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Profit Maximization Techniques for Operating Chemical Plants


Profit Maximization Techniques for Operating Chemical Plants


1. Aufl.

von: Sandip K. Lahiri

153,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 01.05.2020
ISBN/EAN: 9781119532170
Sprache: englisch
Anzahl Seiten: 416

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Beschreibungen

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

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