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Optimization in Chemical Engineering [Kietas viršelis]

  • Formatas: Hardback, 380 pages, aukštis x plotis x storis: 246x184x24 mm, weight: 820 g
  • Išleidimo metai: 11-Mar-2016
  • Leidėjas: Cambridge University Press
  • ISBN-10: 1107091233
  • ISBN-13: 9781107091238
Kitos knygos pagal šią temą:
Optimization in Chemical EngineeringDidesnis paveikslėlis
  • Formatas: Hardback, 380 pages, aukštis x plotis x storis: 246x184x24 mm, weight: 820 g
  • Išleidimo metai: 11-Mar-2016
  • Leidėjas: Cambridge University Press
  • ISBN-10: 1107091233
  • ISBN-13: 9781107091238
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9781107091238.html
Raktažodžiai:
Optimization is used to determine the most appropriate value of variables under given conditions. The primary focus of using optimisation techniques is to measure the maximum or minimum value of a function depending on the circumstances. This book discusses problem formulation and problem solving with the help of algorithms such as secant method, quasi-Newton method, linear programming and dynamic programming. It also explains important chemical processes such as fluid flow systems, heat exchangers, chemical reactors and distillation systems using solved examples. The book begins by explaining the fundamental concepts followed by an elucidation of various modern techniques including trust-region methods, LevenbergMarquardt algorithms, stochastic optimization, simulated annealing and statistical optimization. It studies the multi-objective optimization technique and its applications in chemical engineering and also discusses the theory and applications of various optimization software tools including LINGO, MATLAB, MINITAB and GAMS.

