Atnaujinkite slapukų nuostatas

El. knyga: Advanced Optimization for Process Systems Engineering

4.50/5 (4 ratings by Goodreads)
(Carnegie Mellon University, Pennsylvania)
Kitos knygos pagal šią temą:
Advanced Optimization for Process Systems EngineeringDidesnis paveikslėlis
Kitos knygos pagal šią temą:

DRM apribojimai

  • Kopijuoti:

    neleidžiama

  • Spausdinti:

    neleidžiama

  • El. knygos naudojimas:

    Skaitmeninių teisių valdymas (DRM)
    Leidykla pateikė šią knygą šifruota forma, o tai reiškia, kad norint ją atrakinti ir perskaityti reikia įdiegti nemokamą programinę įrangą. Norint skaityti šią el. knygą, turite susikurti Adobe ID . Daugiau informacijos  čia. El. knygą galima atsisiųsti į 6 įrenginius (vienas vartotojas su tuo pačiu Adobe ID).

    Reikalinga programinė įranga
    Norint skaityti šią el. knygą mobiliajame įrenginyje (telefone ar planšetiniame kompiuteryje), turite įdiegti šią nemokamą programėlę: PocketBook Reader (iOS / Android)

    Norint skaityti šią el. knygą asmeniniame arba „Mac“ kompiuteryje, Jums reikalinga  Adobe Digital Editions “ (tai nemokama programa, specialiai sukurta el. knygoms. Tai nėra tas pats, kas „Adobe Reader“, kurią tikriausiai jau turite savo kompiuteryje.)

    Negalite skaityti šios el. knygos naudodami „Amazon Kindle“.

A unique text covering basic and advanced concepts of optimization theory and methods for process systems engineers. With examples illustrating key concepts and algorithms, and exercises involving theoretical derivations, numerical problems and modeling systems, it is ideal for single-semester, graduate courses in process systems engineering.

Based on the author's forty years of teaching experience, this unique textbook covers both basic and advanced concepts of optimization theory and methods for process systems engineers. Topics covered include continuous, discrete and logic optimization (linear, nonlinear, mixed-integer and generalized disjunctive programming), optimization under uncertainty (stochastic programming and flexibility analysis), and decomposition techniques (Lagrangean and Benders decomposition). Assuming only a basic background in calculus and linear algebra, it enables easy understanding of mathematical reasoning, and numerous examples throughout illustrate key concepts and algorithms. End-of-chapter exercises involving theoretical derivations and small numerical problems, as well as in modeling systems like GAMS, enhance understanding and help put knowledge into practice. Accompanied by two appendices containing web links to modeling systems and models related to applications in PSE, this is an essential text for single-semester, graduate courses in process systems engineering in departments of chemical engineering.

Recenzijos

'Authored by Ignacio Grossmann, the creator and key developer of the field of mixed integer nonlinear programming, this outstanding textbook provides a thorough and comprehensive treatment of fundamental concepts, optimization models and effective solution strategies for discrete and continuous optimization. It is an essential, 'must-have' reference for all students, researchers and practitioners in process systems engineering.' Lorenz Biegler, Carnegie Mellon University 'From the globally recognized leading authority in the field of process systems engineering, this long-awaited book will definitely become the standard reference for anyone interested in optimization. It is very well thought and written, with excellent presentation of the material. The theory is described in a very effective, rigorous, and clear way, with appropriate explanations and examples used throughout, covering traditional topics such as linear and nonlinear optimization concepts and mixed-integer linear programming, along with more advanced topics, such as disjunctive programming, global optimization, and stochastic programming. A real gem and a must read!' Stratos Pistikopoulos, Texas A & M University 'Excellent coverage of the basic concepts and approaches developed in the area of process systems engineering in the last forty years. A unique book that can be easily adapted to advanced undergraduate and graduate-level classes to provide overall guidance to different tools that can be used to model and optimize complex engineering problems. I am certainly looking forward to using it in my class on mathematical modeling and optimization principles.' Marianthi Ierapetritou, University of Delaware

