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http://hdl.handle.net/11375/30123
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DC Field | Value | Language |
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dc.contributor.advisor | Mhaskar, Prashant | - |
dc.contributor.author | Chandrasekar, Aswin | - |
dc.date.accessioned | 2024-08-30T18:55:52Z | - |
dc.date.available | 2024-08-30T18:55:52Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | http://hdl.handle.net/11375/30123 | - |
dc.description.abstract | It is important to note that the techniques proposed in this thesis are applicable to any batch process with a similar input-output-quality variable structure. The thesis demonstrates the effectiveness of these approaches through experiments on example batch processes. The primary focus of this thesis is to facilitate the modelling of batch processes considering quality variables in the context of model-based quality control and provide direct quality control formulations as opposed to existing techniques which are primarily focused on trajectory control of measured variables. These quality variables, which are unique to batch processes, cannot be measured during a batch run and can only be assessed at the end of the batch. The ability to implement good quality control is dependent on the ability to capture the complex dynamic behavior of batch processes. The first challenge is that of handling process non-linearities and/or multiple phases while being cognizant of the fact that a relatively modest amount of informative data is available, making the recently developed deep-learning-based machine-learning techniques not directly implementable. Yet another challenge/opportunity that exists is in situations where traditional sensors such as thermocouples may not be possible to implement in practice, but feedback may be available through non-traditional sensors such as thermal images. The present thesis addresses the above-mentioned challenges and demonstrates the approaches on a pilot-scale experimental setup. In the first contribution, a data-driven model-based economic control formulation was initially developed and implemented on a batch Rotational Molding process to achieve product specifications through constraints on the predicted quality variables, while either minimizing the total input consumption or further maximizing the product quality. A Linear Time-Invariant (LTI) State Space (SS) model is used in conjunction with a Partial Least Squares (PLS) quality model to model the process and quality variables. The next contribution presents one way of handling process nonlinearity. An adaptive modelling strategy unique to batch processes was proposed to alleviate this issue and implemented on the Rotational molding process to continuously adapt the LTI SS model during a batch run. The next contribution leveraged the nonlinearity-capturing capabilities of modelling techniques like Neural Networks (NN) while handling the overfitting problem. The key idea in this approach was to use a subspace identification approach to first determine the state trajectory evolution followed by a Recurrent Neural Network (RNN) to model the non-linear process dynamics. This approach, along with the previous PLS quality model, also exhibited superior dynamic and quality predictions compared to a standard RNN case. In a departure from existing approaches, where the dynamic model and quality model are identified separately, an adaptation of the prediction error minimization framework was proposed where the dynamic model and the quality model are identified simultaneously, resulting in an improved unified model. Finally, the opportunity/challenge of non-traditional sensors was addressed. A framework was proposed where first, the high dimensional output, a thermal image in the specific example batch process, is reduced using a suitable dimensionality reduction technique to a set of latent features, which then would be used as the outputs of the dynamic model in any of the previously discussed approaches. The proposed modelling framework was implemented as a part of a Model Predictive Controller implementation using the thermal images as feedback to produce the desired product. | en_US |
dc.language.iso | en | en_US |
dc.title | Data-Driven Modelling and Control of Batch Processes | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Chemical Engineering | en_US |
dc.description.degreetype | Dissertation | en_US |
dc.description.degree | Doctor of Philosophy (PhD) | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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chandrasekar_aswin_202408_phd.pdf | Thesis | 2.98 MB | Adobe PDF | View/Open |
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