Neural Networks in Bioprocessing and Chemical Engineering

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

Contenu

Introduction to Neural Networks: Introduction. Properties of Neural Networks. Potential Applications of Neural Networks. Reported Commercial and Emerging Applications. Fundamental and Practical Aspects of Neural Computing: Introduction to Neural Computing. Fundamentals of Backpropagation Learning. Practical Aspects of Neural Computing. Standard Format for Presenting Training Data Files and Neural Network Specifications. Introduction to Special Neural Network Architectures. Appendices. Classification: Fault Diagnosis and Feature Categorization: Overview of Classification Neural Networks. Radial-Basis-Function Networks. Comparison of Classification Neural Networks. Classification Neural Networks for Fault Diagnosis. Classification Neural Networks for Feature Categorization. Prediction and Optimization: Case Study 1: Neural Networks and Nonlinear Regression Analysis. Case Study 2: Neural Networks as Soft Sensors for Bioprocessing. Illustrative Case Study: Neural Networks for Process Quality Control and Optimization. Process Forecasting, Modeling, and Control of Time-Dependent Systems: Data Compression and Filtering. Recurrent Networks for Process Forecasting. Illustrative Case Study: Development of aTime-Dependent Network for Predictive Modeling of a Batch Fermentation Process. Illustrative Case Study: Tennessee Eastman Plantwide Control Problem. Neural Networks for Process Control. Development of Expert Networks: A Hybrid System of Expert Systemsand Neural Networks: Introduction to Expert Networks. Illustrative Case Study: Bioseparation of Proteins in Aqueous Two-Phase Systems. Appendix. Glossary. Data Files. Subject Index.