Full Scale Plant Optimization in Chemical Engineering: A Practical Guide

Description

Full Scale Plant Optimization in Chemical Engineering Highlights the basic principles and applications of the primary three methods in plant and process optimization for responsible operators and engineers. Chemical engineers are a vital part of the creation of any process development―lab-scale and pilot-scale―for any plant. In fact, they are the lynchpin of later efforts to scale-up and full-scale plant process improvement. As these engineers approach a new project, there are three generally recognized methodologies that are applicable in industry generally: Design of Experiments (DOE), Evolutionary Operations (EVOP), and Data Mining Using Neural Networks (DM). In Full Scale Plant Optimization in Chemical Engineering, experienced chemical engineer Živorad R. Laziċ offers an in-depth analysis and comparison of these three methods in full-scale plant optimization applications. The book is designed to provide the basic principles and necessary information for complete understanding of these three methods (DOE, EVOP, and DM). The application of each method is fully described. Full Scale Plant Optimization in Chemical Engineering readers will also find: A thorough discussion of the advantages, disadvantages and applications for the five different EVOP methods (BEVOP, ROVOP, REVOP, QSEVOP & SEVOP) with examples and simulations An overview of EVOP tools that responsible operators and engineers utilize in deciding which EVOP method is the most appropriate for the certain type of the process Particular attention is given to the simple but powerful technique Evolutionary Operation or EVOP, which provides the experimental tools for the full scale plant optimization Full Scale Plant Optimization in Chemical Engineering is a useful reference for all chemists in industry, chemical engineers, pharmaceutical chemists, and process engineers. Cover Title Page Copyright Contents Preface Biography Chapter 1 The Basic Ideas 1.1 Introduction Chapter 2 Design of Experiments – DOE 2.1 The 22 Factorial Designs 2.2 Effects for 22 Factorial Designs 2.3 Interactions Between Factors 2.4 Standard Error for the Effects 2.5 23 Factorial Design 2.6 Effects for the 23 Factorial Designs 2.7 Standard Errors of Effects for Two‐ and Three‐Level Factorial Designs Chapter 3 Neural Network Modeling – Data Mining 3.1 Data Preprocessing 3.2 Building, Training, and Verifying Model 3.3 Model Analyzing 3.4 What‐Ifs Optimization 3.5 DOE Experiment Using Neural Networks Model Chapter 4 Evolutionary Operation – EVOP 4.1 Small‐Scale and Plant‐Scale Investigation 4.2 Scale‐up 4.3 Static and Evolutionary Operation 4.4 Analysis of Information Board 4.5 Three‐Factor Scheme 4.6 Current Best‐Known Conditions 4.7 Change in Mean for a 22 Factorial Design with Center Point 4.8 Standard Errors for the Effects 4.9 The Effects and Their Standard Errors for a 22 Design with Center Point 4.10 Analysis of Information Board for Three Responses Using Factorial Effects 4.11 23 Factorial Design Effects, Interpretation, and Information Board 4.11.1 An Estimate of Standard Deviation 4.12 Dividing the 23 Factorial Design Into Two Blocks 4.13 23 Design with Two Center Points Run in Two Blocks 4.13.1 Two Standard Error Limits for the Overall Change in Mean Chapter 5 Different Techniques of EVOP 5.1 Box EVOP – BEVOP 5.2 Calculation Procedure for Two‐Factor EVOP 5.3 Calculation Procedure for Three‐Factor EVOP 5.4 BEVOP in Plant‐Scale Experiments 5.5 BEVOP Applications 5.6 BEVOP Advantages and Disadvantages 5.7 BEVOP Simulation 5.7.1 22 BEVOP Simulation 5.7.1.1 Simulation No. 1 5.7.1.2 Simulation No. 2: 22 BEVOP 5.7.1.3 Simulation No. 3: 22 BEVOP 5.7.2 23 BEVOP Simulation 5.7.2.1 Simulation No. 4 5.8 Rotating Square Evolutionary Operation – ROVOP 5.8.1 22 ROVOP 5.8.2 Method of Analysis 5.8.3 22 ROVOP Simulation 5.8.3.1 Simulation No. 5 5.8.4 23 ROVOP Simulation 5.8.4.1 Simulation No. 6: 23 ROVOP 5.9 Random Evolutionary Operation – REVOP 5.9.1 REVOP Simulation 5.9.1.1 Simulation No. 7 5.10 Quick‐Start EVOP – QSEVOP 5.10.1 The way QSEVOP works 5.10.2 How to Recover From “Hang‐ups” 5.11 QSEVOP Simulation 5.11.1 Simulation No. 8 5.12 Simplex Evolutionary Operation – SEVOP 5.12.1 The Basic Simplex Method 5.12.2 Simplex Evolutionary Operation – SEVOP 5.12.2 Procedure to calculate the new run value 5.12.3 SEVOP Simulation 5.12.3.1 Simulation S‐9 5.12.3.2 Simulation S‐10 5.13 Some Practical Advice About Using EVOP Chapter 6 EVOP Software Appendix A The Approximate Method of Estimating the Standard Deviation in EVOP Appendix B 22‐ and 23‐Factor Box EVOP Calculations with Center Point B.1 23 Three‐Factor Box EVOP Calculations with Center Point Appendix C Short Table of Random Normal Deviates Appendix D How Many Cycles Are Necessary to Detect Effects of Reasonable Size Appendix E Multiple Responses: The Desirability Approach References Index EULA