Urmila Diwekar – författare

Optimization has pervaded all spheres of human endeavor. Although op- mization has been practiced in some form or other from the early prehistoric era, this area has seen progressive growth during the last ?ve decades. M- ern society lives not only in an environment of intense competition but is also constrained to plan its growth in a sustainable manner with due concern for conservation of resources. Thus, it has become imperative to plan, design, operate, and manage resources and assets in an optimal manner. Early - proaches have been to optimize individual activities in a standalone manner, however,thecurrenttrendistowardsanintegratedapproach:integratings- thesis and design, design and control, production planning, scheduling, and control. The functioning of a system may be governed by multiple perf- mance objectives. Optimization of such systems will call for special strategies for handling the multiple objectives to provide solutions closer to the systems requirement. Uncertainty and variability are two issues which render op- mal decision making di?cult. Optimization under uncertainty would become increasingly important if one is to get the best out of a system plagued by uncertain components. These issues have thrown up a large number of ch- lenging optimization problems which need to be resolved with a set of existing and newly evolving optimization tools. Optimization theory had evolved initially to provide generic solutions to optimizationproblemsinlinear,nonlinear,unconstrained,andconstrained- mains. Theseoptimization problems wereoften called mathematical progr- mingproblemswithtwodistinctiveclassi?cations,namelylinearandnonlinear programming problems.

Optimization has pervaded all spheres of human endeavor. Although op- mization has been practiced in some form or other from the early prehistoric era, this area has seen progressive growth during the last ?ve decades. M- ern society lives not only in an environment of intense competition but is also constrained to plan its growth in a sustainable manner with due concern for conservation of resources. Thus, it has become imperative to plan, design, operate, and manage resources and assets in an optimal manner. Early - proaches have been to optimize individual activities in a standalone manner, however,thecurrenttrendistowardsanintegratedapproach:integratings- thesis and design, design and control, production planning, scheduling, and control. The functioning of a system may be governed by multiple perf- mance objectives. Optimization of such systems will call for special strategies for handling the multiple objectives to provide solutions closer to the systems requirement. Uncertainty and variability are two issues which render op- mal decision making di?cult. Optimization under uncertainty would become increasingly important if one is to get the best out of a system plagued by uncertain components. These issues have thrown up a large number of ch- lenging optimization problems which need to be resolved with a set of existing and newly evolving optimization tools. Optimization theory had evolved initially to provide generic solutions to optimizationproblemsinlinear,nonlinear,unconstrained,andconstrained- mains. Theseoptimization problems wereoften called mathematical progr- mingproblemswithtwodistinctiveclassi?cations,namelylinearandnonlinear programming problems.

Tools are needed to define, measure and assess pollution in processes and products, to direct and measure improvements in designing cleaner processes and products, and to design benign processes and products. Life cycle assessment, process simulation and integration, material substitution and environmental impact assessment are some of these tools. Advances have been made recently in incorporating environmental criteria in process and product design, some renditions of which are commercially available. This is a compilation of methods, tools and models that can be used to design products and manufacturing processes that prevent pollution from occurring in the first place, rather than treating the wastes after they are formed. There is also a critique of social barriers to pollution prevention.

Difficult ethical and political issues confront the application of scientific and technological solutions for preventing pollution from occurring. Often the questions embodying proposed solutions to known problems are ambiguous; and no singular solution can exist for all locations. Yet the common organizing principle for all solutions to environmental problems must be scientific, because only sound science can provide a rational understanding that decision-makers can use universally. The NATO Advanced Research Workshop (ARW) on Tools and Methods for Pollution Prevention (October 12-14, 1998, Prague, Czech Republic) was designed to assemble experts from NATO and former socialist countries of Eastern Europe to discuss science-based tools and methods, either available or in development, that can be used to analyze environmental impacts of manufacturing processes and manufactured products. The presentations at the workshop were written up specifically for the purpose of this book, albeit after peer reviews and thorough revisions. Most of the chapters focus on specific method development for designing environmentally benign processes or products. Several chapters take a broader view and focus on such designs from an implementation perspective, and dwell on ethical conflicts, resistance to change because of inertia or inadequate infrastructure, and on the lack of resources. We placed these chapters at the beginning of the book. The volume starts with the overview by Sikdar et aI. , who describe the scope and difficulties of pollution prevention in the manufacturing sectors.

