Data-driven development of advanced controllers for complex reaction systems with minimal prior information
Creators: Pathan, Shahdab Mohamedimran
Publisher: name: University of Alberta Library
Year: 2024

<p>In the realm of complex reactive systems where full knowledge of ongoing reactions is<br>
unattainable, the adoption of data-driven inferential models based on mixture spectra has<br>
gained significant traction. Spectra-based online monitoring has shown promise due to the<br>
rapidity, non-invasiveness, non-destructiveness, and cost-effectiveness in spectral analysis.<br>
This study aims to develop advanced controllers for such complex reactive systems in the<br>
absence of ground truth information and subsequently compare their performances. To<br>
achieve this objective, a comprehensive suite of tools, including spectral deconvolution,<br>
Bayesian networks, neural ordinary differential equations (ODEs), long short-term memory (LSTM), model predictive control (MPC), and reinforcement learning are employed to<br>
transform spectra into actionable control strategies. The initial phase of the research focuses<br>
on establishing a model-based control framework through the utilization of spectral deconvolution and Bayesian networks, particularly in scenarios where ground truth knowledge is<br>
limited or the system dynamics are complex. Spectral deconvolution untangles pseudo component spectra and their corresponding concentration profiles from mixture spectra. These<br>
deconvoluted spectra serve as the Bayesian network’s inputs, effectively identifying potential<br>
reaction networks within the system. Concurrently, neural ODEs leverage the concentration<br>
profiles obtained from spectral deconvolution to extract rate law parameters and facilitate<br>
step-ahead concentration predictions. This holistic approach results in a comprehensive<br>
rate-law-based kinetic model that captures the reaction system’s dynamics. Two modeling<br>
approaches are employed and compared: a data-driven LSTM and a physics-driven greybox model utilizing Neural ODE. While the LSTM model operates as a black box, providing<br>
step-ahead concentration predictions, the Neural ODE model represents a grey-box approach<br>
incorporating first principles, also generating step-ahead predictions. The aim is to evaluate<br>
the performance of these approaches, contrasting the efficacy of the data-driven black box<br>
model (LSTM) with the physics-driven grey-box model (Neural ODE). In the latter phase<br>
of the study, reinforcement learning-based techniques are leveraged to design a model-free<br>
controller with a focus on optimizing the selectivity of desired products, like in MPC with<br>
neural ODE as model/environment.<br>
For future work, the focus will be on leveraging spectra corresponding to specific wavenumber ranges that are indicative of the functional groups associated with target products. This<br>
strategy diverges from previous approaches, such as the deconvolution pathway that emphasized modeling the kinetics. Instead, the plan is to adopt a model that utilizes mixture<br>
spectra as inputs. This model, in its control segment, will be designed to incentivize the<br>
agent or controller to prioritize selectivity towards certain products and/or wavenumber<br>
ranges. This methodology enables the system to refine its control strategy by relying solely<br>
on spectral data. This is particularly beneficial in situations where a comprehensive understanding of the system’s dynamics is not available, thus circumventing the need to develop<br>
detailed kinetic models. In conclusion, this work harnesses a range of advanced modelling<br>
and control methodologies to translate spectral data into actionable control strategies for<br>
complex reactive systems. The efficacy of the developed controllers is demonstrated through<br>
a simulation environment of a CSTR aimed at maximizing the selectivity of a desired species,<br>
thereby achieving the desired overall system performance.</p>