Third submission of my master thesis, first one didn't finish for some reason, the second I think I forgot to include my name. / This thesis focuses on the creation of new parallel hybrid model designs for prediction and control in batch and fed-batch reactors within Model Predictive Control (MPC) frameworks. In the hybrid model, the first principle (FP) explains the dynamics and the residual Subspace Identification (SID) model explains the error between the FP and the process. Modifications to the structure of the hybrid model are motivated by limitations of MPC frameworks. MPCs need accurate models to explain the system dynamics to make informed control decisions, and mechanistic models can be difficult to implement due to challenges of solving the optimization problem in real time. Two tools are demonstrated to help solve these problems. The first tool, Residual First Principle 0 Hybrid (RFP0H) model, helps to deal with the intractability of a mechanistic model in a hybrid modelling framework. The input for the FP model is kept constant and the SID predicts the error between the first principle and the process. Allowing for the desired output to be subtracted by the predicted FP to create a desired error value. Thus, MPC control only needs to be solved using the linear SID model in a linear or quadratic framework. Making a potentially intractable problem, tractable in MPC. This is demonstrated using a simulated fed-batch crystallization process. The second tool, Scaling Factor First Principle 0 Hybrid (SFFP0H) model, modifies the hybrid model structure to multiple the sub-models’ outputs together. The SID data driven model predicts a factor to scale the FP output for the process prediction. The results demonstrate that the SFFP0H model has increased predictive ability and has smaller variability in control compared to the RFP0H model. Helping to solve the problem of needing accurate models within an MPC formulation. This is demonstrated by using a laboratory scale batch polymerization process. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/27987 |
| Date | January 2022 |
| Creators | McKay, Alexander |
| Contributors | Mhaskar, Prashant, Chemical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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