LEADER 05707nam 2200709 a 450 001 9910780885703321 005 20230725041533.0 010 $a1-283-14433-6 010 $a9786613144331 010 $a981-4299-21-9 035 $a(CKB)2490000000001935 035 $a(EBL)731324 035 $a(OCoLC)738434184 035 $a(SSID)ssj0000525888 035 $a(PQKBManifestationID)12177060 035 $a(PQKBTitleCode)TC0000525888 035 $a(PQKBWorkID)10508649 035 $a(PQKB)10683598 035 $a(MiAaPQ)EBC731324 035 $a(WSP)00007669 035 $a(Au-PeEL)EBL731324 035 $a(CaPaEBR)ebr10479744 035 $a(CaONFJC)MIL314433 035 $a(EXLCZ)992490000000001935 100 $a20110131d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aStochastic global optimization$b[electronic resource] $etechniques and applications in chemical engineering /$feditor, Gade Pandu Rangaiah 210 $aSingapore ;$aHackensack, N.J. $cWorld Scientific Pub. Co.$d2010 215 $a1 online resource (722 p.) 225 1 $aAdvances in process systems engineering ;$vv. 2 300 $aDescription based upon print version of record. 311 $a981-4299-20-0 320 $aIncludes bibliographical references and index. 327 $aPreface; 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 327 $a2.2. 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 327 $a2.1. 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 327 $a4.2.3. Influence of parameter K in the equilibrium criterion4.2.4. Influence of parameter ? 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 327 $a1. 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 327 $a4.3. Constraint handling 330 $aOptimization has played a key role in the design, planning and operation of chemical and related processes, for several decades. Global optimization has been receiving considerable attention in the past two decades. Of the two types of techniques for global optimization, stochastic global optimization is applicable to any type of problems having non-differentiable functions, discrete variables and/or continuous variables. It, thus, shows significant promise and potential for process optimization. So far, there are no books focusing on stochastic global optimization and its applications in chem 410 0$aAdvances in process systems engineering ;$vv. 2. 606 $aChemical processes 606 $aMathematical optimization 606 $aStochastic processes 606 $aChemical engineering$xMathematics 615 0$aChemical processes. 615 0$aMathematical optimization. 615 0$aStochastic processes. 615 0$aChemical engineering$xMathematics. 676 $a519.62 701 $aRangaiah$b Gade Pandu$0898775 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910780885703321 996 $aStochastic global optimization$93849655 997 $aUNINA