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http://hdl.handle.net/11375/29734
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DC Field | Value | Language |
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dc.contributor.advisor | Prashant, Mhaskar | - |
dc.contributor.author | Wang, Xiaonian | - |
dc.date.accessioned | 2024-05-04T01:34:18Z | - |
dc.date.available | 2024-05-04T01:34:18Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | http://hdl.handle.net/11375/29734 | - |
dc.description.abstract | This thesis consists of four chapters including two main contributions on the application of machine learning and artificial intelligence on process modeling and controller design. Chapter 2 will talk about applying AI to controller design. This chapter proposes and implements a novel reinforcement learning (RL)--based controller design on chemical engineering examples. To address the issue of costly and unsafe training of model-free RL-based controllers, we propose an implementable RL-based controller design that leverages offline MPC calculations, that have already developed based on a step-response model. In this method, a RL agent is trained to imitate the MPC performance. Then, the trained agent is utilized in a model-free RL framework to interact with the actual process so as to continuously learn and optimize its performance under a safe operating range of processes. This contribution is marked as the first implementable RL-based controller for practical industrial application. Chapter 3 will focus on AI applications in process modeling. As nonlinear dynamics are widely encountered and challenging to simulate, nonlinear MPC (NMPC) is recognized as a promising tool to tackle this challenge. However, the lack of a reliable nonlinear model remains a roadblock for this technique. To address this issue, we develop a novel data-driven modeling method that utilizes the nonlinear autoencoder, to result in a modeling technique where the nonlinearity in the model stems from the analysis of the measured variables. Moreover, a quadratic program (QP) based MPC is developed based on this model, by utilizing an autoencoder as a transformer between the controller and process. This work contributes as an extension of the classic Koopman operator modeling method and a remarkable linear MPC design that can outperform other NMPCs such as neural network-based MPC. | en_US |
dc.language.iso | en | en_US |
dc.subject | Process control | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Reinforcement learning | en_US |
dc.subject | Koopman operator | en_US |
dc.subject | modeling | en_US |
dc.subject | optimization | en_US |
dc.title | Machine Learning and Artificial Intelligence Application in Process Control | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Chemical Engineering | en_US |
dc.description.degreetype | Thesis | en_US |
dc.description.degree | Master of Applied Science (MASc) | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Wang_Xiaonian_202404_MASc.pdf | 4.93 MB | Adobe PDF | View/Open |
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