Full Scale Plant Optimization in Chemical Engineering

A Practical Guide
1. Auflage 2022
ISBN: 978-3-527-35038-4
Verlag: WILEY-VCH

Buch, Englisch, 272 Seiten, Format (B × H): 170 mm x 244 mm, Gewicht: 680 g

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. Lazic 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.

Autoren/Hrsg.

Lazic, Zivorad R.

Fachgebiete

• Technische Wissenschaften Umwelttechnik | Umwelttechnologie Umwelttechnik
• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Chemische Verfahrenstechnik
• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Verfahrenstechnik, Chemieingenieurwesen

Inhaltsverzeichnis

Preface
I Basic Ideas
1.1 Introduction
 
II Design of Experiments (DOE)
2.1 22 Factorial Designs
2.2 Effects for the 22 Factorial Designs
2.3 Interactions Between Factors
2.4 Standard Error for the Effects
2.5 The 23 Factorial Design
2.6 Effects for the 23 Factorial Designs
2.7 Standard Errors of Effects for Two-Level Factorial Designs
 
III Neural Network Modeling-Data Mining
3.1 Data Preprocessing
3.2 Building, Training and Verifying the Model
3.3 Analyzing the Model
3.4 What-Ifs Optimization
3.5 DOE Experiment Using Neural Networks Model
 
IV 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 the Information Board
4.5 Three Factors 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 the Information Board for Three Responses Using the Factorial Effects
4.11 23 Factorial Design Effects, Interpretation and Information Board
4.12 Dividing the 23 Factorial Design Into Two Blocks
4.13 23 Design with Two Center Points Run in Two Blocks
 
V Different Techniques of EVOP
5.1 Box EVOP-BEVOP
5.2 Calculation Procedure for Two Factors EVOP
5.3 Conclusions from the Information Board
5.4 Calculation Procedure for the Three Factors EVOP
5.5 BEVOP in Plant-Scale Experiments
5.6 BEVOP Application
5.7 BEVOP Advantages & Disadvantages
5.8 BEVOP Simulation
5.8.1 22 BEVOP Simulation
5.8.2 23 BEVOP Simulation
5.9 Rotating Square Evolutionary Operation-ROVOP
5.9.1 22 ROVOP Simulation
5.9.2 Method of Analysis
5.9.3 22 ROVOP Simulation
5.9.4 23 ROVOP Simulation
5.10 Random Evolutionary Operation-REVOP
5.10.1 REVOP Simulation
5.11 Quick Start EVOP-QSEVOP
5.11.1 QSEVOP Simulation
5.12 Simplex Evolutionary Operation-SEVOP
5.12.1 The Basic Simplex Method
5.12.2 Simplex Evolutionary Operation-SEVOP
5.12.3 SEVOP Simulation
5.13 Some Practical Advice About Using EVOP
 
VI EVOP Software
 
VII. Appendix
A-I The Approximate Method of Estimating the Standard Deviation in EVOP
A-II 22 Two Factors Box EVOP Calculations with the Center Point
A-III Short Table of Random Normal Deviates
A-IV How Many Cycles Are Necessary to Detect Effects of Reasonable Size

Preface
I Basic Ideas
1.1 Introduction
 
II Design of Experiments (DOE)
2.1 22 Factorial Designs
2.2 Effects for the 22 Factorial Designs
2.3 Interactions Between Factors
2.4 Standard Error for the Effects
2.5 The 23 Factorial Design
2.6 Effects for the 23 Factorial Designs
2.7 Standard Errors of Effects for Two-Level Factorial Designs
 
III Neural Network Modeling-Data Mining
3.1 Data Preprocessing
3.2 Building, Training and Verifying the Model
3.3 Analyzing the Model
3.4 What-Ifs Optimization
3.5 DOE Experiment Using Neural Networks Model
 
IV 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 the Information Board
4.5 Three Factors 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 the Information Board for Three Responses Using the Factorial Effects
4.11 23 Factorial Design Effects, Interpretation and Information Board
4.12 Dividing the 23 Factorial Design Into Two Blocks
4.13 23 Design with Two Center Points Run in Two Blocks
 
V Different Techniques of EVOP
5.1 Box EVOP-BEVOP
5.2 Calculation Procedure for Two Factors EVOP
5.3 Conclusions from the Information Board
5.4 Calculation Procedure for the Three Factors EVOP
5.5 BEVOP in Plant-Scale Experiments
5.6 BEVOP Application
5.7 BEVOP Advantages & Disadvantages
5.8 BEVOP Simulation
5.8.1 22 BEVOP Simulation
5.8.2 23 BEVOP Simulation
5.9 Rotating Square Evolutionary Operation-ROVOP
5.9.1 22 ROVOP Simulation
5.9.2 Method of Analysis
5.9.3 22 ROVOP Simulation
5.9.4 23 ROVOP Simulation
5.10 Random Evolutionary Operation-REVOP
5.10.1 REVOP Simulation
5.11 Quick Start EVOP-QSEVOP
5.11.1 QSEVOP Simulation
5.12 Simplex Evolutionary Operation-SEVOP
5.12.1 The Basic Simplex Method
5.12.2 Simplex Evolutionary Operation-SEVOP
5.12.3 SEVOP Simulation
5.13 Some Practical Advice About Using EVOP
 
VI EVOP Software
 
VII. Appendix
A-I The Approximate Method of Estimating the Standard Deviation in EVOP
A-II 22 Two Factors Box EVOP Calculations with the Center Point
A-III Short Table of Random Normal Deviates
A-IV How Many Cycles Are Necessary to Detect Effects of Reasonable Size

Über Autor(innen)

Zivorad R. Lazic is Quality Assurance Manager at Lenzing Fibers Corporation, Lowland, Tennessee, USA. Born in Ljubovija, Serbia, he completed his studies at Belgrade University, writing his thesis under the supervision of Prof. Dragoljub Vukovic. He began his career in Belgrade at the Military Technical Institute (VTI), where he was Head of Department for R&D into composite rocket propellants. He was trained at Hercules Inc., McGregor, TX, and spent more than six years as Vice President for Process and Product Development in P.T. South Pacific Viscose, Indonesia from 1994 until 2001.
Dr. Lazic's interests include advanced statistical tools, DOE, SPC, EVOP, neural network modeling and the six-sigma approach.