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Simulation and Control of Anaerobic Reactors

Ghanavati, Mohammad Amin | 2020

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 52974 (06)
  4. University: Sharif University of Technology
  5. Department: Chemical and Petroleum Engineering
  6. Advisor(s): Shahrokhi, Mohammad; Vafa, Ehsan
  7. Abstract:
  8. Anaerobic digestion of waste and wastewater has attracted researchers’ attention due to its important effect on reducing greenhouse gas emissions and reducing the level of environmental pollution and production of methane as a renewable energy source. This process is highly sensitive and can become unstable due to the presence of complex biochemical reactions and containing various types of chemical in it. In order to understand and improve the performance of this process, modeling and design of an appropriate control strategy is needed. ADM1 is the most accurate model for this process which is used in this study to describe and simulate the anaerobic reactor. Constrained and unconstrained adaptive model predictive control strategies have been used to regulate the methane flow rate and keep the concentration of volatile fatty acids below the predetermined level. To avoid measuring system states, an input-output ARMA model has been used to estimate system outputs in the prediction horizon. As the process is highly nonlinear, ARMA model parameters are updated at each sample time to capture system nonlinearities and increasing controller robustness against uncertainties. The controller performance has been tested in different operational scenarios compared to the PI controller. According to the obtained results, the unconstrained model predictive controller has the ability to regulate produced methane flow rate and reject the external loads. Additionally, the constrained model predictive controller can maintain volatile fatty acid concentration below the predetermined level against external disturbances
  9. Keywords:
  10. Anaerobic Digestion ; Online Identification ; Anaerobic Reactors ; Greenhouse Gases ; Constrained Predictive Control

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