Abstract: Model-based control and optimization is the predominant paradigm in process systems
engineering. The performance of model-based methods, however, rely heavily
on the accuracy of the process model, which declines over the operation cycle due to
various causes, such as catalyst deactivation, equipment aging, feedstock variability,
and others. This work aims to tackle this challenge by considering two alternative
approaches. The first approach replaces existing control and optimization methods
with model-free reinforcement learning (RL). We apply a state-of-the-art reinforcement
learning algorithm to a network of reactions, evaluate the performance of the
RL controller in terms of setpoint tracking, disturbance rejection, and robustness to
parameter uncertainties, and optimize the reward function to achieve the desired control
and optimization performance. The second approach presents a novel framework
for integrating Economic Model Predictive Control (EMPC) and RL for online model
parameters estimation. In this framework, EMPC optimally operates the closed-loop
system while maintaining closed-loop stability and recursive feasibility. At the same
time, the RL agent continuously compares the measured state of the process with the
model’s predictions, and modifies the model parameters accordingly to optimize the
process. The performance of the proposed framework is illustrated on a network of
reactions with challenging dynamics and practical significance.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/664024 |
| Date | 07 1900 |
| Creators | Alhazmi, Khalid |
| Contributors | Sarathy, Mani, Physical Sciences and Engineering (PSE) Division, Shamma, Jeff S., Pinnau, Ingo |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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