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

By Ch. Venkateswarlu and 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.

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

• Language: English
• Publisher: Elsevier Science
• Release date: Nov 18, 2019
• ISBN: 9780128173930

Ch. Venkateswarlu

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.

Book preview

Preface

This book is addressed to students, researchers, and industry professionals concerning to multiple domains of engineering and technology. It covers the fundamentals, classical and advanced optimization topics with a number of examples and case studies that are beneficial to the personnel of different disciplines to gain knowledge and apply to the problems encountered in their domains. General readers of initial chapters that cover classical optimization topics are expected to have familiarity with the fundamentals of mathematics and calculus. Readers of lateral chapters of advanced optimization topics are expected to have familiarity with the fundamentals of optimization along with their basic domain knowledge in engineering and science.

Optimization is of great interest and it has widespread applications in engineering, science, and business. It has become a major technology contributor to the growth of the industry. It is extensively used in solving a wide variety of problems 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 in multiple domains.

Chapter 1 of this book provides motivation for optimization with the presentation of basic features of optimization along with its scope, examples of applications, and essential components. Further in this chapter, the basic concepts of optimization are described in terms of functions, behavior of functions, and maxima and minima 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 functions with equality and inequality constraints.

Chapter 3 elaborates numerical search methods for unconstrained optimization problems. The classical analytical methods based on necessary conditions with their 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 constrained optimization make use of numerical search techniques to locate the minimum (maximum) of single variable and multivariable functions. These numerical search methods find the optimum by using the function f(x) and sometimes derivative values of f(x) at successive trial points of x. Chapter 3 discusses various gradient and direct search methods that are used to solve single variable and multivariable optimization problems. In this chapter, various one-dimensional gradient search methods, polynomial approximation methods, multivariable direct search methods, and multivariable gradient search methods with examples are discussed.

Chapter 4 describes various stochastic and evolutionary optimization algorithms. Classical optimization methods fail to solve problems that pose difficulties concerning 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 global optimization methods are now becoming industry standard. Chapter 4 mainly focuses on evolutionary and stochastic optimization algorithms such as genetic algorithm, simulated annealing, differential evolution, ant colony optimization, tabu search, particle swarm optimization, artificial bee colony algorithm, and cuckoo 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 for their further implementation to real engineering applications. Chapter 5 provides different base case applications and performance evaluation of various stochastic global optimization methods.

Chapter 6 discusses application of stochastic evolutionary optimization techniques to chemical processes. The chemical industry is experiencing significant changes because of global market competition, strict bounds on product specifications, 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, and multimodality. The classical deterministic optimization methods are not effective in solving optimization problems of complex chemical processes and often require high computational time. Stochastic evolutionary optimization methods are robust numerical techniques and are widely used to solve complex chemical engineering problems that are not easily solved by classical deterministic methods of optimization. Chapter 6 deals with various real applications of stochastic and evolutionary optimization strategies to different chemical processes that are highly nonlinear and high dimensional. In this chapter, different stochastic optimization-based multistage dynamic optimization, multiloop controller tuning, and nonlinear model predictive control strategies are designed and applied to complex and high-dimensional processes such as semibatch copolymerization reactors and reactive distillation columns.

Chapter 7 concentrates on application of stochastic evolutionary optimization techniques to biochemical processes. Bioprocess technology plays a vital role in delivering innovative and sustainable products and processes to fulfill the needs of the society. In the present situation of increasing energy demand, 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. The advantages of these techniques include the reduction 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 provides a quantitative and systematic framework to maximize process profitability, safety, and reliability. Chapter 7 mainly focuses on application of stochastic evolutionary optimization techniques for modeling and optimization of biotechnological processes. In this chapter, various mathematical, empirical, and artificial neural network model–based 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.

Chapter 8 presents application of evolutionary and artificial intelligence–based 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 strategies are derived by combining artificial neural networks, radial basis function 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, and multiobjective optimization of cytotoxic potency of marine macroalgae on human carcinoma cell lines. Chapter 9 focuses on application of stochastic evolutionary optimization techniques to environmental processes. Modeling and optimization studies can lead to considerable benefits to environmental engineering systems in terms of efficiency improvement, energy reduction, and pollution control. A variety of optimization approaches are used for the solution of environmental problems in the areas of air pollution, solid, liquid, and industrial waste management, and energy management. This chapter mainly focuses on application of stochastic and evolutionary optimization techniques to environmental processes concerning to industry wastewater treatment. Various process model and artificial intelligence model–based 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 essence of the book and its benefit to the readers.

Many references with a variety of classical and advanced optimization problem titles are included at the end of each chapter of this book. These references will be immensely useful to the readers to advance their general knowledge and domain knowledge in the field of optimization.

