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URN etd-0407104-163203 Statistics This thesis had been viewed 2564 times. Download 1210 times. Author Ying-Ta Sung Author's Email Address firstname.lastname@example.org Department Chemical Engineering Year 2003 Semester 1 Degree Master Type of Document Master's Thesis Language English Page Count 74 Title Optimization of a Batch Polymerization Reactor Via Hybrid Neural-Network Rate Function Model Keyword Optimization Hybrid Neural-Network Rate Function Model Batch Polymerization Reactor Batch Polymerization Reactor Hybrid Neural-Network Rate Function Model Optimization Abstract A simulated verification and validation of the proposed hybrid neural-network rate-function (HNNRF) approach to modeling a batch polymerization reactor system is provided. In a chemical process, some measurements may not be obtainable easily, and the designed NNRF model does not embed these state variables in the built dynamic model. To overcome this problem, the approximated physical model is combined with the NNRF model to give the hybrid neural-network rate-function (HNNRF) model. In this study, a sequential pseudo-uniform design is used to locate desired experiments to provide the HNNRF model of a batch polymerization reactor with rich information. Transformation of the HNNRF dynamic model into a feed-forward artificial neural network (FANN) static model reduces the computation time in determining the optimal operation conditions base on the random search method. An optimal temperature trajectory and initial loading of the initiator for achieving the molecular weight distribution control can be obtained accordingly. Advisor Committee Jyh-Shyong Chang - advisor
none - co-chair
none - co-chair
Files Date of Defense 2003-07-31 Date of Submission 2004-04-07