05741nam 2200721 a 450 991045622830332120200520144314.01-283-14433-69786613144331981-4299-21-9(CKB)2490000000001935(EBL)731324(OCoLC)738434184(SSID)ssj0000525888(PQKBManifestationID)12177060(PQKBTitleCode)TC0000525888(PQKBWorkID)10508649(PQKB)10683598(MiAaPQ)EBC731324(WSP)00007669(Au-PeEL)EBL731324(CaPaEBR)ebr10479744(CaONFJC)MIL314433(EXLCZ)99249000000000193520110131d2010 uy 0engur|n|---|||||txtccrStochastic global optimization[electronic resource] techniques and applications in chemical engineering /editor, Gade Pandu RangaiahSingapore ;Hackensack, N.J. World Scientific Pub. Co.20101 online resource (722 p.)Advances in process systems engineering ;v. 2Description based upon print version of record.981-4299-20-0 Includes bibliographical references and index.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 foods2.2. 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 Approach2.1. 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 INV4.2.3. 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 Areibi1. 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 evaluation4.3. Constraint handlingOptimization 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 chemAdvances in process systems engineering ;v. 2.Chemical processesMathematical optimizationStochastic processesChemical engineeringMathematicsElectronic books.Chemical processes.Mathematical optimization.Stochastic processes.Chemical engineeringMathematics.519.62Rangaiah Gade Pandu898775MiAaPQMiAaPQMiAaPQBOOK9910456228303321Stochastic global optimization2008004UNINA