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Stochastic Process Optimization using Aspen Plus® [Kõva köide]

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  • Formaat: Hardback, 224 pages, kõrgus x laius: 234x156 mm, kaal: 612 g, 40 Illustrations, color; 148 Illustrations, black and white
  • Ilmumisaeg: 28-Aug-2017
  • Kirjastus: CRC Press Inc
  • ISBN-10: 1498785107
  • ISBN-13: 9781498785105
Teised raamatud teemal:
Stochastic Process Optimization using Aspen Plus®Suurem pilt
  • Formaat: Hardback, 224 pages, kõrgus x laius: 234x156 mm, kaal: 612 g, 40 Illustrations, color; 148 Illustrations, black and white
  • Ilmumisaeg: 28-Aug-2017
  • Kirjastus: CRC Press Inc
  • ISBN-10: 1498785107
  • ISBN-13: 9781498785105
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781498785105.html
Märksõnad:

Stochastic Process Optimization using Aspen® Plus

Bookshop Category: Chemical Engineering

Optimization can be simply defined as "choosing the best alternative among a set of feasible options". In all the engineering areas, optimization has a wide range of applications, due to the high number of decisions involved in an engineering environment. Chemical engineering, and particularly process engineering, is not an exception; thus stochastic methods are a good option to solve optimization problems for the complex process engineering models.

In this book, the combined use of the modular simulator Aspen® Plus and stochastic optimization methods, codified in MATLAB, is presented. Some basic concepts of optimization are first presented, then, strategies to use the simulator linked with the optimization algorithm are shown. Finally, examples of application for process engineering are discussed.

The reader will learn how to link the process simulator Aspen® Plus and stochastic optimization algorithms to solve process design problems. They will gain ability to perform multi-objective optimization in several case studies.

Key Features:

• The book links simulation and optimization through numerical analyses and stochastic optimization techniques

• Includes use of examples to illustrate the application of the concepts and specific guidance on the use of the softwares (Aspen® Plus, Excel, MATLB) to set up and solve models representing complex problems.

• Illustrates several examples of applications for the linking of simulation and optimization software with other packages for optimization purposes.

• Provides specific information on how to implement stochastic optimization with process simulators.

• Enable readers to identify practical and economic solutions to problems of industrial relevance, enhancing the safety, operation, environmental, and economic performance of chemical processes.

