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

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

Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control presents various mechanistic model based state estimators and data-driven model based state estimators with a special emphasis on their development and applications to process monitoring, fault diagnosis and control. The design and analysis of different state estimators are highlighted with a number of applications and case studies concerning to various real chemical and biochemical processes. The book starts with the introduction of basic concepts, extending to classical methods and successively leading to advances in this field.

Design and implementation of various classical and advanced state estimation methods to solve a wide variety of problems makes this book immensely useful for the audience working in different disciplines in academics, research and industry in areas concerning to process monitoring, fault diagnosis, control and related disciplines.

Describes various classical and advanced versions of mechanistic model based state estimation algorithms Describes various data-driven model based state estimation techniques Highlights a number of real applications of mechanistic model based and data-driven model based state estimators/soft sensors Beneficial to those associated with process monitoring, fault diagnosis, online optimization, control and related areas

Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control presents various mechanistic model based state estimators and data-driven model based state estimators with a special emphasis on their development and applications to process monitoring, fault diagnosis and control. The design and analysis of different state estimators are highlighted with a number of applications and case studies concerning to various real chemical and biochemical processes. The book starts with the introduction of basic concepts, extending to classical methods and successively leading to advances in this field.Design and implementation of various classical and advanced state estimation methods to solve a wide variety of problems makes this book immensely useful for the audience working in different disciplines in academics, research and industry in areas concerning to process monitoring, fault diagnosis, control and related disciplines.- Describes various classical and advanced versions of mechanistic model based state estimation algorithms- Describes various data-driven model based state estimation techniques- Highlights a number of real applications of mechanistic model based and data-driven model based state estimators/soft sensors- Beneficial to those associated with process monitoring, fault diagnosis, online optimization, control and related areas

Advances in Process Control with Real Applications presents various advanced controllers, including the formulation, design, and implementation of various advanced control strategies for a wide variety of processes. These strategies include generalized predictive control with and without constraints; linear and nonlinear model predictive control; dynamic matrix control; nonlinear control, such as generic model control, globally linearizing control, and nonlinear internal model control; optimal and optimizing control; inferential control; intelligent control based on fuzzy reasoning and neural networks; and controllers based on stochastic and evolutionary optimization.This book will be highly beneficial to students, researchers, and industry professionals working in process design, process monitoring, process systems engineering, process operations and control, and related areas.

Describes various advanced controllers for the control of complex nonlinear processesProvides the fundamentals, algorithms, approaches, control strategies, and implementation procedures systematicallyHighlights the significance and importance of advanced process control with many real applications

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

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

Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control presents various mechanistic model based state estimators and data-driven model based state estimators with a special emphasis on their development and applications to process monitoring, fault diagnosis and control. The design and analysis of different state estimators are highlighted with a number of applications and case studies concerning to various real chemical and biochemical processes. The book starts with the introduction of basic concepts, extending to classical methods and successively leading to advances in this field.

Design and implementation of various classical and advanced state estimation methods to solve a wide variety of problems makes this book immensely useful for the audience working in different disciplines in academics, research and industry in areas concerning to process monitoring, fault diagnosis, control and related disciplines.

Describes various classical and advanced versions of mechanistic model based state estimation algorithms Describes various data-driven model based state estimation techniques Highlights a number of real applications of mechanistic model based and data-driven model based state estimators/soft sensors Beneficial to those associated with process monitoring, fault diagnosis, online optimization, control and related areas

Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control presents various mechanistic model based state estimators and data-driven model based state estimators with a special emphasis on their development and applications to process monitoring, fault diagnosis and control. The design and analysis of different state estimators are highlighted with a number of applications and case studies concerning to various real chemical and biochemical processes. The book starts with the introduction of basic concepts, extending to classical methods and successively leading to advances in this field.Design and implementation of various classical and advanced state estimation methods to solve a wide variety of problems makes this book immensely useful for the audience working in different disciplines in academics, research and industry in areas concerning to process monitoring, fault diagnosis, control and related disciplines.- Describes various classical and advanced versions of mechanistic model based state estimation algorithms- Describes various data-driven model based state estimation techniques- Highlights a number of real applications of mechanistic model based and data-driven model based state estimators/soft sensors- Beneficial to those associated with process monitoring, fault diagnosis, online optimization, control and related areas

Advances in Process Control with Real Applications presents various advanced controllers, including the formulation, design, and implementation of various advanced control strategies for a wide variety of processes. These strategies include generalized predictive control with and without constraints; linear and nonlinear model predictive control; dynamic matrix control; nonlinear control, such as generic model control, globally linearizing control, and nonlinear internal model control; optimal and optimizing control; inferential control; intelligent control based on fuzzy reasoning and neural networks; and controllers based on stochastic and evolutionary optimization.This book will be highly beneficial to students, researchers, and industry professionals working in process design, process monitoring, process systems engineering, process operations and control, and related areas.

Describes various advanced controllers for the control of complex nonlinear processesProvides the fundamentals, algorithms, approaches, control strategies, and implementation procedures systematicallyHighlights the significance and importance of advanced process control with many real applications