1 Step 1: Preliminary Cost -Benefit Analysis 33616.9.2 Step 2: Assessment of Base Control Loops 33716.9.3 Step 3: Functional Design of the Controller 33716.9.4 Step 4: Conduct the Plant Step Test 33816.9.5 Step 5: Generate a Process Model 33816.9.6 Step 6: Commission the Online Controller 33816.9.7 Step 7: Online APC Controller Tuning 33916.10 How Can an Effective Functional Design Be Done? 33916.10.1 Step 1: Define Process Control Objectives 34016.10.2 Step 2: Identification of Process Constraints 34216.10.3 Step 3: Define Controller Scope 34316.10.4 Step 4: Variable Selection 34416.10.5 Step 5: Rectify Regulatory Control Issues 34616.10.6 Step 6: Explore the Scope of Inclusions of Inferential Calculations 34716.10.7 Step 7: Evaluate Potential Optimization Opportunity 34716.10.8 Step 8: Define LP or QP Objective Function 348References 34917 150 Ways and Best Practices to Improve Profit in Running Chemical Plant 35117.1 Best Practices Followed in Leading Process Industries Around the World 35117.2 Best Practices Followed in a Steam and Condensate System 35117.3 Best Practices Followed in Furnaces and Boilers 35517.4 Best Practices Followed in Pumps, Fans, and Compressor 35717.5 Best Practices Followed in Illumination Optimization 35917.6 Best Practices in Operational Improvement 35917.7 Best Practices Followed in Air and Nitrogen Header 36017.8 Best Practices Followed in Cooling Tower and CoolingWater 36117.9 Best Practices Followed inWater Conservation 36217.10 Best Practices Followed in Distillation Column and Heat Exchanger 36317.11 Best Practices in Process Improvement 36417.12 Best Practices in Flare Gas Reduction 36517.13 Best Practices in Product or Energy Loss Reduction 36517.14 Best Practices to Monitor Process Control System Performance 36617.15 Best Practices to Enhance Plant Reliability 36717.16 Best Practices to Enhance Human Resource 36817.17 Best Practices to Enhance Safety, Health, and the Environment 36817.18 Best Practices to Use New Generation Digital Technology 36917.19 Best Practices to Focus a Detailed Study and R&D Effort 370Index 373