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Skip to Search Results- 7Process Control
- 4Machine Learning
- 3Reinforcement Learning
- 2Primary Separation Vessel
- 1Data-driven
- 1Distributed Parameter Systems
- 2Shafi, Hareem
- 1Abbasi Brujeni, Lena
- 1Huang, Zhiyinan
- 1Huang,Biao
- 1Ibrahim, Fadi
- 1Ozorio Cassol,Guilherme
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A Hierarchical Constrained Reinforcement Learning for Optimization of Bitumen Recovery Rate in a Primary Separation Vessel
Download2020-01-01
Shafi, Hareem, Velswamy, Kirubakaran, Ibrahim, Fadi, Huang,Biao
This work proposes a two-level hierarchical constrained control structure for reinforcement learning (RL) with application in a Primary Separation Vessel (PSV). The lower level is concerned with servo tracking and regulation of the interface level against variances in ore quality by manipulating...
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Data-driven development of advanced controllers for complex reaction systems with minimal prior information
DownloadFall 2024
In the realm of complex reactive systems where full knowledge of ongoing reactions is unattainable, the adoption of data-driven inferential models based on mixture spectra has gained significant traction. Spectra-based online monitoring has shown promise due to the rapidity, non-invasiveness,...
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Spring 2010
In this thesis, a Reinforcement Learning (RL) method called Sarsa is used to dynamically tune a PI-controller for a Continuous Stirred Tank Heater (CSTH) experimental setup. The proposed approach uses an approximate model to train the RL agent in the simulation environment before implementation...
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Spring 2024
Advanced process control has been considered as a promising tool for addressing various control objectives for complex industrial applications, including ensuring safe operation, reducing operational cost, improving process efficiency, achieving more environmentally friendly practices, etc....
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From Continuous Modelling to Discrete Constrained Optimal Control of Distributed Parameter Systems
DownloadSpring 2022
Distributed parameter systems (DPS) are systems that have their evolution through time and in space. These systems are present in every type of industrial process, from chemical to electrical applications. Thus, proper modeling and control of DPS are indispensable for the optimization and control...
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Monitoring of Industrial Processes via Non-stationary Probabilistic Slow Feature Analysis Machine Learning Algorithm
DownloadSpring 2020
This research develops a first of its kind machine learning (ML) algorithm, called probabilistic slow feature analysis (PSFA), that monitors and detects faults for non-stationary industrial processes. The novelty of this ML algorithm is that it can monitor and detect faults for non-stationary...
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Spring 2020
Reinforcement learning (RL) has received wide attention in various fields lately. Model-free RL brings data-driven solutions that learn the control strategy directly from interaction with process data without the need for a process model. This is especially beneficial in the case of nonlinear...