Optimization of Chemical Processes,9780070189911

Optimization of Chemical Processes

by ;
ISBN13: 9780070189911
ISBN10: 0070189919
Format: Hardcover
Pub. Date: 1988-01-01
Publisher(s): McGraw-Hill College
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Table of Contents

Preface xv
Part I Problem Formulation 1(120)
1 The Nature and Organization of Optimization Problems
3(31)
1.1 What Optimization Is All About
4(1)
1.2 Why Optimize?
4(1)
1.3 Scope and Hierarchy of Optimization
5(4)
1.4 Examples of Applications of Optimization
9(5)
1.5 The Essential Features of Optimization Problems
14(4)
1.6 General Procedure for Solving Optimization Problems
18(8)
1.7 Obstacles to Optimization
26(1)
References
27(1)
Problems
28(6)
2 Fitting Models to Data
34(35)
2.1 Classification of Models
36(5)
2.2 How to Build a Model
41(2)
2.3 Fitting Functions to Empirical Data
43(7)
2.3.1 How to Determine the Form of a Model
43(7)
2.4 The Method of Least Squares
50(7)
2.5 Factorial Experimental Designs
57(3)
2.6 Fitting a Model to Data Subject to Constraints
60(2)
References
62(1)
Problems
63(6)
3 Formulation of Objective Functions
69(52)
3.1 Investment Costs and Operating Costs in Objective Functions
70(8)
3.2 Consideration of the Time Value of Money
78(5)
3.3 Measures of Profitability
83(5)
3.4 Optimizing Profitability
88(5)
3.5 Project Financial Evaluation
93(12)
3.6 Cost Estimation
105(8)
References
113(1)
Problems
114(7)
Part II Optimization Theory and Methods 121(318)
4 Basic Concepts of Optimization
123(34)
4.1 Continuity of Functions
124(3)
4.2 Unimodal Versus Multimodal Functions
127(2)
4.3 Convex and Concave Functions
129(5)
4.4 Convex Region
134(4)
4.5 Necessary and Sufficient Conditions for an Extremum of an Unconstrained Function
138(7)
4.6 Interpretation of the Objective Function in Terms of Its Quadratic Approximation
145(6)
References
151(1)
Problems
152(5)
5 Optimization of Unconstrained Functions: One-Dimensional Search
157(31)
5.1 Numerical Methods for Optimizing a Function of One Variable
160(1)
5.2 Scanning and Bracketing Procedures
161(1)
5.3 Newton, Quasi-Newton, and Secant Methods of Unidimensional Search
162(9)
5.3.1 Newton's Method
163(1)
5.3.2 Quasi-Newton Method
164(1)
5.3.3 Secant Method
164(7)
5.4 Region Elimination Methods
171(4)
5.5 Polynomial Approximation Methods
175(6)
5.5.1 Quadratic Interpolation
175(3)
5.5.2 Cubic Interpolation
178(3)
5.6 How the One-Dimensional Search is Applied in a Multidimensional Problem
181(2)
5.7 Evaluation of Unidimensional Search Methods
183(1)
References
184(1)
Problems
184(4)
6 Unconstrained Multivariable Optimization
188(62)
6.1 Direct Methods
190(12)
6.1.1 Random Search
190(1)
6.1.2 Grid Search
190(1)
6.1.3 Univariate Search
190(2)
6.1.4 Simplex Method
192(2)
6.1.5 Conjugate Search Directions
194(3)
6.1.6 Powell's Method
197(5)
6.1.7 Summary
202(1)
6.2 Indirect Methods--First Order
202(6)
6.2.1 Gradient Method
202(4)
6.2.2 Conjugate Gradient
206(2)
6.3 Indirect Methods--Second Order
208(11)
6.3.1 Newton's Method
208(6)
6.3.2 Forcing the Hessian Matrix to be Positive Definite
214(2)
6.3.3 Movement in the Search Direction
216(1)
6.3.4 Termination
217(1)
6.3.5 Summary of Newton's Method
218(1)
