| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNINA9910456228303321 |
|
|
Titolo |
Stochastic global optimization [[electronic resource] ] : techniques and applications in chemical engineering / / editor, Gade Pandu Rangaiah |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Singapore ; ; Hackensack, N.J., : World Scientific Pub. Co., 2010 |
|
|
|
|
|
|
|
ISBN |
|
1-283-14433-6 |
9786613144331 |
981-4299-21-9 |
|
|
|
|
|
|
|
|
Descrizione fisica |
|
1 online resource (722 p.) |
|
|
|
|
|
|
Collana |
|
Advances in process systems engineering ; ; v. 2 |
|
|
|
|
|
|
Altri autori (Persone) |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Chemical processes |
Mathematical optimization |
Stochastic processes |
Chemical engineering - Mathematics |
Electronic books. |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Note generali |
|
Description based upon print version of record. |
|
|
|
|
|
|
Nota di bibliografia |
|
Includes bibliographical references and index. |
|
|
|
|
|
|
Nota di contenuto |
|
Preface; CONTENTS; Chapter 1 Introduction Gade Pandu Rangaiah; 1. Optimization in Chemical Engineering; 2. Examples Requiring Global Optimization; 2.1. Modified Himmelblau function; 2.2. Ellipsoid and hyperboloid intersection; 2.3. Reactor design example; 2.4. Stepped paraboloid function; 3. Global Optimization Techniques; 4. Scope and Organization of the Book; References; Exercises; Chapter 2 Formulation and Illustration of Luus-Jaakola Optimization Procedure Rein Luus; 1. Introduction; 2. LJ Optimization Procedure; 2.1. Example of an optimization problem-diet problem with 7 foods |
2.2. Example 2-Alkylation process optimization2.3. Example 3 -Gibbs free energy minimization; 3. Handling Equality Constraints; 3.1. Example 4 -Geometric problem; 3.2. Example 5 -Design of columns; 4. Effect of Parameters; 4.1. Example 7 -Minimization of Rosenbrock function; 4.2. Example 8 -Maximization of the Shubert function; 5. Conclusions; References; Exercises; Chapter 3 Adaptive Random Search and Simulated Annealing Optimizers: Algorithms and Application Issues Jacek M. Je ̇zowski, Grzegorz Poplewski and Roman Bochenek; 1. |
|
|
|
|
|
|
|
|
|
|
|
Introduction and Motivation; 2. Adaptive Random Search Approach |
2.1. Introduction3. Simulated Annealing with Simplex Method; 3.1. Introduction; 3.2. SA-S/1 algorithm; 3.3. Important mechanisms of SA-S/1 algorithm; 3.3.1. Initial simplex generation; 3.3.2. Determination of the initial temperature; 3.3.3. Acceptance criterion; 3.3.4. Cooling scheme-Temperature decrease; 3.3.5. Equilibrium criterion; 3.3.6. Stopping (convergence) criterion; 4. Tests, Control Parameters Settings and Important Application Issues; 4.1. Tests-Test problems and results; 4.2. Parameter settings for SA-S/1 algorithm; 4.2.1. Cooling scheme; 4.2.2. Influence of parameter INV |
4.2.3. Influence of parameter K in the equilibrium criterion4.2.4. Influence of parameter γ in the adaptive cooling scheme; 4.2.5. Influence of parameter T min; 4.3. Results and analysis of tests for LJ-MM algorithm; 4.4. Selected application issues; 4.4.1. Dealing with inequality constraints; 4.4.2. Dealing with equality constraints; 4.5. Problem size effect; 5. Summary; Symbols; Superscripts; Acronyms; References; Exercises; Appendix A; Chapter 4 Genetic Algorithms in Process Engineering: Developments and Implementation Issues Abdunnaser Younes, Ali Elkamel and Shawki Areibi |
1. Introduction2. Review of Chemical Engineering Applications; 3. The Basic Genetic Algorithm; 3.1. Encoding; 3.2. Fitness evaluation; 3.3. Initial population; 3.4. Selection; 3.4.1. Fitness proportionate selection; 3.4.2. Other selection schemes; 3.5. Crossover; 3.6. Mutation; 3.7. Theoretical aspects; 3.8. General characteristics; 3.8.1. Advantages; 3.8.2. Disadvantages; 3.9. When should we use GAs?; 4. Implementation Issues; 4.1. Primary decisions; 4.1.1. Encoding; 4.2. Complex evaluations; 4.2.1. Reducing the total number of evaluations; 4.2.2. Reducing the cost of individual evaluation |
4.3. Constraint handling |
|
|
|
|
|
|
Sommario/riassunto |
|
Optimization has played a key role in the design, planning and operation of chemical and related processes, for several decades. Global optimization has been receiving considerable attention in the past two decades. Of the two types of techniques for global optimization, stochastic global optimization is applicable to any type of problems having non-differentiable functions, discrete variables and/or continuous variables. It, thus, shows significant promise and potential for process optimization. So far, there are no books focusing on stochastic global optimization and its applications in chem |
|
|
|
|
|
|
|
| |