Data-driven development of advanced controllers for complex reaction systems with minimal prior information

• Author / Creator

  Pathan, Shahdab Mohamedimran

• 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, non-destructiveness, and cost-effectiveness in spectral analysis.
  This study aims to develop advanced controllers for such complex reactive systems in the
  absence of ground truth information and subsequently compare their performances. To
  achieve this objective, a comprehensive suite of tools, including spectral deconvolution,
  Bayesian networks, neural ordinary differential equations (ODEs), long short-term memory (LSTM), model predictive control (MPC), and reinforcement learning are employed to
  transform spectra into actionable control strategies. The initial phase of the research focuses
  on establishing a model-based control framework through the utilization of spectral deconvolution and Bayesian networks, particularly in scenarios where ground truth knowledge is
  limited or the system dynamics are complex. Spectral deconvolution untangles pseudo component spectra and their corresponding concentration profiles from mixture spectra. These
  deconvoluted spectra serve as the Bayesian network’s inputs, effectively identifying potential
  reaction networks within the system. Concurrently, neural ODEs leverage the concentration
  profiles obtained from spectral deconvolution to extract rate law parameters and facilitate
  step-ahead concentration predictions. This holistic approach results in a comprehensive
  rate-law-based kinetic model that captures the reaction system’s dynamics. Two modeling
  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
  step-ahead concentration predictions, the Neural ODE model represents a grey-box approach
  incorporating first principles, also generating step-ahead predictions. The aim is to evaluate
  the performance of these approaches, contrasting the efficacy of the data-driven black box
  model (LSTM) with the physics-driven grey-box model (Neural ODE). In the latter phase
  of the study, reinforcement learning-based techniques are leveraged to design a model-free
  controller with a focus on optimizing the selectivity of desired products, like in MPC with
  neural ODE as model/environment.
  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
  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
  spectra as inputs. This model, in its control segment, will be designed to incentivize the
  agent or controller to prioritize selectivity towards certain products and/or wavenumber
  ranges. This methodology enables the system to refine its control strategy by relying solely
  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
  detailed kinetic models. In conclusion, this work harnesses a range of advanced modelling
  and control methodologies to translate spectral data into actionable control strategies for
  complex reactive systems. The efficacy of the developed controllers is demonstrated through
  a simulation environment of a CSTR aimed at maximizing the selectivity of a desired species,
  thereby achieving the desired overall system performance.

• Subjects / Keywords

  • Machine Learning
  • Process Control

• Graduation date

  Fall 2024

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-w77y-3y33

• License

  This thesis is made available by the University of Alberta Library with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.