Dynamic Simulation and Control of Reactive Systems Involving Metabolic Pathways

Aghaee Foroushani, Mohammad | 2020

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 52973 (06)
  4. University: Sharif University of Technology
  5. Department: Chemical and Petroleum Engineering
  6. Advisor(s): Bozorgmehry Bozarjomehry, Ramin; Setoodeh, Payam
  7. Abstract:
  8. In this project, modeling, simulation, and control of the Saccharomyces Cerevisiae were studied. In the first section, simulation and control of a structural model of saccharomyces Cerevisiae were performed. Fuzzy Logic Controller (FLC) as a model-independent controller and Global Linearization Controller (GLC) as a model-based controller were designed. Additionally, two types of Kalman filters were designed to predict all states of the structural model: 1-Extended Kalman Filter (EKF), 2- Unscented Kalman Filter (UKF). As a concise explanation, the control action of the GLC is a function of all states of the model, and since that measuring all metabolites is not practical, the EKF and UKF were designed. Both controllers were able to control the system, track the set-point, and minimize the negative effects of the feed disturbance. Besides these results, the FLC was able to minimize the negative effects of parameter and unstructured uncertainties. Both controllers were able to track the set-point, reject the disturbance of the feed concentration, minimize the negative effects of uncertainties. In the simulations of parameter uncertainties, the whole closed-loop system was not able to track the set-point, and an offset occurred when the GLC-EKF or GLC-UKF was used as the controller. Therefore, a much better dynamic response was obtained when the FLC was used. Furthermore, FLC was much easier to implement.Finally, we created a reduced metabolic network for Saccharomyces Cerevisiae based on the shortest paths in the primary metabolic network, S288C. After that, we developed an algorithm that identifies the most important intracellular metabolites on the dynamic phenotype of the bacteria. These key metabolites determined the dependency of the measured metabolites (the extracellular metabolites), and based on that, two types of artificial intelligence modeling were performed: 1- Cascade Fuzzy Logic Modeling, 2-Cascade Neural Networks Modeling. Both of them were able to have a good prediction of the dynamic behavior of the experimental data
  9. Keywords:
  10. Saccharomyces Cerevisiae ; Metabolic Networks ; Modeling ; Dynamic Simulation ; Control ; Artificial Intelligence ; System Biology

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