Stochastic Global Optimization Methods and Applications to Chemical, Biochemical, Pharmaceutical and Environmental Processes - 1st Edition - ISBN: 9780128173923

Stochastic Global Optimization Methods and Applications to Chemical, Biochemical, Pharmaceutical and Environmental Processes

1st Edition

Authors: Ch. Venkateswarlu Jujjavarapu Satya Eswari
Paperback ISBN: 9780128173923
Imprint: Elsevier
Published Date: 1st November 2019
Page Count: 287
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Description

Stochastic Global Optimization Methods and Applications to Chemical, Biochemical, Pharmaceutical and Environmental Processes focuses on the evolutionary, stochastic and artificial intelligence optimization algorithms. It gives a special emphasis to their design, analysis and implementation to solve complex optimization problems of different engineering fields. Drs. Satya Eswari and Ch. Venkateswarlu detail several real applications of the algorithms in chemical, biochemical, pharmaceutical, and environmental engineering processes. This book is unequalled in the field in its thorough description of the formulation, design and implementation of various advanced optimization strategies to solve wide variety of real engineering problems.

It presents various algorithms, including the genetic algorithm (GA), simulated annealing (SA), differential evolution (DE), ant colony optimization (ACO), tabu search (TS), artificial neural networks (ANN) and radial basis function networks (RBFN). The design and analysis of these algorithms is then studied by applying them to solve complex optimization problems.

Key Features

  • Outlines design, analysis and implementation of optimization strategies to solve complex optimization problems of different domains
  • Highlights a number of real applications concerning chemical, biochemical, pharmaceutical and environmental engineering processes

Readership

Engineers, researchers and scientists in chemical, biotechnological, pharmaceutical environmental and electrical engineering fields. A reference book to masters and Ph.D level research scholars

Table of Contents

1. Basic Concepts 1.1 What is optimization ? 1.2 Why to optimize ? What are benefits ? 1.3 Scope for optimization 14 Essential requisites 15 Sequence of steps 16 Pre-requisites 1.7 Problem statement

2. A Brief Review of Classical Methods 2.1 Classification of optimization problems 2.2 Classification of optimization methods 2.3 One dimensional search methods 2.4 Multivariable optimization methods 2.5 Limitations of classical optimization methods

3. Stochastic and Evolutionary Optimization Algorithms 3.1 Genetic algorithm (GA) 3.2 Simulated annealing (SA) 3.3 Differential evolution (DE) 3.4 Ant colony optimization (ACO) 3.5 Tabu search (TS) 3.6 Artifiocial neural networks (ANN) 3.7 Radial basis function networks (RBFN)

4: Application of Stochastic and Evolutionary Optimization Algorithms to Base Case Problems  4.1 Process model based multistage dynamic optimization of a copolymerization reactor using differential evolution. 4.2 Process model based multistage dynamic optimization of a Copolymerization reactor using tabu search. 4.3 Optimizing the controller parameters of a reactive distillation column using genetic algorithm. 4.4 Optimizing the control sequence of a reactive distillation column using simulated annealing. 4.5 Artificial neural network model based multiobjective optimization of a biosurfactant process using differential evolution. 4.6 Response surface model based multiobjective optimization of a biosurfactant process using ant colony optimization. 4.7 Media optimization of chinese hamster ovary (CHO) cells production process using differential evolution. 4.8 Optimizing the biofilm kinetics of industry wastewater treating biofilm reactor using ant colony optimization. 4.9 Optimizing the biofilm kinetics of industry wastewater treating biofilm reactor using tabu search. 4.10 Optimizing the pharmaceutical formulation using radial basis function network and distance minimizing function. 4.11 Optimizing reaction rates of a biofilm process using hybrid mechanistic- neural network rate function model.

5. Applications to Chemical Processes

6. Applications to Biochemical Processes

7. Applications to Pharmaceutical Processes

8. Applications to Environmental Processes

9. Conclusions

Details

No. of pages:
287
Language:
English
Copyright:
© Elsevier 2020
Published:
Imprint:
Elsevier
Paperback ISBN:
9780128173923

About the Author

Ch. Venkateswarlu

Dr. Ch. Venkateswarlu has previously worked as Scientist, Senior Principal Scientist, and Chief Scientist (Director Grade) at the Indian Institute of Chemical Technology (IICT) at Hyderabad, a premier research and development (R&D) institute of the Council of Scientific and Industrial Research (CSIR). Currently, he is working as the Principal and Professor of B V Raju Institute of Technology, Narsapur and also functioned as the Head of Chemical Engineering Department of the same institute until recently. He received his degree from Andhra University, as well as one from the Indian Institute of Chemical Engineers, and a Ph.D in Chemical Engineering from Osmania University. He has over thirty-three years of R&D experience, eighteen years of teaching experience, and 2 years of industry experience. His research interests include dynamic process modelling and simulation, process optimization, process monitoring and fault diagnosis, statistical and advanced process control, applied engineering mathematics & evolutionary computing, artificial intelligence, and bioprocess engineering. He has published more than 85 research papers in international journals of high repute, along with few international proceeding publications. He is also credited with 70 national conference proceedings and technical paper presentations. He has executed more than 10 R&D projects sponsored by DST and Industry. He has delivered more than 85 invited lectures on various specialized technical topics. He is a reviewer for 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 member for premier institutes like Bhaba Atomic Research Centre Scientific Officers Training, BITS Pilani Master level and IICT-CDAC Bioinformatics programs. He has received various awards in recognition to his R&D contributions. He is a Fellow of Andhra Pradesh Akademi of Sciences.

Affiliations and Expertise

B V Raju Institute of Technology, Narsapur, India

Jujjavarapu Satya Eswari

Dr. Jujjavarapu Satya Eswari is currently an Assistant professor at Biotechnology Department of National Institute of Technology (NIT) in Raipur, India. She earned her Masters degree in Technology with a concentration in Biotechnology from the Indian Institute Technology (IIT), Kharagpur and her Ph.D from IIT, Hyderabad. During her research career, she worked as a Scientist in the department of science and technology (DST) at the Indian Institute of Chemical Technology (IICT), Hyderabad. She has rigorously pursued research in the areas of bioinformatics, bioprocesses and product development. She gained pioneering expertise in the application of mathematical and engineering tools to biotechnological processes. Her fields of specializations include bioinformatics, biotechnology, process modelling, evolutionary optimization, and artificial intelligence. She has published more than 18 Sci and Scopus research papers and 25 in international conference proceedings. Her research contributions have received global recognition. She completed a DST woman scientist project (22 lakhs) and is currently handling a DST-Early career research project (43 lakhs) and a CCOST (4 lakhs). She has more than four years of teaching experience and more than 3 years of research experience.

Affiliations and Expertise

Department of Biotechnology, National Institute of Technology, Raipur, India

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