Neural Networks in Bioprocessing and Chemical Engineering

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.