Dr. Ch. Venkateswarlu M.Tech., Ph. D, has formerly 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. Later, he worked as Director R&D at BV Raju Institute of Technology (BVRIT), Narsapur, Greater Hyderabad. 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 and industry experience along with 20 years teaching experience. His research interests lie in the areas of conventional process control & advanced process control, dynamic process modelling & simulation, process identification & dynamic optimization, process monitoring & fault diagnosis, state estimation & soft sensing, applied engineering mathematics & evolutionary computing, artificial intelligence & expert systems, and bioprocess engineering & bio-informatics. He published more than 120 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. He authored two books published by Elsevier along with few book chapters. He is also in editorial boards of few international journals. 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. 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.

"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

• 1. Basic Concepts2. Classical Analytical Methods of Optimization3. Numerical Search Methods for Unconstrained Optimization Problems4. Stochastic and Evolutionary Optimization Algorithms5. Application of Stochastic and Evolutionary Optimization Algorithms to Base Case Problems6. Applications to Chemical Processes7. Applications to Biochemical Processes8. Applications to Pharmaceutical Processes9. Applications to Environmental Processes10. Conclusions

Dr. Ch. Venkateswarlu M.Tech., Ph. D, has formerly 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. Later, he worked as Director R&D at BV Raju Institute of Technology (BVRIT), Narsapur, Greater Hyderabad. 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 and industry experience along with 20 years teaching experience. His research interests lie in the areas of conventional process control & advanced process control, dynamic process modelling & simulation, process identification & dynamic optimization, process monitoring & fault diagnosis, state estimation & soft sensing, applied engineering mathematics & evolutionary computing, artificial intelligence & expert systems, and bioprocess engineering & bio-informatics. He published more than 120 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. He authored two books published by Elsevier along with few book chapters. He is also in editorial boards of few international journals. 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. 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.

"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

• 1. Basic Concepts2. Classical Analytical Methods of Optimization3. Numerical Search Methods for Unconstrained Optimization Problems4. Stochastic and Evolutionary Optimization Algorithms5. Application of Stochastic and Evolutionary Optimization Algorithms to Base Case Problems6. Applications to Chemical Processes7. Applications to Biochemical Processes8. Applications to Pharmaceutical Processes9. Applications to Environmental Processes10. Conclusions

Dr. Ch. Venkateswarlu M.Tech., Ph. D, has formerly 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. Later, he worked as Director R&D at BV Raju Institute of Technology (BVRIT), Narsapur, Greater Hyderabad. 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 and industry experience along with 20 years teaching experience. His research interests lie in the areas of conventional process control & advanced process control, dynamic process modelling & simulation, process identification & dynamic optimization, process monitoring & fault diagnosis, state estimation & soft sensing, applied engineering mathematics & evolutionary computing, artificial intelligence & expert systems, and bioprocess engineering & bio-informatics. He published more than 120 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. He authored two books published by Elsevier along with few book chapters. He is also in editorial boards of few international journals. 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. 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.

"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

• 1. Basic Concepts2. Classical Analytical Methods of Optimization3. Numerical Search Methods for Unconstrained Optimization Problems4. Stochastic and Evolutionary Optimization Algorithms5. Application of Stochastic and Evolutionary Optimization Algorithms to Base Case Problems6. Applications to Chemical Processes7. Applications to Biochemical Processes8. Applications to Pharmaceutical Processes9. Applications to Environmental Processes10. Conclusions