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MARC Record from marc_columbia

Record ID marc_columbia/Columbia-extract-20221130-030.mrc:159489189:7780
Source marc_columbia
Download Link /show-records/marc_columbia/Columbia-extract-20221130-030.mrc:159489189:7780?format=raw

LEADER: 07780cam a2200721Mi 4500
001 14772306
005 20220618232724.0
006 m o d
007 cr cn|||||||||
008 170717s2017 flua ob 001 0 eng d
035 $a(OCoLC)ocn993989094
035 $a(NNC)14772306
040 $aCRCPR$beng$erda$epn$cCRCPR$dN$T$dYDX$dN$T$dEBLCP$dIDEBK$dOCLCF$dCRCPR$dOCLCQ$dMERER$dOCLCQ$dU3W$dWYU$dVT2$dTYFRS$dOCLCQ$dUKAHL$dS2H$dOCLCO$dNLW$dOCLCQ$dZCU$dOCLCO
019 $a1003910250$a1015204366$a1118355756$a1202483574$a1274703458
020 $a9781315155739$q(electronic bk.)
020 $a1315155737$q(electronic bk.)
020 $a9781498785105
020 $a1498785107
020 $a9781351651783$q(EPUB)
020 $a1351651781$q(EPUB)
020 $z9781498785112
020 $z1498785115
035 $a(OCoLC)993989094$z(OCoLC)1003910250$z(OCoLC)1015204366$z(OCoLC)1118355756$z(OCoLC)1202483574$z(OCoLC)1274703458
037 $a9781351651783$bIngram Content Group
050 4 $aTP184
072 7 $aSCI$x013060$2bisacsh
072 7 $aTEC$x009010$2bisacsh
082 04 $a660.28$223
049 $aZCUA
100 1 $aSegovia-Hernández, Juan Gabriel,$eauthor.
245 10 $aStochastic Process Optimization using Aspen Plus® /$cJuan Gabriel Segovia-Hernández and Fernando Israel Gomez-Castro.
250 $aFirst edition.
264 1 $aBoca Raton, FL :$bCRC Press,$c2017.
300 $a1 online resource :$btext file, PDF
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
520 2 $a"Stochastic Process Optimization using Aspen® PlusBookshop Category: Chemical EngineeringOptimization can be simply defined as "choosing the best alternative among a set of feasible options". In all the engineering areas, optimization has a wide range of applications, due to the high number of decisions involved in an engineering environment. Chemical engineering, and particularly process engineering, is not an exception; thus stochastic methods are a good option to solve optimization problems for the complex process engineering models. In this book, the combined use of the modular simulator Aspen® Plus and stochastic optimization methods, codified in MATLAB, is presented. Some basic concepts of optimization are first presented, then, strategies to use the simulator linked with the optimization algorithm are shown. Finally, examples of application for process engineering are discussed. The reader will learn how to link the process simulator Aspen® Plus and stochastic optimization algorithms to solve process design problems. They will gain ability to perform multi-objective optimization in several case studies. Key Features:" The book links simulation and optimization through numerical analyses and stochastic optimization techniques " Includes use of examples to illustrate the application of the concepts and specific guidance on the use of software (Aspen® Plus, Excel, MATLB) to set up and solve models representing complex problems." Illustrates several examples of applications for the linking of simulation and optimization software with other packages for optimization purposes." Provides specific information on how to implement stochastic optimization with process simulators." Enable readers to identify practical and economic solutions to problems of industrial relevance, enhancing the safety, operation, environmental, and economic performance of chemical processes."--Provided by publisher.