Chapter 1: Basic features and concepts of optimization

Abstract

The basic features, concepts, and benefits of optimization are explained in terms of its scope, illustrative examples, prerequisites, functions, behavior of functions, maxima and minima of functions, search region, classification of optimization problems, general solution procedure, and the obstacles of optimization. Learning about the mathematical concepts involved with the subject of optimization with the illustrative examples in engineering and technology domain along with the concepts and definitions makes this topic more useful to the readers and learners of different domains even if they are not directly associated with this field. These fundamental concepts of optimization set the stage to the further topics of the book, which are organized in order to cover various classical and advanced methods and their applications to different domains.

Keywords

Behavior of functions; Classification of optimization problems; Functions; Maxima and minima; Region of search

1.1 Introduction

1.2 Basic features

1.2.1 Optimization and its benefits

1.2.2 Scope for optimization

1.2.3 Illustrative examples

1.2.4 Essential requisites for optimization

1.3 Basic concepts

1.3.1 Functions in optimization

1.3.2 Interpretation of behavior of functions

1.3.3 Maxima and minima of functions

1.3.4 Region of search for constrained optimization

1.4 Classification and general procedure

1.4.1 Classification of optimization problems

1.4.2 General procedure of solving optimization problems

1.4.3 Bottlenecks in optimization

1.5 Summary

Optimization is the process of finding the set of conditions required to achieve the best solution in a given situation. Optimization is of great interest and finds widespread use in engineering, science, economics, and operations research. This introductory chapter presents the basic features and concepts that set the stage for the development of optimization methods in the subsequent chapters.

1.1. Introduction

A wide variety of problems in design, operation, and analysis of engineering and technological processes can be resolved by optimization. This chapter provides the motivation for the topic of optimization by means of presenting its basic features along with its scope, examples of its applications, and its essential components. Furthermore, its basic concepts are described in terms of functions, behavior of functions, 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

Optimization with its mathematical principles and techniques is used to solve a wide variety of quantitative problems in many disciplines. In industrial environment, optimization can be used to take decisions at different levels. It is useful to begin the subject of optimization with its basic features and concepts.

1.2.1. Optimization and its benefits

Optimization is the process of selecting the best course of action from the available resources. Optimization problems are made up of three basic components: an objective 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. Profit maximization or cost minimization is expressed by means of a performance index. Decision variables are the variables that engineers or managers choose in making technological or managerial system to achieve the desired objective. Optimization has to find the values of decision variables that yield the best values of the performance criterion. Constraints are restrictions imposed on the system by which the decision variables are chosen to maximize the benefit or minimize the effort.

Optimization has widespread applications in engineering and science. It has become a major technology contributor to the growth of the industry. In plant operations, 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, less equipment wear, and better staff utilization. It helps in planning and scheduling of efficient construction of plants. With the systematic identification of objective, constraints, and degrees of freedom in processes or plants, optimization leads to provide improved quality of design, faster and more reliable trouble shooting, and faster decision-making. It helps in minimizing the inventory charges 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 from different sources.

1.2.2. Scope for optimization

Optimization can be applied to the entire company, a plant, a process, a single unit operation, and a single piece of equipment. In typical industrial environment, optimization can be used in taking decisions at management level, plant design level, and plant operations level [1].

Management level: At management level, optimization helps in taking decisions concerning to project evaluation, product selection, corporate budget, investment in sales, research and development, and new plant construction. At this stage the information available is qualitative and uncertain as these decisions are made well in advance the plant design level.

Plant design level: Decisions made at this level are concerned to choice of the process whether batch or continuous, nominal operating conditions, configuration of the plant, size of individual units, use of flow sheeting programs, and aid of process design simulators.

Plant operations level: Decisions at this stage include allocation of raw materials on a weekly/daily basis, day-to-day optimization of a plant to minimize steam consumption, cooling water consumption, operating controls for a given unit at certain temperatures and pressures, and costs toward shipping, transportation, and distribution of products.

1.2.3. Illustrative examples

The basic applications of optimization are explained in terms of different illustrative examples concerning to industry.

(a) Optimum pipe diameter for pumping fluid

One typical example is the problem of determining the optimum pipe diameter for pumping a given amount of fluid from one point to another. Here the amount of fluid pumped between the two points can be accomplished by means of different pipe diameters. However, this task can be realized by one particular pipe diameter which minimizes the total cost representing the cost for pumping the liquid and the cost for the installed piping system as shown in Fig. 1.1 [2]. From the figure it can be observed that the pumping cost increases with decreased size of pipe diameter because of frictional effects. But the fixed charges for the pipeline become lower due to reduced capital investment because of the use of smaller pipe diameter. The optimum diameter is located at point E where the sum of the pumping costs and fixed costs for the pipeline becomes a