Stochastic Global Optimization Methods and Applications to Chemical, Biochemical, Pharmaceutical and Environmental Processes PDF
Preview Stochastic Global Optimization Methods and Applications to Chemical, Biochemical, Pharmaceutical and Environmental Processes
Stochastic Global Optimization Methods and Applications to Chemical, Biochemical, Pharmaceutical and Environmental Processes Ch. Venkateswarlu B V Raju Institute of Technology, Narsapur, Andhra Pradesh, India; Formerly: Indian Institute of Chemical Technology (CSIR-IICT), Hyderabad, Telangana, India Satya Eswari Jujjavarapu Department of Biotechnology, National Institute of Technology, Raipur, Chhattisgarh, India Elsevier Radarweg29,POBox211,1000AEAmsterdam,Netherlands TheBoulevard,LangfordLane,Kidlington,OxfordOX51GB,UnitedKingdom 50HampshireStreet,5thFloor,Cambridge,MA02139,UnitedStates Copyright©2020ElsevierInc.Allrightsreserved. Nopartofthispublicationmaybereproducedortransmittedinanyformorbyanymeans,electronic ormechanical,includingphotocopying,recording,oranyinformationstorageandretrievalsystem, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangementswithorganizations suchastheCopyrightClearanceCenterandtheCopyrightLicensingAgency,canbefoundatour website:www.elsevier.com/permissions. Thisbookandtheindividualcontributionscontainedinitareprotectedundercopyrightbythe Publisher(otherthanasmaybenotedherein). Notices Knowledgeandbestpracticeinthisfieldareconstantlychanging.Asnewresearchandexperience broaden our understanding, changes in research methods, professional practices, or medical treatmentmaybecomenecessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. Inusingsuchinformationormethodstheyshouldbemindfuloftheirownsafetyandthesafety ofothers,includingpartiesforwhomtheyhaveaprofessionalresponsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assumeanyliabilityforanyinjuryand/ordamagetopersonsorpropertyasamatterofproducts liability, negligence or otherwise, or from any use or operation of any methods, products, instructions,orideascontainedinthematerialherein. LibraryofCongressCataloging-in-PublicationData AcatalogrecordforthisbookisavailablefromtheLibraryofCongress BritishLibraryCataloguing-in-PublicationData AcataloguerecordforthisbookisavailablefromtheBritishLibrary ISBN:978-0-12-817392-3 ForinformationonallElsevierpublicationsvisitourwebsite athttps://www.elsevier.com/books-and-journals Publisher:SusanDennis AcquisitionEditor:KostasMarinakis EditorialProjectManager:AndreaDulberger ProductionProjectManager:NirmalaArumugam CoverDesigner:GregHarris TypesetbyTNQTechnologies About the authors Dr. Ch. Venkateswarlu, the Director R&D at BV Raju Institute of Technology (BVRIT), Narsapur, Greater Hyderabad, India, has earlier worked as Scientist, SeniorPrincipalScientist,andChiefScientistatIndianInstituteofChemicalTech- nology(IICT),Hyderabad,apremierresearchanddevelopment(R&D)instituteof CouncilofScientificandIndustrialResearch(CSIR,India).PriortoDirectorR&D at BVRIT, he worked as Professor, Principal, and Head of Chemical Engineering Department of the same institute. He did his graduation from Andhra University as well as from Indian Institute of Chemical Engineers and postgraduation and PhD in Chemical Engineering from Osmania University, Hyderabad, India. He holds 35 years R&D experience along with 19 years teaching experience and 2yearsindustryexperience.Hisresearchinterestslieintheareasofdynamicprocess modelingandsimulation,processidentificationanddynamicoptimization,process monitoring and fault diagnosis, state estimation and soft sensing, statistical process control and advanced process control, applied engineering mathematics and evolutionary computing, artificial intelligence and expert systems, and bio- processengineeringandbioinformatics.Hepublishedmorethan100researchpapers inpeerjournalsofreputealongwithfewinternationalandnationalproceedingpub- lications. He is also credited with 150 technical paper presentations and invited lectures and few book chapters. He has executed several R&D projects sponsored by DST and Industry. He is a reviewer of several international research journals and many national and international research project proposals. He has guided several postgraduate and PhD students. He served as a long-term guest faculty for premier institutes such as Bhabha Atomic Research Centre Scientific Officer Training,BITSPilaniMS(off-campus),andIICT-CDACBioinformaticsPrograms. He is a Fellow of Andhra Pradesh Academy of Sciences and Telangana State Academy of Sciences in India. He received various awards in recognition to his R&Dandacademiccontributions. Dr. Satya Eswari Jujjavarapu is currently an Assistant Professor at Biotechnology Department of National Institute of Technology (NIT), Raipur, India. She did her MTech in Biotechnology from Indian Institute Technology (IIT), Kharagpur, and PhD from IIT, Hyderabad. During her research career, sheworked as DSTwoman scientist at the Indian Institute of Chemical Technology (IICT), Hyderabad. Her fields of specializations include bioinformatics, biotechnology, process modeling, evolutionaryoptimization,andartificialintelligence.Shegainedconsiderableexper- tise in the application of mathematical and engineering tools to biotechnological processes.Shehaspublishedmorethan18sci/scopusresearchpapersand25inter- nationalconferenceproceedings.ShecompletedaDSTwomanscientistprojectand is currently handling a DST-Early Career Research project and a CCOST project. She has morethan 4 yearsteaching experienceand 3 years research experience. xv Preface Thisbookisaddressedtostudents,researchers,andindustryprofessionalsconcern- ingtomultipledomainsofengineeringandtechnology.Itcoversthefundamentals, classical and advanced optimization topics with a number of examples and case studiesthatarebeneficialtothepersonnelofdifferentdisciplinestogainknowledge and apply to the problems encountered in their domains. General readers ofinitial chapters that cover classical optimization topics are expected to have familiarity with the fundamentals of mathematics and calculus. Readers of lateral chapters of advancedoptimizationtopicsareexpectedtohavefamiliaritywiththefundamentals of optimization along with their basic domain knowledge in engineering and science. Optimizationisofgreatinterestandithaswidespreadapplicationsinengineer- ing, science, and business. It has become a major technology contributor to the growthoftheindustry.Itis extensivelyused insolvingawidevarietyofproblems in design, operation, and analysis of engineering and technological processes. The initial chapters of this book emphasize various classical methods of optimization and their applications. This book mainly focuses on evolutionary, stochastic, and artificial intelligence optimization algorithms with a special emphasis on their design, analysis, and implementation to solve complex optimization problems and includes a number of real applications concerning chemical, biochemical, pharmaceutical, and environmental engineering processes. Formulation, design, and implementation of various advanced optimization strategies to solve a wide variety of base case and real engineering problems make this book beneficial to researchers working inmultipledomains. Chapter1ofthisbookprovidesmotivationforoptimizationwiththepresentation ofbasicfeaturesofoptimizationalongwithitsscope,examplesofapplications,and essentialcomponents.Furtherinthischapter,thebasicconceptsofoptimizationare describedintermsoffunctions, behavior offunctions,andmaximaandminima of functions. This chapter also deals with the region of search within the constraints, classification of problems in optimization, general solution procedure, and the obstacles of optimization. Chapter 2 discusses classical analytical methods of optimization. The classical optimization techniques are analytical in nature and make use of differential calculus to solve the problems involving continuous differentiable functions. These techniques with necessary and sufficient conditions are employed to find the optimum of unconstrained single variable functions and multivariable functionswith equality andinequality constraints. Chapter 3 elaborates numerical search methods for unconstrained optimization problems.Theclassicalanalyticalmethodsbasedonnecessaryconditionswiththeir analytical derivatives can yield exact solution for functions that have no complex form of expressions. These analytical methods are usually difficult to apply for nonlinear functions for which the analytical derivatives are hard to compute and for functions involving more variables. Most algorithms of unconstrained and xvii xviii Preface constrainedoptimizationmakeuseofnumericalsearchtechniquestolocatethemin- imum (maximum) of singlevariable and multivariable functions. These numerical searchmethodsfindtheoptimumbyusingthefunctionf(x)andsometimesderiva- tivevaluesoff(x)atsuccessivetrialpointsofx.Chapter3discussesvariousgradient and direct search methods that are used to solve single variable and multivariable optimization problems. In this chapter, various one-dimensional gradient search methods,polynomialapproximationmethods,multivariabledirectsearchmethods, andmultivariablegradient search methods with examplesare discussed. Chapter4describesvariousstochasticandevolutionaryoptimizationalgorithms. Classicaloptimizationmethodsfailtosolveproblemsthatposedifficultiesconcern- ing to dimensionality, differentiability, multimodality, nonlinearity in objective function and constraints, and problems that have many local optima. There has been a rapidly growing interest in advanced optimization algorithms over the last decade. Stochastic and evolutionary optimization methods are increasingly used to solve challenging optimization problems. These methods are typically inspired by some phenomena from nature and they are robust. These methods are capable of locating global optimum of multimodal functions and they have flexibility with ease of operation. These algorithms do not require any gradient information and are even suitable to solve discrete optimization problems. These methods are extensively used in the analysis, design, and operation of systems that are highly nonlinear, high dimensional, and noisy or for solving problems that are not easily solved by classical deterministic methods of optimization. Various stochastic and globaloptimizationmethodsarenowbecomingindustrystandard.Chapter4mainly focuses on evolutionary and stochastic optimization algorithms such as genetic algorithm, simulated annealing, differential evolution, ant colony optimization, tabusearch,particleswarmoptimization,artificialbeecolonyalgorithm,andcuckoo search algorithm. In Chapter 4, these algorithms are described in detail with flow schemes and implementation procedures. Implementation of stochastic global optimization methods to base case problems involving continuous and discrete numerical functions gives intriguing insight about the efficacy of these methods fortheirfurtherimplementationtorealengineeringapplications.Chapter5provides different base case applications and performance evaluation of various stochastic global optimization methods. Chapter 6 discusses application of stochastic evolutionary optimization tech- niques to chemical processes. The chemical industry is experiencing significant changes because of global market competition, strict bounds on product specifica- tions, pricing pressures, and environmental issues. Optimization is the most important approach that addresses the performance issues related to several areas of chemical process engineering including process design, process development, process modeling, process identification, process control, and real-time process operation. Optimization is also used in process synthesis, experimental design, planning, scheduling, distribution, and integration of process operations. Most of the chemical engineering problems exhibit highly nonlinear dynamics and often present nonconvexity,discontinuity,andmultimodality.Theclassicaldeterministic Preface xix optimizationmethodsarenoteffectiveinsolvingoptimizationproblemsofcomplex chemical processes and often require high computational time. Stochastic evolutionaryoptimizationmethodsarerobustnumericaltechniquesandarewidely usedtosolvecomplexchemicalengineeringproblemsthatarenoteasilysolvedby classical deterministic methods of optimization. Chapter 6 deals with various real applications of stochastic and evolutionary optimization strategies to different chemicalprocessesthatarehighlynonlinearandhighdimensional.Inthischapter, differentstochasticoptimization-basedmultistagedynamicoptimization,multiloop controllertuning,andnonlinearmodelpredictivecontrolstrategiesaredesignedand applied to complex and high-dimensional processes such as semibatch copoly- merization reactorsand reactivedistillation columns. Chapter 7 concentrates on application of stochastic evolutionary optimization techniques to biochemical processes. Bioprocess technology plays a vital role in delivering innovativeand sustainable products and processes tofulfill the needs of thesociety.Inthepresentsituationofincreasingenergydemand, depleting natural sources, and ever-demanding environmental awareness, bioprocesses occupy a unique position in converting variety of resources into useful products. Modeling and optimization techniques are increasingly used to understand and improve the cellular-based processes.Theadvantages ofthese techniques include thereduction of excessive experimentation, facilitating the most informative experiments, providing strategies to optimize and automate the processes and reducing cost and time in devising operational strategies. The model-based bioprocess optimization providesaquantitativeandsystematicframeworktomaximizeprocessprofitability, safety,andreliability.Chapter7mainlyfocusesonapplicationofstochasticevolu- tionaryoptimizationtechniquesformodelingandoptimizationofbiotechnological processes. In this chapter, various mathematical, empirical, and artificial neural network modelebased stochastic and evolutionary optimization strategies are designed and applied for optimization of different biotechnical processes such as Chinese hamster ovary (CHO) cells production, lipopeptide, and rhamnolipid biosurfactant processes. Chapter8presentsapplicationofevolutionaryandartificialintelligenceebased optimization techniques to pharmaceutical processes. Design, modeling, and optimization studies can lead to considerable benefits in pharmaceutical processes in terms of improvement in productivity and product quality as well as reduction in energy consumption and environmental pollution. In this chapter, different strategiesarederivedbycombiningartificialneuralnetworks,radialbasisfunction networks, and statistical response surface models with differential evolution, nonsorting differential evolution, and nonsorting genetic algorithms, and these strategies are applied for simultaneous optimization of pharmaceutical product formulation, multiobjective Pareto optimization of a pharmaceutical product formulation,andmultiobjectiveoptimizationofcytotoxicpotencyofmarinemacro- algaeonhumancarcinomacelllines.Chapter9focusesonapplicationofstochastic evolutionary optimization techniques to environmental processes. Modeling and optimizationstudiescanleadtoconsiderablebenefitstoenvironmentalengineering xx Preface systemsintermsofefficiencyimprovement,energyreduction,andpollutioncontrol. A variety of optimization approaches are used for the solution of environmental problemsintheareasofairpollution,solid,liquid,andindustrialwastemanagement, andenergymanagement.Thischaptermainlyfocusesonapplicationofstochasticand evolutionary optimization techniques to environmental processes concerning to industry wastewater treatment. Various process model and artificial intelligence modelebased strategies involving stochastic optimization algorithms such as ant colony optimization and tabu search are derived and applied for modeling and optimization of different wastewater treatment processes. Chapter 10 given at the end of the book represents the conclusions section. This chapter summarizes the essenceofthebookanditsbenefittothereaders. Manyreferenceswithavarietyofclassicalandadvancedoptimizationproblem titlesareincludedattheendofeachchapterofthisbook.Thesereferenceswillbe immensely useful to the readers to advance their general knowledge and domain knowledge inthe field ofoptimization. CHAPTER 1 Basic features and concepts of optimization Chapter outline 1.1 Introduction.........................................................................................................2 1.2 Basicfeatures......................................................................................................2 1.2.1 Optimizationanditsbenefits...............................................................2 1.2.2 Scopeforoptimization........................................................................3 1.2.3 Illustrativeexamples...........................................................................3 1.2.4 Essentialrequisitesforoptimization.....................................................6 1.3 Basicconcepts....................................................................................................7 1.3.1 Functionsinoptimization....................................................................7 1.3.2 Interpretationofbehavioroffunctions................................................12 1.3.3 Maximaandminimaoffunctions.......................................................15 1.3.4 Regionofsearchforconstrainedoptimization.....................................18 1.4 Classificationandgeneralprocedure...................................................................19 1.4.1 Classificationofoptimizationproblems..............................................19 1.4.2 Generalprocedureofsolvingoptimizationproblems............................23 1.4.3 Bottlenecksinoptimization...............................................................23 1.5 Summary...........................................................................................................24 References...............................................................................................................25 Optimization is the process of finding the set ofconditions required toachievethe best solution in a given situation. Optimization is of great interest and finds wide- spreaduseinengineering,science,economics,andoperationsresearch.Thisintro- ductory chapter presents the basic features and concepts that set the stage for the development ofoptimization methods inthe subsequent chapters. 1 StochasticGlobalOptimizationMethodsandApplicationstoChemical,Biochemical,PharmaceuticalandEnvironmentalProcesses. https://doi.org/10.1016/B978-0-12-817392-3.00001-6 Copyright©2020ElsevierInc.Allrightsreserved. 2 CHAPTER 1 Basic features and concepts of optimization 1.1 Introduction Awide variety of problems in design, operation, and analysis of engineering and technological processes can be resolved by optimization. This chapter provides themotivationforthetopicofoptimizationbymeansofpresentingitsbasicfeatures along with its scope, examples of its applications, and its essential components. Furthermore,itsbasicconceptsaredescribedintermsoffunctions,behavioroffunc- tions, and maxima and minima of functions. This chapter further deals with the region of search within the constraints, classification of problems in optimization, general solution procedure,and the obstacles of optimization. 1.2 Basic features Optimizationwithitsmathematicalprinciplesandtechniquesisusedtosolveawide varietyofquantitativeproblemsinmanydisciplines.Inindustrialenvironment,opti- mization can be used to take decisions at different levels. It is useful to begin the subject of optimizationwith itsbasicfeaturesand concepts. 1.2.1 Optimization and its benefits Optimizationistheprocessofselectingthebestcourseofactionfromtheavailable resources.Optimizationproblemsaremadeupofthreebasiccomponents:anobjec- tive function, a set of unknowns or decision variables, and a set of constraints. An objective function can be maximization or minimization type. In an industrial system, decisions are to be made either to minimize the cost or to maximize the profit.Profitmaximizationorcostminimizationisexpressedbymeansofaperfor- manceindex.Decisionvariablesarethevariablesthatengineersormanagerschoose inmakingtechnologicalormanagerialsystemtoachievethedesiredobjective.Opti- mizationhastofindthevaluesofdecisionvariablesthatyieldthebestvaluesofthe performancecriterion.Constraintsarerestrictionsimposedonthesystembywhich the decisionvariablesare chosen tomaximizethe benefit orminimizethe effort. Optimization has widespread applications in engineering and science. It has becomeamajortechnologycontributortothegrowthoftheindustry.Inplantoper- ations, optimization provides improved plant performance in terms of improved yields of valuable products, reduced energy consumption, and higher processing rates. Optimization can also benefit the plants by means of reduced maintenance cost,lessequipmentwear,andbetterstaffutilization.Ithelpsinplanningandsched- ulingofefficientconstructionofplants.Withthesystematicidentificationofobjec- tive,constraints,anddegreesoffreedominprocessesorplants,optimizationleadsto provide improved quality of design, faster and more reliable trouble shooting, and faster decision-making. It helpsin minimizing the inventorycharges and increases overall efficiency with the allocation of resources or services among various processes or activities. It also facilitates to reduce transportation charges through strategic planning of distribution networks for products and procurement of raw materials fromdifferent sources.
The list of books you might like