505 0 $aCover -- Half title -- Title page -- Copyright page -- Dedication -- Contents -- Preface -- Acknowledgment -- Editors -- Contributors -- Chapter 1: Introduction to Optimization -- 1.1 What Is Optimization? -- 1.2 Mathematical Modeling and Optimization -- 1.3 Classification of Optimization Problems -- 1.4 Objective Function -- 1.5 Optimization with Constraints: Feasible Region -- 1.6 Multiobjective Optimization -- 1.6.1 Weighted Sum Method -- 1.6.2 Constraint Method -- 1.7 Process Optimization -- References -- Chapter 2: Deterministic Optimization
505 8 $a2.1 Introduction2.2 Single-Variable Deterministic Optimization -- 2.3 Continuity and Convexity -- 2.4 Unconstrained Optimization -- 2.5 Equality-Constrained Optimization -- 2.5.1 Method of Lagrange Multipliers -- 2.5.2 Generalized Reduced Gradient Method -- 2.6 Equality- and Inequality-Constrained Optimization -- 2.6.1 Active Set Strategy -- 2.7 Software for Deterministic Optimization -- References -- Chapter 3: Stochastic Optimization -- 3.1 Introduction to Stochastic Optimization -- 3.2 Stochastic Optimization vs. Deterministic Optimization
505 8 $a3.3 Stochastic Optimization with Constraints3.4 Genetic Algorithms -- 3.5 Differential Evolution -- 3.6 Tabu Search -- 3.7 Simulated Annealing -- 3.8 Other Methods -- 3.8.1 Ant Colony Optimization -- 3.8.2 Particle Swarm Optimization -- 3.8.3 Harmony Search -- References -- Chapter 4: The Simulator Aspen Plus® -- 4.1 Importance of Software for Process Analysis -- 4.2 Characteristics of the Process Simulator Aspen Plus -- 4.3 Sequential Modular Simulation -- References -- Chapter 5: Direct Optimization in Aspen Plus® -- 5.1 Optimization Methods
505 8 $a5.2 Sensitivity Analysis Tools in Aspen Plus5.3 Sequential Quadratic Programming in Aspen Plus -- 5.4 Optimization of a Heat Exchanger -- 5.4.1 Description of the Problem -- 5.4.2 Initial Simulation -- 5.4.3 Optimization through Sensitivity Analysis -- 5.4.4 Optimization through Sequential Quadratic Programming -- 5.5 Optimization of a Flash Drum -- 5.5.1 Description of the Problem -- 5.5.2 Initial Simulation -- 5.5.3 Optimization through Sensitivity Analysis -- 5.5.4 Optimization through Sequential Quadratic Programming -- 5.6 Optimization of a Tubular Reactor
505 8 $a5.6.1 Description of the Problem5.6.2 Initial Simulation -- 5.6.3 Optimization through Sensitivity Analysis -- 5.6.4 Optimization through Sequential Quadratic Programming -- References -- Chapter 6: Optimization using Aspen Plus® and Stochastic Toolbox* -- 6.1 Introduction -- 6.2 Software for Stochastic Optimization -- 6.3 Linking Aspen Plus with the Stochastic Optimization Software -- 6.3.1 Creating a Function to be Optimized with MATLAB -- 6.3.2 Creating a Subroutine in Microsoft Excel -- 6.4 Mono-Objective Optimization of a Multicomponent Distillation Column
504 $aIncludes bibliographical references and index.
506 1 $aLegal Deposit;$cOnly available on premises controlled by the deposit library and to one user at any one time;$eThe Legal Deposit Libraries (Non-Print Works) Regulations (UK).$5WlAbNL
540 $aRestricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.$5WlAbNL
650 0 $aChemical processes$xData processing.
650 0 $aMathematical optimization.
650 0 $aStochastic processes.
650 6 $aProcédés chimiques$xInformatique.
650 6 $aOptimisation mathématique.
650 6 $aProcessus stochastiques.
650 07 $aSCIENCE$xChemistry$xIndustrial & Technical.$2bisacsh
650 7 $aTECHNOLOGY & ENGINEERING$xChemical & Biochemical.$2bisacsh
650 7 $aChemical processes$xData processing.$2fast$0(OCoLC)fst00853161
650 7 $aMathematical optimization.$2fast$0(OCoLC)fst01012099
650 7 $aStochastic processes.$2fast$0(OCoLC)fst01133519
655 0 $aElectronic books.
655 4 $aElectronic books.
700 1 $aGómez-Castro, Fernando Israel,$eauthor.
776 08 $iPrint version:$z9781315155739$z9781498785112
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio14772306$zTaylor & Francis eBooks
852 8 $blweb$hEBOOKS