Batch processes are widely used in pharmaceutical, food, and specialty chemicals where high value, low volume products are manufactured. Designing these processes and unit operations can be an onerous task that requires computational efforts. With examples, case studies, and more than 100 homework problems, this book presents an overview of computer-aided design packages and describes the unit operations in batch and bioprocessing. Providing students with a thorough grounding in the numerical methods necessary to solve design problems, it discusses the basics of design, modeling, and optimization. Figure slides are available upon qualifying course adoption.

Most available books in chemical engineering mainly pertain to continuous processes, with batch distillation relegated to a small section. Filling this void in the chemical engineering literature, Batch Distillation: Simulation, Optimal Design, and Control, Second Edition helps readers gain a solid, hands-on background in batch processing. The second edition of this bestseller explores numerous new developments in batch distillation that have emerged since the publication of the first edition.New to the Second Edition Special sections on complex column configurations and azeotropic, extractive, and reactive distillationA chapter on various kinds of uncertainties in batch distillation A chapter covering software packages for batch distillation simulation, design, optimization, and controlSeparate chapters on complex columns and complex systems Up-to-date references and coverage of recent research articlesThis edition continues to explain how to effectively design, synthesize, and make operations decisions related to batch processes. Through careful treatments of uncertainty analysis, optimization, and optimal control methods, the author gives readers the necessary tools for making the best decisions in practice. While primarily designed for a graduate course in batch distillation, the text can also be used in undergraduate chemical engineering courses. In addition, researchers and academics faced with batch distillation research problems and practicing chemical engineers tackling problems in actual day-to-day operations will find the book to be a useful reference source.

Although batch processing has existed for a long time, designing these processes and unit operations has been considered an onerous task that required computational efforts. Design of these processes is made more complex because of the time dependent nature of the process and the allowable flexibility. More often than not, every unit encounters optimal control problems. Therefore, traditional design books have not covered batch processing in detail. Filling this void, Batch Processing: Modeling and Design describes various unit operations in batch and bio-processing as well as design methods for these units.Topics include: Batch distillation operating modes and configurations Batch absorption operations based on the solubility differenceBatch adsorption based on differential affinity of various soluble molecules to solid absorbentsBatch chromatography for measuring a wide variety of thermodynamic, kinetic, and physico-chemical propertiesBatch crystallization where a phase is used to find the supersaturation at which point material crystallizesBatch drying that stresses the phase diagram of water to describe this operationBatch filtration using a porous medium or screen to separate solids from liquidsBatch centrifugation where centrifugal force is used for separationBatch processes are widely used in pharmaceutical, food, and specialty chemicals where high value, low volume products are manufactured. Recent developments in bio-based manufacturing also favor batch processes because feed variations can be easily handled in batch processes. Further, the emerging area of nanomaterials manufacturing currently uses batch processes as they are low volume, high energy intensive processes. With examples, case studies, and more than 100 homework problems, this book describes the unit operations in batch and bioprocessing and gives students a thorough grounding in the numerical methods necessary to solve these design problems.

Although batch processing has existed for a long time, designing these processes and unit operations has been considered an onerous task that required computational efforts. Design of these processes is made more complex because of the time dependent nature of the process and the allowable flexibility. More often than not, every unit encounters optimal control problems. Therefore, traditional design books have not covered batch processing in detail. Filling this void, Batch Processing: Modeling and Design describes various unit operations in batch and bio-processing as well as design methods for these units.Topics include: Batch distillation operating modes and configurations Batch absorption operations based on the solubility differenceBatch adsorption based on differential affinity of various soluble molecules to solid absorbentsBatch chromatography for measuring a wide variety of thermodynamic, kinetic, and physico-chemical propertiesBatch crystallization where a phase is used to find the supersaturation at which point material crystallizesBatch drying that stresses the phase diagram of water to describe this operationBatch filtration using a porous medium or screen to separate solids from liquidsBatch centrifugation where centrifugal force is used for separationBatch processes are widely used in pharmaceutical, food, and specialty chemicals where high value, low volume products are manufactured. Recent developments in bio-based manufacturing also favor batch processes because feed variations can be easily handled in batch processes. Further, the emerging area of nanomaterials manufacturing currently uses batch processes as they are low volume, high energy intensive processes. With examples, case studies, and more than 100 homework problems, this book describes the unit operations in batch and bioprocessing and gives students a thorough grounding in the numerical methods necessary to solve these design problems.