Daugiau informacijos

In this book, optimization of chemical processes is performed using both classical and advanced algorithms.
List of Figures
xiii
List of Tables
xvii
Preface xix
1 A Brief Discussion on Optimization
1.1 Introduction to Process Optimization
1(1)
1.2 Statement of an Optimization Problem
2(1)
1.3 Classification of Optimization Problems
3(5)
1.4 Salient Feature of Optimization
8(1)
1.5 Applications of Optimization in Chemical Engineering
9(1)
1.6 Computer Application for Optimization Problems
10(2)
Summary
10(1)
Review Questions
10(1)
References
11(1)
2 Formulation of Optimization Problems in Chemical and Biochemical Engineering
2.1 Introduction
12(1)
2.2 Formulation of Optimization Problem
12(1)
2.3 Fluid Flow System
13(4)
2.3.1 Optimization of liquid storage tank
13(1)
2.3.2 Optimization of pump configurations
14(3)
2.4 Systems with Chemical Reaction
17(4)
2.4.1 Optimization of product concentration during chain reaction
18(2)
2.4.2 Optimization of gluconic acid production
20(1)
2.5 Optimization of Heat Transport System
21(7)
2.5.1 Calculation of optimum insulation thickness
21(3)
2.5.2 Optimization of simple heat exchanger network
24(2)
2.5.3 Maximum temperature for two rotating cylinders
26(2)
2.6 Calculation of Optimum Cost of an Alloy using LP Problem
28(2)
2.7 Optimization of Biological Wastewater Treatment Plant
30(1)
2.8 Calculation of Minimum Error in Least Squares Method
31(2)
2.9 Determination of Chemical Equilibrium
33(7)
Summary
35(1)
Exercise
35(4)
References
39(1)
3 Single Variable Unconstrained Optimization Methods
3.1 Introduction
40(1)
3.2 Optimization of Single Variable Function
41(7)
3.2.1 Criteria for optimization
41(6)
3.2.2 Classification of unconstrained minimization methods
47(1)
3.3 Direct Search Methods
48(16)
3.3.1 Finding a bracket for a minimum
48(1)
3.3.2 Unrestricted search method
49(2)
3.3.3 Exhaustive search
51(2)
3.3.4 Dichotomous search
53(3)
3.3.5 Interval halving method
56(3)
3.3.6 Fibonacci method
59(3)
3.3.7 Golden section method
62(2)
3.4 Direct Root Methods
64(4)
3.4.1 Newton method
65(1)
3.4.2 Quasi-Newton method
66(1)
3.4.3 Secant method
67(1)
3.5 Polynomial Approximation Methods
68(6)
3.5.1 Quadratic interpolation
69(1)
3.5.2 Cubic interpolation
70(2)
Summary
72(1)
Exercise
72(1)
References
73(1)
4 Trust-Region Methods
4.1 Introduction
74(1)
4.2 Basic Trust-Region Method
75(4)
4.2.1 Problem statement
75(1)
4.2.2 Trust-Region radius
76(2)
4.2.3 Trust-Region subproblem
78(1)
4.2.4 Trust-Region fidelity
78(1)
4.3 Trust-Region Methods for Unconstrained Optimization
79(1)
4.4 Trust-Region Methods for Constrained Optimization
80(2)
4.5 Combining with Other Techniques
82(1)
4.6 Termination Criteria
83(1)
4.7 Comparison of Trust-Region and Line-Search
83(3)
Summary
84(1)
Exercise
84(1)
References
84(2)
5 Optimization of Unconstrained Multi variable Functions
5.1 Introduction
86(1)
5.2 Formulation of Unconstrained Optimization
87(1)
5.3 Direct Search Method
87(12)
5.3.1 Random search methods
87(3)
5.3.2 Grid search method
90(3)
5.3.3 Univariate method
93(1)
5.3.4 Pattern search methods
94(5)
5.4 Gradient Search Method
99(15)
5.4.1 Steepest descent (Cauchy) method
100(2)
5.4.2 Conjugate gradient (Fletcher-Reeves) method
102(2)
5.4.3 Newton's method
104(2)
5.4.4 Marquardt method
106(3)
5.4.5 Quasi-Newton method
109(4)
5.4.6 Broydon-Fletcher-Goldfrab-Shanno method
113(1)
5.5 Levenberg-Marquardt Algorithm
114(5)
Summary
116(1)
Review Questions
116(1)
References
117(2)
6 Multivariable Optimization with Constraints
6.1 Formulation of Constrained Optimization
119(3)
6.2 Linear Programming
122(22)
6.2.1 Formulation of linear programming problems
122(5)
6.2.2 Simplex method
127(6)
6.2.3 Nonsimplex methods
133(6)
6.2.4 Integer linear programming
139(5)
6.3 Nonlinear Programming with Constraints
144(13)
6.3.1 Problems with equality constraints
144(5)
6.3.2 Problems with inequality constraints
149(2)
6.3.3 Convex optimization problems
151(3)
Summary
154(1)
Review Questions
154(2)
References
156(1)
7 Optimization of Staged and Discrete Processes
7.1 Dynamic Programming
157(9)
7.1.1 Components of dynamic programming
158(1)
7.1.2 Theory of dynamic programming
159(1)
7.1.3 Description of a multistage decision process
160(6)
7.2 Integer and Mixed Integer Programming
166(14)
7.2.1 Formulation of MINLP
167(2)
7.2.2 Generalized Benders Decomposition
169(7)
Summary
176(1)
Exercise
176(2)
References
178(2)
8 Some Advanced Topics on Optimization
8.1 Stochastic Optimization
180(13)
8.1.1 Uncertainties in process industries
180(2)
8.1.2 Basic concept of probability theory
182(4)
8.1.3 Stochastic linear programming
186(5)
8.1.4 Stochastic nonlinear programming
191(2)
8.2 Multi-Objective Optimization
193(13)
8.2.1 Basic theory of multi-objective optimization
197(5)
8.2.2 Multi-objective optimization applications in chemical engineering
202(4)
8.3 Optimization in Control Engineering
206(16)
8.3.1 Real time optimization
206(2)
8.3.2 Optimal control of a batch reactor
208(4)
8.3.3 Optimal regulatory control system
212(2)
8.3.4 Dynamic matrix control
214(4)
Summary
218(1)
Review Questions
218(1)
References
219(3)
9 Nontraditional Optimization
9.1 Genetic Algorithm
222(7)
9.1.1 Working principle of GAs
223(5)
9.1.2 Termination
228(1)
9.2 Particle Swarm Optimization
229(12)
9.2.1 Working principle
230(1)
9.2.2 Algorithm
231(1)
9.2.3 Initialization
231(1)
9.2.4 Variants of PSO
232(3)
9.2.5 Stopping criteria
235(1)
9.2.6 Swarm communication topology
236(5)
9.3 Differential Evolution
241(4)
9.3.1 DE algorithm
241(1)
9.3.2 Initialization
242(1)
9.3.3 Mutation
243(1)
9.3.4 Crossover
244(1)
9.3.5 Selection
245(1)
9.4 Simulated Annealing
245(13)
9.4.1 Procedure
246(7)
9.4.2 Applications of SA in chemical engineering
253(1)
Summary
253(1)
Exercise
254(1)
References
255(3)
10 Optimization of Various Chemical and Biochemical Processes
10.1 Heat Exchanger Network Optimization
258(5)
10.1.1 Superstructure
259(1)
10.1.2 Problem statement
260(1)
10.1.3 Model formulation
260(3)
10.2 Distillation System Optimization
263(4)
10.3 Reactor Network Optimization
267(4)
10.4 Parameter Estimation in Chemical Engineering
271(5)
10.4.1 Derivation of objective function
271(2)
10.4.2 Parameter estimation of dynamic system
273(3)
10.5 Environmental Application
276(8)
Summary
281(1)
Review Questions
281(1)
References
282(2)
11 Statistical Optimization
11.1 Design of Experiment
284(12)
11.1.1 Stages of DOE
285(1)
11.1.2 Principle of DOE
286(3)
11.1.3 ANOVA study
289(2)
11.1.4 Types of experimental design
291(5)
11.2 Response Surface Methodology
296(11)
11.2.1 Analysis of a second order response surface
301(2)
11.2.2 Optimization of multiple response processes
303(2)
Summary
305(1)
Review Questions
305(1)
References
305(2)
12 Software Tools for Optimization Processes
12.1 LINGO
307(9)
12.2 MATLAB
316(7)
12.3 MINITAB®
323(10)
12.4 GAMS
333(10)
Summary
342(1)
Review Questions
342(1)
References
342(1)
Multiple Choice Questions - 1 343(6)
Multiple Choice Questions - 2 349(6)
Multiple Choice Questions - 3 355(4)
Index 359
Suman Dutta received his Ph.D. from Jadavpur University, Kolkata and has published a number of research papers in national and international journals. He was a visiting researcher at the Centre of Water Science in Cranfield University, UK. He teaches courses on chemical engineering thermodynamics, chemical reaction engineering, fluid mechanics, process modelling and optimisation and process instrumentation and control. His areas of research include wastewater treatment, membrane technology, advanced oxidation process, process simulation and optimisation.