The prevalence of batch and batch-like operations, in conjunction with the continued
resurgence of artificial intelligence techniques for clustering and classification applications, has increasingly motivated the exploration of the applicability of deep learning
for modeling and feedback control of batch and batch-like processes. To this end, the
present study seeks to evaluate the viability of artificial intelligence in general, and
neural networks in particular, toward process modeling and control via a case study.
Nonlinear autoregressive with exogeneous input (NARX) networks are evaluated in
comparison with subspace models within the framework of model-based control. A
batch polymethyl methacrylate (PMMA) polymerization process is chosen as a simulation test-bed. Subspace-based state-space models and NARX networks identified
for the process are first compared for their predictive power. The identified models
are then implemented in model predictive control (MPC) to compare the control performance for both modeling approaches. The comparative analysis reveals that the
state-space models performed better than NARX networks in predictive power and
control performance. Moreover, the NARX networks were found to be less versatile
than state-space models in adapting to new process operation. The results of the
study indicate that further research is needed before neural networks may become
readily applicable for the feedback control of batch processes. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/28831 |
| Date | January 2023 |
| Creators | Mustafa Rashid |
| Contributors | Dr. Prashant Mhaskar, Chemical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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