Preface xi
Acknowledgement xiii
Editors xv
Contributors xvii
1 Introduction to Optimization
1(14)
1.1 What Is Optimization?
1(1)
1.2 Mathematical Modeling and Optimization
2(2)
1.3 Classification of Optimization Problems
4(1)
1.4 Objective Function
5(2)
1.5 Optimization with Constraints: Feasible Region
7(1)
1.6 Multiobjective Optimization
8(4)
1.6.1 Weighted Sum Method
11(1)
1.6.2 Constraint Method
12(1)
1.7 Process Optimization
12(3)
References
14(1)
2 Deterministic Optimization
15(24)
2.1 Introduction
15(1)
2.2 Single-Variable Deterministic Optimization
15(4)
2.3 Continuity and Convexity
19(5)
2.4 Unconstrained Optimization
24(1)
2.5 Equality-Constrained Optimization
25(7)
2.5.1 Method of Lagrange Multipliers
26(3)
2.5.2 Generalized Reduced Gradient Method
29(3)
2.6 Equality- and Inequality-Constrained Optimization
32(4)
2.6.1 Active Set Strategy
33(3)
2.7 Software for Deterministic Optimization
36(3)
References
36(3)
3 Stochastic Optimization
39(16)
3.1 Introduction to Stochastic Optimization
39(1)
3.2 Stochastic Optimization vs. Deterministic Optimization
40(1)
3.3 Stochastic Optimization with Constraints
40(2)
3.4 Genetic Algorithms
42(1)
3.5 Differential Evolution
43(2)
3.6 Tabu Search
45(2)
3.7 Simulated Annealing
47(1)
3.8 Other Methods
48(7)
3.8.1 Ant Colony Optimization
49(1)
3.8.2 Particle Swarm Optimization
50(1)
3.8.3 Harmony Search
50(1)
References
51(4)
4 The Simulator Aspen Plus®
55(6)
4.1 Importance of Software for Process Analysis
55(1)
4.2 Characteristics of the Process Simulator Aspen Plus
56(2)
4.3 Sequential Modular Simulation
58(3)
References
59(2)
5 Direct Optimization in Aspen Plus®
61(46)
5.1 Optimization Methods
61(1)
5.2 Sensitivity Analysis Tools in Aspen Plus
62(1)
5.3 Sequential Quadratic Programming in Aspen Plus
62(1)
5.4 Optimization of a Heat Exchanger
63(13)
5.4.1 Description of the Problem
63(1)
5.4.2 Initial Simulation
63(5)
5.4.3 Optimization through Sensitivity Analysis
68(5)
5.4.4 Optimization through Sequential Quadratic Programming
73(3)
5.5 Optimization of a Flash Drum
76(15)
5.5.1 Description of the Problem
76(2)
5.5.2 Initial Simulation
78(5)
5.5.3 Optimization through Sensitivity Analysis
83(2)
5.5.4 Optimization through Sequential Quadratic Programming
85(6)
5.6 Optimization of a Tubular Reactor
91(16)
5.6.1 Description of the Problem
91(1)
5.6.2 Initial Simulation
92(6)
5.6.3 Optimization through Sensitivity Analysis
98(5)
5.6.4 Optimization through Sequential Quadratic Programming
103(3)
References
106(1)
6 Optimization using Aspen Plus® and Stochastic Toolbox
107(18)
6.1 Introduction
107(1)
6.2 Software for Stochastic Optimization
107(2)
6.3 Linking Aspen Plus with the Stochastic Optimization Software
109(9)
6.3.1 Creating a Function to be Optimized with MATLAB
110(1)
6.3.2 Creating a Subroutine in Microsoft Excel
111(7)
6.4 Mono-Objective Optimization of a Multicomponent Distillation Column
118(1)
6.5 Multi-Objective Optimization of a Multicomponent Distillation Column
119(3)
6.6 Conclusions
122(3)
References
123(2)
7 Using External User-Defined Block Model in Aspen Plus®
125(16)
7.1 Introduction
125(1)
7.2 Importance of User-Defined Block Models
125(1)
7.3 Previous Work and Loading a User-Defined Block Model in Aspen Plus
126(4)
7.4 Linking User-Defined Block Model with Microsoft Excel and MATLAB
130(8)
7.5 Conclusions
138(3)
References
139(2)
8 Optimization with a User Kinetic Model
141(28)
8.1 Introduction
141(1)
8.2 Kinetic Models Allowed in Aspen Plus
141(2)
8.3 Developing a User Kinetic Model
143(6)
8.4 Loading a User Kinetic Model in Aspen Plus
149(3)
8.5 Optimization of a Reactive Distillation Column with a User Kinetic Model
152(7)
8.6 Reactive Distillation Column with a Default Kinetic Model
159(7)
8.7 Conclusions
166(3)
References
166(3)
9 Optimization of a Biobutanol Production Process
169(24)
9.1 Introduction
169(2)
9.2 Description of the Process
171(2)
9.3 Thermodynamics and Kinetic Model
173(13)
9.4 Optimization Process
186(2)
9.5 Optimization Results
188(2)
9.6 Conclusions
190(3)
References
191(2)
10 Optimization of a Silane Production Process
193(26)
10.1 Introduction
193(2)
10.2 Silane Production
195(1)
10.3 Description of the Process Using Reactive Distillation
196(2)
10.4 Economic Potential of Reactive Distillation Production of Silane
198(1)
10.5 Thermodynamics and Kinetic Model
198(2)
10.6 Initial Design
200(12)
10.6.1 Buildup of the Initial Column
201(11)
10.7 Process Optimization
212(4)
10.7.1 Economic Objective Function
212(1)
10.7.2 Environmental Objective Function
212(1)
10.7.3 Global Stochastic Optimization
213(1)
10.7.4 Results
213(3)
10.8 Conclusions
216(3)
References
217(2)
Index 219
Juan Gabriel Segovia-Hernández

Prof. Segovia-Hernández has been with University of Guanajuato, Mexico, since 2004, in the Chemical Engineering Department. He has co-edited one book and published over 110 papers in international journals, 8 book chapters and several refereed conference proceedings. He was national president of Mexican Academy of Chemical Engineering (2013-2015). He is Member of Mexican Academy of Sciences since 2012. His research interests include design, optimization and control of intensified processes. He is currently the lecturer in several universities in México and abroad. For more details on his research and publications, browse https://www.segovia-hernandez.com

Fernando Israel Gómez-Castro

Professor in the Chemical Engineering Department of the University of Guanajuato since 2012. Obtained the degree of ScD in Chemical Engineering in 2010 at the Institute of Technology of Celaya, Mexico. Author of 31 research papers published in national and international journals and 5 chapters of books, and reviewer of international journals as Chemical Engineering & Technology, Chemical Engineering Research & Design, Industrial & Chemistry Engineering Research, Fuel, among others. Member of the National Researchers System (Mexico) and the American Chemical Society. Its biography has appeared in directories as "Whos Who in the World" and "2000 Outstanding Intellectuals of the 21st Century". He is currently the lecturer of subjects associated with mathematical optimization and its application to chemical process design, for both bachelor and graduate levels. Among his research interests it can be mentioned the use of computational tools for the design and optimization of conventional and intensified chemical processes.