Most available books in chemical engineering mainly pertain to continuous processes, with batch distillation relegated to a small section. Filling this void in the chemical engineering literature, Batch Distillation: Simulation, Optimal Design, and Control, Second Edition helps readers gain a solid, hands-on background in batch processing. The second edition of this bestseller explores numerous new developments in batch distillation that have emerged since the publication of the first edition.New to the Second Edition Special sections on complex column configurations and azeotropic, extractive, and reactive distillationA chapter on various kinds of uncertainties in batch distillation A chapter covering software packages for batch distillation simulation, design, optimization, and controlSeparate chapters on complex columns and complex systems Up-to-date references and coverage of recent research articlesThis edition continues to explain how to effectively design, synthesize, and make operations decisions related to batch processes. Through careful treatments of uncertainty analysis, optimization, and optimal control methods, the author gives readers the necessary tools for making the best decisions in practice. While primarily designed for a graduate course in batch distillation, the text can also be used in undergraduate chemical engineering courses. In addition, researchers and academics faced with batch distillation research problems and practicing chemical engineers tackling problems in actual day-to-day operations will find the book to be a useful reference source.

Most available books in chemical engineering mainly pertain to continuous processes, with batch distillation relegated to a small section. Filling this void in the chemical engineering literature, Batch Distillation: Simulation, Optimal Design, and Control, Second Edition helps readers gain a solid, hands-on background in batch processing. The seco

Batch processes are widely used in pharmaceutical, food, and specialty chemicals where high value, low volume products are manufactured. Designing these processes and unit operations can be an onerous task that requires computational efforts. With examples, case studies, and more than 100 homework problems, this book presents an overview of computer-aided design packages and describes the unit operations in batch and bioprocessing. Providing students with a thorough grounding in the numerical methods necessary to solve design problems, it discusses the basics of design, modeling, and optimization. Figure slides are available upon qualifying course adoption.

Most available books in chemical engineering mainly pertain to continuous processes, with batch distillation relegated to a small section. Filling this void in the chemical engineering literature, Batch Distillation: Simulation, Optimal Design, and Control, Second Edition helps readers gain a solid, hands-on background in batch processing. The seco

Provides well-written self-contained chapters, including problem sets and exercises, making it ideal for the classroom setting;

Introduces applied optimization to the hazardous waste blending problem;

Explores linear programming, nonlinear programming, discrete optimization, global optimization, optimization under uncertainty, multi-objective optimization, optimal control and stochastic optimal control;

Includes an extensive bibliography at the end of each chapter and an index;

GAMS files of case studies for Chapters 2, 3, 4, 5, and 7 are linked to http://www.springer.com/math/book/978-0-387-76634-8;

Solutions manual available upon adoptions.

Introduction to Applied Optimization is intended for advanced undergraduate and graduate students and will benefit scientists from diverse areas, including engineers.

This book presents the details of the BONUS algorithm and its real world applications in areas like sensor placement in large scale drinking water networks, sensor placement in advanced power systems, water management in power systems, and capacity expansion of energy systems. A generalized method for stochastic nonlinear programming based on a sampling based approach for uncertainty analysis and statistical reweighting to obtain probability information is demonstrated in this book. Stochastic optimization problems are difficult to solve since they involve dealing with optimization and uncertainty loops. There are two fundamental approaches used to solve such problems. The first being the decomposition techniques and the second method identifies problem specific structures and transforms the problem into a deterministic nonlinear programming problem. These techniques have significant limitations on either the objective function type or the underlying distributions for the uncertain variables. Moreover, these methods assume that there are a small number of scenarios to be evaluated for calculation of the probabilistic objective function and constraints. This book begins to tackle these issues by describing a generalized method for stochastic nonlinear programming problems. This title is best suited for practitioners, researchers and students in engineering, operations research, and management science who desire a complete understanding of the BONUS algorithm and its applications to the real world.

This book presents the details of the BONUS algorithm and its real world applications in areas like sensor placement in large scale drinking water networks, sensor placement in advanced power systems, water management in power systems, and capacity expansion of energy systems. A generalized method for stochastic nonlinear programming based on a sampling based approach for uncertainty analysis and statistical reweighting to obtain probability information is demonstrated in this book. Stochastic optimization problems are difficult to solve since they involve dealing with optimization and uncertainty loops. There are two fundamental approaches used to solve such problems. The first being the decomposition techniques and the second method identifies problem specific structures and transforms the problem into a deterministic nonlinear programming problem. These techniques have significant limitations on either the objective function type or the underlying distributions for the uncertain variables. Moreover, these methods assume that there are a small number of scenarios to be evaluated for calculation of the probabilistic objective function and constraints. This book begins to tackle these issues by describing a generalized method for stochastic nonlinear programming problems. This title is best suited for practitioners, researchers and students in engineering, operations research, and management science who desire a complete understanding of the BONUS algorithm and its applications to the real world.