Daugiau informacijos

A unique text covering both basic and advanced concepts of optimization theory and methods for process systems engineers.
Preface xiii
1 Optimization in Process Systems Engineering
1(8)
1.1 Introduction
1(2)
1.2 Classification of Optimization Models
3(4)
1.3 Outline of the Book
7(2)
2 Solving Nonlinear Equations
9(7)
2.1 Process Modeling Approaches
9(1)
2.2 Newton's Method
9(2)
2.3 Quasi-Newton Methods
11(5)
Exercises
14(2)
3 Basic Theoretical Concepts in Optimization
16(16)
3.1 Basic Formulations
16(1)
3.2 Nonlinear Programming Example
17(2)
3.3 Basic Concepts
19(2)
3.4 Optimality Conditions
21(11)
3.4.1 Unconstrained Optimization
21(1)
3.4.2 Constrained Optimization (Equalities)
22(2)
3.4.3 Constrained Optimization (Inequalities)
24(2)
3.4.4 Nonlinear Programming Problem
26(1)
3.4.5 Active-Set Strategy Procedure for Determining a Karush-Kuhn-Tucker Point (Sargent, 1975)
27(3)
Exercises
30(2)
4 Nonlinear Programming Algorithms
32(12)
4.1 Successive-Quadratic Programming
32(3)
4.2 Reduced-Gradient Method
35(3)
4.3 Interior-Point Method
38(2)
4.4 Comparison of NLP Algorithms
40(1)
4.5 Guidelines for Formulating NLP Models
40(4)
Exercises
42(2)
5 Linear Programming
44(9)
5.1 Basic Theory
44(3)
5.2 Simplex Algorithm
47(4)
5.3 Numerical Example
51(2)
Exercises
52(1)
6 Mixed-Integer Programming Models
53(9)
6.1 Modeling with 0-1 Variables
53(9)
6.1.1 Motivating Examples
53(2)
6.1.2 Modeling with Linear 0-1 Variables yj
55(2)
6.1.3 Some Common IP Problems
57(4)
Exercises
61(1)
7 Systematic Modeling of Constraints with Logic
62(10)
7.1 Modeling 0-1 Constraints with Propositional Logic
62(2)
7.1.1 Example 1 of Logic Proposition
63(1)
7.1.2 Example 2 of Logic Proposition
64(1)
7.2 Modeling of Disjunctions
64(4)
7.2.1 Big-M Reformulation
65(1)
7.2.2 Convex-Hull Reformulation
65(2)
7.2.3 Example
67(1)
7.3 Generalized Disjunctive Programming
68(4)
Exercises
69(3)
8 Mixed-Integer Linear Programming
72(9)
8.1 Introduction
72(1)
8.2 MILP Methods
73(1)
8.3 Gomory Cutting Planes
74(1)
8.4 Branch and Cut Method
75(6)
Exercises
79(2)
9 Mixed-Integer Nonlinear Programming
81(12)
9.1 Overview of Solution Methods
81(1)
9.2 Derivation of Outer-Approximation and Generalized Benders Decomposition Methods
82(4)
9.3 Extended Cutting-Plane Method
86(1)
9.4 Properties and Extensions
87(6)
Exercises
91(2)
10 Generalized Disjunctive Programming
93(10)
10.1 Logic-Based Formulation for Discrete/Continuous Optimization
93(1)
10.2 Relaxations and Reformulations of GDP
94(3)
10.3 Special Purpose Methods for GDP
97(6)
10.3.1 Disjunctive Branch and Bound
97(3)
10.3.2 Logic-Based Outer Approximation
100(2)
Exercises
102(1)
11 Constraint Programming
103(6)
11.1 Logic-Based Modeling
103(2)
11.2 Search in Constraint Programming
105(4)
11.2.1 Domain Reduction and Constraint Propagation
105(1)
11.2.2 Tree Search
106(2)
Exercises
108(1)
12 Nonconvex Optimization
109(10)
12.1 Major Approaches to Global Optimization
109(1)
12.2 Convexification
110(1)
12.3 Global Optimization of Bilinear Programs
111(6)
12.4 Global Optimization of More General Functions
117(2)
Exercises
117(2)
13 Lagrangean Decomposition
119(10)
13.1 Overview of Decomposition for Large-Scale Problems
119(1)
13.2 Lagrangean Relaxation
120(1)
13.3 Lagrangean Dual
121(3)
13.4 Lagrangean Decomposition
124(2)
13.5 Update of Lagrange Multipliers
126(3)
Exercises
128(1)
14 Stochastic Programming
129(13)
14.1 Strategies for Optimization under Uncertainty
129(3)
14.2 Linear Stochastic Programming
132(2)
14.3 L-Shaped Method
134(4)
14.4 Multistage Stochastic Programming
138(1)
14.5 Robust Optimization
139(3)
Exercises
141(1)
15 Flexibility Analysis
142(21)
15.1 Introduction
142(1)
15.2 Two-Stage Programming with Guaranteed Feasibility
142(2)
15.3 Flexibility Analysis
144(1)
15.4 Flexibility Test with No Control Variables
145(2)
15.5 Flexibility Test with Control Variables
147(2)
15.6 Parametric Region of Feasible Operation and Vertex Search
149(2)
15.7 Flexibility Index and Vertex Search
151(2)
15.8 Theoretical Conditions for Vertex Solutions
153(3)
15.9 Active-Set Strategy
156(7)
Exercises
161(2)
Appendix A Modeling Systems and Optimization Software 163(8)
Appendix B Optimization Models for Process Systems Engineering 171(9)
References 180(7)
Index 187
Ignacio E. Grossmann is the R. R. Dean University Professor of Chemical Engineering at Carnegie Mellon University, and Director of the Center for Advanced Process Decision-making. He is a member of the National Academy of Engineering.
Daugiau apie elektronines knygas Daugiau apie elektronines knygas
Pastovi nuoroda: https://www.kriso.lt/db/97811089347322e.html
Raktažodžiai: