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Stochastic global optimization methods and applications to chemical, biochemical, pharmaceutical and environmental processes presents various algorithms that include the genetic algorithm, simulated annealing, differential evolution, ant colony optimization, tabu search, particle swarm optimization, artificial bee colony optimization, and cuckoo search algorithm. The design and analysis of these algorithms is studied by applying them to solve various base case and complex optimization problems concerning chemical, biochemical, pharmaceutical, and environmental engineering processes.
Design and implementation of various classical and advanced optimization strategies to solve a wide variety of optimization problems makes this book beneficial to graduate students, researchers, and practicing engineers working in multiple domains. This book mainly focuses on stochastic, evolutionary, 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.
- Presents various classical, stochastic, evolutionary, and artificial intelligence optimization algorithms for the benefit of the audience in different domains
- Outlines design, analysis, and implementation of optimization strategies to solve complex optimization problems of different domains
- Highlights numerous real applications concerning chemical, biochemical, pharmaceutical, and environmental engineering processes
Engineers, researchers and scientists in chemical, biotechnological, pharmaceutical environmental and electrical engineering fields. A reference book to masters and Ph.D level research scholars
1. Basic Concepts
2. Classical Analytical Methods of Optimization
3. Numerical Search Methods for Unconstrained Optimization Problems
4. Stochastic and Evolutionary Optimization Algorithms
5. Application of Stochastic and Evolutionary Optimization Algorithms to Base Case Problems
6. Applications to Chemical Processes
7. Applications to Biochemical Processes
8. Applications to Pharmaceutical Processes
9. Applications to Environmental Processes
- No. of pages:
- © Elsevier 2020
- 18th November 2019
- Paperback ISBN:
- eBook ISBN:
Dr. Ch. Venkateswarlu, the Director R&D at BV Raju Institute of Technology (BVRIT), Narsapur, Greater Hyderabad, India has earlier worked as Scientist, Senior Principal Scientist and Chief Scientist at Indian Institute of Chemical Technology (IICT), Hyderabad, a premier research and development (R&D) institute of Council of Scientific and Industrial Research (CSIR, India). Prior to Director R&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 post graduation and Ph.D in Chemical Engineering from Osmania University, Hyderabad, India. He holds 35 years R&D experience along with 19 years teaching experience and 2 years industry experience. His research interests lie in the areas of dynamic process modelling & simulation, process identification & dynamic optimization, process monitoring & fault diagnosis, state estimation & soft sensing, statistical process control & advanced process control, applied engineering mathematics & evolutionary computing, artificial intelligence & expert systems, and bioprocess engineering & bio-informatics. He published more than 100 research papers in peer journals of repute along with few international and national proceeding publications. 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 Ph.D students. He served as a long term guest faculty for premier institutes like Bhaba Atomic Research Centre Scientific Officers Training, BITS Pilani MS (off-campus) and IICT-CDAC Bioinformatics Programs. He is a Fellow of Andhra Pradesh Akademi of Sciences and Telangana State Academy of Sciences in India. He received various awards in recognition to his R&D and academic contributions.
Chief Scientist (Retd.), Indian Institute of Chemical Technology (CSIR-IICT), Hyderabad, India
Dr. Satya Eswari Jujjavarapu is currently an Assistant Professor in the Biotechnology Department of National Institute of Technology (NIT), Raipur, India. She did her M.Tech in Biotechnology from Indian Institute Technology (IIT) Kharagpur and Ph.D from IIT, Hyderabad. During her research career, she worked as DST-woman scientist at Indian Institute of Chemical Technology (IICT) Hyderabad. Her fields of specializations include bioinformatics, biotechnology, process modelling, evolutionary optimization and artificial intelligence. She gained considerable expertise in the application of mathematical and engineering tools to biotechnological processes. She has published more than 18 sci/scopus research papers and 25 in international conference proceedings. She completed a DST woman scientist project and is currently handling a DST-Early career research project and a CCOST project. She has more than 4 years teaching experience and 3 years research experience.
Department of Biotechnology, National Institute of Technology, Raipur, India
"The book contains a good introduction to optimization algorithms including classical analytical methods, stochastic and evolutionary optimization algorithms and their applications. For algorithms, additionally to their descriptions, advantages and disadvantages are discussed and basic case problems are analyzed. A particular consideration is given to stochastic and evolutionary optimization algorithms because they can successfully overcome difficulties connected with bad differentiability, high dimensionality, multimodality and nonlinearity in objective functions and constraints. Probably the most valuable contribution of the book is application of optimization algorithms to real-life problems which were solved in chemical, biochemical, pharmaceutical and environmental processes. The book can be recommended to graduate students, researchers and practicing engineers. The book consists of ten chapters. In Chapter 1, basic features of optimization are introduced. In Chapters 2, classical analytical methods including optimization with constraints are presented. Chapter 3 contains numerical search methods, e.g., gradient method. In Chapter 4, stochastic and artificial intelligence optimization algorithms are considered: genetic algorithms, simulated annealing, differential evolution, ant colony optimization, tabu search, particle swarm optimization, artificial bee colony optimization, cuckoo search algorithm. In Chapter 5, these algorithms are applied to base case problems.
In Chapter 6, differential evolution optimization method is applied to control problem in chemistry. In Chapter 7, application of artificial neural network is applied to optimization of biochemical processes. Chapter 8 describes application to multiobjective optimization. In Chapter 9, artificial intelligence optimization algorithms are applied to optimization of environmental processes. Chapter 10 contains conclusions." --ZBMath
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