Neural Networks in Bioprocessing and Chemical Engineering
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This dissertation is also unique in that it includes the following ten detailed case studies of neural network applications in bioprocessing and chemical engineering: â ¢ Process fault-diagnosis of a chemical reactor. â ¢ Leonard-Kramer fault-classification problem. â ¢ Process fault-diagnosis for an unsteady-state continuous stirred-tank reactor system. â ¢ Classification of protein secondary-structure categories. â ¢ Quantitative prediction and regression analysis of complex chemical kinetics. â ¢ Software-based sensors for quantitative predictions ofproduct compositions from fluorescent spectra in bioprocessing. â ¢ Quality control and optimization of an autoclave curing process for manufacturing composite materials. â ¢ Predictive modeling of an experimental batch fermentation process. â ¢ Supervisory control of the Tennessee Eastman plantwide control problem â ¢ Predictive modeling and optimal design of extractive bioseparation in aqueous two-phase systems This dissertation also includes a glossary, which explains the terminology used in neural network applications in science and engineering.
- Doctoral Dissertations