6.3.6 Relation Between Conjugate Gradient Methods and Quasi-Newton Methods
219(1)
6.4 Secant Methods
219(11)
6.4.1 Determination of the Approximate Hessian Matrix
220(7)
6.4.2 Movement in the Search Direction
227(1)
6.4.3 Termination
228(1)
6.4.4 Summary of Secant Methods
228(1)
6.4.5 Summary of Indirect Methods
229(1)
6.5 Finite Difference Approximations as Substitutes for Derivatives
230(3)
6.6 Sources of Computer Codes for Unconstrained Optimization
233(3)
6.7 Evaluation of Codes for Unconstrained Optimization
236(3)
6.8 Diagnosis of Optimization Code Failure to Solve a Problem
239(1)
References
239(2)
Problems
241(9)
7 Linear Programming and Applications
250(49)
7.1 Basic Concepts in Linear Programming
254(5)
7.2 Degenerate LP's--Graphical Solution
259(2)
7.3 Natural Occurrence of Linear Constraints
261(2)
7.4 The Simplex Method of Solving Linear Programming Problems
263(7)
7.5 Standard LP Form
270(3)
7.6 Obtaining a First Feasible Solution
273(6)
7.7 The Revised Simplex Method
279(3)
7.8 Sensitivity Analysis
282(2)
7.9 Duality in Linear Programming
284(3)
7.10 The Karmarkar Algorithm
287(3)
7.11 LP Applications
290(1)
References
290(1)
Problems
291(8)
8 Nonlinear Programming with Constraints
299(96)
8.1 The Lagrange Multiplier Method
302(7)
8.2 Necessary and Sufficient Conditions for a Local Minimum
309(10)
8.3 Quadratic Programming
319(3)
8.4 The Generalized Reduced-Gradient Method
322(12)
8.4.1 Concept of the Reduced Gradient
323(4)
8.4.2 The Generalized Reduced Gradient Algorithm
327(7)
8.4.3 Sources of Computer Codes
334(1)
8.5 Penalty Function and Augmented Lagrangian Methods
334(8)
8.6 Successive (Sequential, Recursive) Quadratic Programming
342(16)
8.6.1 Form of the Quadratic-Programming Subproblem
343(5)
8.6.2 Successive Quadratic-Programming Algorithm
348(10)
8.6.3 Successive Quadratic-Programming Codes
358(1)
8.7 Random Search Methods
358(3)
8.8 Comparative Evaluation of General Nonlinear Programming Codes
361(2)
8.9 Successive Linear Programming
363(1)
8.10 Optimization of Dynamic Processes
364(8)
8.11 Diagnosis of the Failure of Optimization Codes to Solve Problems
372(1)
References
373(6)
Problems
379(16)
9 Optimization of Staged and Discrete Processes
395(44)
9.1 Dynamic Programming
397(9)
9.2 Integer and Mixed Integer Programming
406(18)
9.2.1 Implicit Enumeration
411(2)
9.2.2 The Branch and Bound Technique
413(9)
9.2.3 Nonlinear Mixed-Integer Programming Algorithms
422(2)
References
424(2)
Problems
426(13)
Part III Applications of Optimization 439(160)
10 Heat Transfer and Energy Conservation
443(31)
10.1 Optimizing Recovery of Waste Heat
447(2)
10.2 Optimum Shell and Tube Heat Exchanger Design
449(8)
10.3 Optimization of Heat Exchanger Networks
457(5)
Example 10.1 Optimal Allocation of Temperatures in a Sequence of Heat Exchangers
458(4)
10.4 Optimization of Evaporator Design
462(5)
Example 10.2 Optimization of a Multistage Evaporator
463(4)
10.5 Boiler/Turbo Generator System Optimization
467(4)
References
471(3)
11 Separation Processes
474(28)
11.1 Optimization of Liquid-Liquid Extraction Processes
475(5)
Example 11.1 Optimization of Liquid Extraction Column Flowrates
475(5)
11.2 Optimal Design and Operation of Staged Distillation Columns
480(21)
