Stochastic Global Optimization Methods and Applications to Chemical Biochemical Pharmaceutical and Environmental Processes 1st Edition by Venkateswarlu, Satya Eswari Jujjavarapu 0128173939 9780128173930

Stochastic Global Optimization Methods and Applications to Chemical, Biochemical, Pharmaceutical and Environmental Processes 1st Edition by Ch. Venkateswarlu, Satya Eswari Jujjavarapu – Ebook PDF Instant Download/DeliveryISBN:  0128173939, 9780128173930 

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ISBN-10 :  0128173939 

ISBN-13 :  9780128173930

Author : Ch. Venkateswarlu, Satya Eswari Jujjavarapu 

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.

Stochastic Global Optimization Methods and Applications to Chemical, Biochemical, Pharmaceutical and Environmental Processes 1st Table of contents:

Chapter 1. Basic features and concepts of optimization
1.1. Introduction
1.2. Basic features
1.3. Basic concepts
1.4. Classification and general procedure
1.5. Summary
Chapter 2. Classical analytical methods of optimization
2.1. Introduction
2.2. Statement of optimization problem
2.3. Analytical methods for unconstrained single-variable functions
2.4. Analytical methods for unconstrained multivariable functions
2.5. Analytical methods for multivariable optimization problems with equality constraints
2.6. Analytical methods for solving multivariable optimization problems with inequality constraints
2.7. Limitations of classical optimization methods
2.8. Summary
Chapter 3. Numerical search methods for unconstrained optimization problems
3.1. Introduction
3.2. Classification of numerical search methods
3.3. One-dimensional gradient search methods
3.4. Polynomial approximation methods
3.5. Multivariable direct search methods
3.6. Multivariable gradient search methods
3.7. Summary
Chapter 4. Stochastic and evolutionary optimization algorithms
4.1. Introduction
4.2. Genetic algorithms
4.3. Simulated annealing
4.4. Differential evolution
4.5. Ant colony optimization
4.6. Tabu search
4.7. Particle swarm optimization
4.8. Artificial bee colony optimization
4.9. Cuckoo search algorithm
4.10. Summary
Chapter 5. Application of stochastic and evolutionary optimization algorithms to base case problems
5.1. Introduction
5.2. Examples of numerical functions
5.3. Application of genetic algorithm to base case problems
5.4. Application of simulated annealing to base case problems
5.5. Application of differential evolution to base case problems
5.6. Application of ant colony optimization to base case problems
5.7. Application of particle swarm optimization to base case problems
5.8. Application of artificial bee colony algorithm to base case problems
5.9. Analysis of results
5.10. Summary
Chapter 6. Application of stochastic evolutionary optimization techniques to chemical processes
6.1. Introduction
6.2. Process model–based multistage dynamic optimization of a copolymerization reactor using differential evolution
6.3. Process model–based multistage dynamic optimization of a copolymerization reactor using tabu search
6.4. Optimization of multiloop proportional–integral controller parameters of a reactive distillation column using genetic algorithm
6.5. Stochastic optimization–based nonlinear model predictive control of reactive distillation column
6.6. Summary
Chapter 7. Application of stochastic evolutionary optimization techniques to biochemical processes
7.1. Introduction
7.2. Bioprocess engineering—significance of modeling and optimization
7.3. Media optimization of Chinese hamster ovary cells production process using differential evolution
7.4. Response surface model–based ant colony optimization of lipopeptide biosurfactant process
7.5. ANN model–based multiobjective optimization of rhamnolipid biosurfactant process using NSDA strategy
7.6. ANN-DE strategy for simultaneous optimization of rhamnolipid biosurfactant process
7.7. Summary
Chapter 8. Application of stochastic evolutionary optimization techniques to pharmaceutical processes
8.1. Introduction
8.2. Quantitative model–based pharmaceutical formulation
8.3. Simultaneous optimization of pharmaceutical (trapidil) product formulation using radial basis function network methodology
8.4. Multiobjective Pareto optimization of a pharmaceutical product formulation using radial basis function network and differential evolution
8.5. Multiobjective optimization of pharmaceutical formulation using response surface methodology and differential evolution
8.6. Multiobjective optimization of cytotoxic potency of a marine macroalgae on human carcinoma cell lines using nonsorting genetic algorithm
8.7. Summary
Chapter 9. Application of stochastic evolutionary optimization techniques to environmental processes
9.1. Introduction
9.2. Modeling and optimization of different environmental processes
9.3. Process model–based optimization of distillery industry wastewater treating fixed bed anaerobic biofilm reactor using ant colony optimization
9.4. Process model–based optimization of pharmaceutical industry wastewater treating a fixed bed anaerobic biofilm reactor using tabu search
9.5. ACO-based strategy for parameter estimation in pharmaceutical industry wastewater treatment biofilm process
9.6. Optimal estimation of wastewater treating biofilm reaction kinetics using hybrid mechanistic-neural network rate function modeling approach
9.7. Summary
Chapter 10. Conclusions

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Tags: Stochastic Global, Optimization Methods, Applications, Chemical, Biochemical, Pharmaceutical, Environmental Processes, Venkateswarlu, Satya Eswari Jujjavarapu