Thesis

Optimal controller design for critical operating regions of a continuous bioreactor

Thesis (M.S., Mechanical Engineering)--California State University, Sacramento, 2017.

Control systems are ubiquitous in the contemporary society. Principles of process control are observed in everyday functions such as cruise control in automobiles.. One challenge many of these systems encounter, is efficient controller design for complex and nonlinear processes. In this work, the modeling and design of an optimal controller to maximize production and minimize the waste in a bioreactor is proposed. Bioreactors involve the use of microorganisms or biomass as the process components. These components and subsequent byproducts can be utilized for the production of chemical solvents and pharmaceuticals. Process parameters such as pH, aeration rate, and growth rate can be simultaneously monitored for a bioreactor system. Optimal controllers allow for process control of a system with multiple variables. A state space model of a continuous bioreactor was developed for the growth and stationary operating regions, following a control study presented in literature. The mathematical model was then used for controller design in MATLAB and Simulink. A linear quadratic Regulator (LQR) control method was used for the control study of the multi-input multi-output system. The simulation results showed that a LQR controller with proportional nonzero set point architecture was efficient in controlling each operating region, while minimizing overshoot and unstable oscillations at steady state. This was observed in both the biomass concentration (BC) and substrate concentration (SC) state variables. The weighting elements for BC and SC were held constant, while the controller weighting elements for dilution rate and feed concentration were varied. This approach was effective for optimizing the system and controller. Utilizing the range of the controller limits as weighting elements, was effective for control design. The drawback for this approach is knowledge of the controller limits of the mechanical actuators or equipment. Additionally, selecting the manipulated inputs as dilution rate and feed concentration, allowed for a scale up of the process as they are independent of reactor size.

Control systems are ubiquitous in the contemporary society. Principles of process control are observed in everyday functions such as cruise control in automobiles.. One challenge many of these systems encounter, is efficient controller design for complex and nonlinear processes. In this work, the modeling and design of an optimal controller to maximize production and minimize the waste in a bioreactor is proposed. Bioreactors involve the use of microorganisms or biomass as the process components. These components and subsequent byproducts can be utilized for the production of chemical solvents and pharmaceuticals. Process parameters such as pH, aeration rate, and growth rate can be simultaneously monitored for a bioreactor system. Optimal controllers allow for process control of a system with multiple variables. A state space model of a continuous bioreactor was developed for the growth and stationary operating regions, following a control study presented in literature. The mathematical model was then used for controller design in MATLAB and Simulink. A linear quadratic Regulator (LQR) control method was used for the control study of the multi-input multi-output system. The simulation results showed that a LQR controller with proportional nonzero set point architecture was efficient in controlling each operating region, while minimizing overshoot and unstable oscillations at steady state. This was observed in both the biomass concentration (BC) and substrate concentration (SC) state variables. The weighting elements for BC and SC were held constant, while the controller weighting elements for dilution rate and feed concentration were varied. This approach was effective for optimizing the system and controller. Utilizing the range of the controller limits as weighting elements, was effective for control design. The drawback for this approach is knowledge of the controller limits of the mechanical actuators or equipment. Additionally, selecting the manipulated inputs as dilution rate and feed concentration, allowed for a scale up of the process as they are independent of reactor size.

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