Example 11.2 Optimal Design and Operation of Conventional Staged Distillation Columns
486(5)
Example 11.3 Nonlinear Regression to Fit Vapor-Liquid Equilibrium Data
491(4)
Example 11.4 Determination of the Optimum Reflux Ratio for a Staged Distillation Column
495(3)
Example 11.5 Use of Linear Programming to Optimize a Separation Train
498(3)
References
501(1)
12 Fluid Flow Systems
502(22)
Example 12.1 Optimal Pipe Diameter
503(3)
Example 12.2 Minimum Work Compression
506(4)
Example 12.3 Economic Operation of a Fixed-Bed Filter
510(3)
Example 12.4 Optimal Design of a Gas Transmission Network
513(9)
References
522(2)
13 Chemical Reactor Design and Operation
524(27)
13.1 Formulation of Chemical Reactor Optimization Problems
525(3)
13.1.1 Modeling of Chemical Reactors
525(1)
13.1.2 Objective Functions for Reactors
526(2)
13.2 Use of Differential Calculus in Reactor Optimization
528(6)
Example 13.1 Optimal Residence Time for Maximum Yield in an Ideal Isothermal Batch Reactor
531(1)
Example 13.2 One-Dimensional Search for Optimum Residence Time of a Chemostat
532(2)
13.3 Use of Linear Programming to Optimize Reactor Operations
534(4)
Example 13.3 Optimization of a Thermal Cracker
534(4)
13.4 Nonlinear Programming Applied to Chemical Reactor Optimization
538(12)
Example 13.4 Maximum Yield With Respect to Reactor Volume
541(1)
Example 13.5 Optimal Design of an Ammonia Reactor
542(4)
Example 13.6 Solution of an Alkylation Process by Sequential Quadratic Programming
546(4)
References
550(1)
14 Optimization in Large-Scale Plant Design and Operation
551(48)
14.1 General Methods of Meshing Optimization Procedures with Process Models/Simulators
556(3)
14.2 Equation-Based Large-Scale Optimization
559(13)
Example 14.1 Equation-Based Optimization for a Refrigeration Process
561(9)
Example 14.2 Application of ASCEND-II to the Optimization of a Distillation Column
570(2)
14.3 Large-Scale Optimization Using Sequential Modular Flowsheeting
572(16)
14.3.1 Feasible Path Strategies
575(3)
Example 14.3 Application of the Feasible Path Method to a Process for the Chlorination of Propylene
578(9)
14.3.2 Infeasible Path Strategies
587(1)
Example 14.4 Application of the Nonfeasible Path Method to a Process for the Chlorination of Propylene
588(1)
14.4 Large-Scale Optimization Incorporating Simultaneous Modular Flowsheeting Strategies
588(6)
14.4.1 Calculation of the Elements in the Jacobian Matrix that Represent the Process
591(2)
14.4.2 Nonlinear Programming Algorithm
593(1)
14.4.3 Scaling of the Objective Function and Variables
593(1)
14.5 Conclusions Regarding Combining Optimization with Flowsheeting Codes
594(1)
14.6 Treatment of Large-Scale Problems with Integer-Valued Variables
594(1)
References
595(4)
Appendixes 599(34)
A Nomenclature 599(5)
B Mathematical Summary 604(19)
B.1 Definitions 605(1)
B.2 Basic Matrix Operations 606(7)
B.3 Linear Independence and Row Operations 613(3)
B.4 Solution of Linear Equations 616(3)
B.5 Eigenvalues, Eigenvectors 619(2)
References 621(1)
Problems 621(2)
C Range Space and Null Space and Relation to Reduced Gradient and Projection Methods 623(9)
References 632(1)
Name Index 633(9)
Subject Index 642

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