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 nomenclature
Includes a PC-compatible disk containing input data files for examples, case studies, and practice problems
Presents 10 detailed case studies
Contains an extensive glossary, explaining terminology used in neural network applications in science and engineering
Provides examples, problems, and ten detailed case studies of neural computing applications, including:
Process fault-diagnosis of a chemical reactor
Leonard–Kramer fault-classification problem
Process fault-diagnosis for an unsteady-state continuous stirred-tank reactor system
Classification of protein secondary-structure categories
Quantitative prediction and regression analysis of complex chemical kinetics
Software-based sensors for quantitative predictions of product compositions from flourescent spectra in bioprocessing
Quality control and optimization of an autoclave curing process for manufacturing composite materials
Predictive modeling of an experimental batch fermentation process
Supervisory control of the Tennessee Eastman plantwide control problem
Predictive modeling and optimal design of extractive bioseparation in aqueous two-phase systems
Each chapter contains an introduction, chapter summary, references to further reading, practice problems, and a section on nomenclature
Includes a PC-compatible disk containing input data files for examples, case studies, and practice problems
Presents 10 detailed case studies
Contains an extensive glossary, explaining terminology used in neural network applications in science and engineering
Provides examples, problems, and ten detailed case studies of neural computing applications, including:
Process fault-diagnosis of a chemical reactor
Leonard–Kramer fault-classification problem
Process fault-diagnosis for an unsteady-state continuous stirred-tank reactor system
Classification of protein secondary-structure categories
Quantitative prediction and regression analysis of complex chemical kinetics
Software-based sensors for quantitative predictions of product compositions from flourescent spectra in bioprocessing
Quality control and optimization of an autoclave curing process for manufacturing composite materials
Predictive modeling of an experimental batch fermentation process
Supervisory control of the Tennessee Eastman plantwide control problem
Predictive modeling and optimal design of extractive bioseparation in aqueous two-phase systems
