Synopsis
Neural networks have received a great deal of attention among scientists and engineers. In chemical engineering, neural computing has moved from pioneering projects toward mainstream industrial applications. This book introduces the fundamental principles of neural computing, and is the first to focus on its practical applications in bioprocessing and chemical engineering. Examples, problems, and 10 detailed case studies demonstrate how to develop, train, and apply neural networks. A disk containing input data files for all illustrative examples, case studies, and practice problems provides the opportunity for hands-on experience. An important goal of the book is to help the student or practitioner learn and implement neural networks quickly and inexpensively using commercially available, PC-based software tools. Detailed network specifications and training procedures are included for all neural network examples discussed in the book.
Each chapter contains an introduction, chapter summary, references to further reading, practice problems, and a section on nomenclatureIncludes a PC-compatible disk containing input data files for examples, case studies, and practice problemsPresents 10 detailed case studiesContains an extensive glossary, explaining terminology used in neural network applications in science and engineeringProvides examples, problems, and ten detailed case studies of neural computing applications, including:Process fault-diagnosis of a chemical reactorLeonard–Kramer fault-classification problemProcess fault-diagnosis for an unsteady-state continuous stirred-tank reactor systemClassification of protein secondary-structure categoriesQuantitative prediction and regression analysis of complex chemical kineticsSoftware-based sensors for quantitative predictions of product compositions from flourescent spectra in bioprocessingQuality control and optimization of an autoclave curing process for manufacturing composite materialsPredictive modeling of an experimental batch fermentation processSupervisory control of the Tennessee Eastman plantwide control problemPredictive modeling and optimal design of extractive bioseparation in aqueous two-phase systems
From the Back Cover
Amid the substance and hype of neural networks, applications in bioprocessing and chemical engineering have progressed from exploratory studies toward the industrial mainstream. This book presents the fundamentals of neural computing with a unique focus on the practical applications in bioprocessing and chemical engineering. Intended for both students and practitioners, this book will also appeal to computer scientists with an interest in biotechnological and engineering applications.
Neural Networks in Bioprocessing and Chemical Engineering guides the reader in developing and implementing neural networks quickly and inexpensively using commercially available PC-based software tools.
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