Model Predictive Control and State Estimation for Membrane-based Water Systems
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AbstractLack of clean fresh water is one of the most pervasive problems afflicting people throughout the world. Efficient desalination of sea and brackish water and safe reuse of wastewater become an insistent need. However, such techniques are energy intensive, and thus, a good control design is needed to increase the process efficiency and maintain water production costs at an acceptable level. This thesis proposes solutions to the above challenges and in particular will be focused on two membranebased water systems: Membrane Distillation (MD) and Membrane Bioreactor (MBR) for wastewater treatment plant (WWPT). The first part of this thesis, Direct Contact Membrane Distillation (DCMD) will study as an example an MD process. MD is an emerging sustainable desalination technique which can be powered by renewable energy. Its main drawback is the low water production rate. However, it can be improved by utilizing advanced control strategies. DCMD is modeled by a set of Differential Algebraic Equations (DAEs). In order to improve its water production, an optimization-based control scheme termed Model Predictive Control (MPC) provides a natural framework to optimally operate DCMD processes due to its unique control advantages. Among these advantages are the flexibility provided in formulating the objective function, the capability to directly handle process constraints, and the ability to work with various classes of nonlinear systems. Motivated by the above considerations, two MPC schemes that can maximize the water production rate of DCMD systems have been developed. The first MPC scheme is formulated to track an optimal set-point while taking input and stability constraints into account. The second MPC scheme, Economic MPC (EMPC), is formulated to maximize the distilled water flux while meeting input, stability and other process operational constraints. The total water production under both control designs is compared to illustrate the effectiveness of the two proposed control paradigms. Simulation results show that the DCMD process produces more distilled water when it is operated by EMPC than when it is operated by MPC. The above control techniques assume the full access to the system states. However, this is not the case for the DCMD plant. To effectively control the closed-loop system, an observer design that can estimate the values of the unmeasurable states is required. Motivated by that, a nonlinear observer design for DCMD is proposed. In addition, the effect of the estimation gain matrix on the differentiation index of the DAE system is investigated. Numerical simulations are presented to illustrate the effectiveness of the proposed observer design. The observer-based MPC and EMPC are also studied in this work. Mathematical modeling of a wastewater treatment system is critical because it enhances the process understanding and can be used for process design and process optimization. Motivated by the above considerations, modeling and optimal control strategies have been developed and applied to the MBR-based wastewater treatment process. The model is an extension of the well-known Benchmark simulation models for wastewater treatment. In addition, model predictive control has been applied to maintain the dissolved oxygen concentration level at the desired value. In addition, a conventional PID controller has also been developed. The simulation results show that the both of controllers can be used for dissolved oxygen concentration control. However, MPC has better performance compared to PID scenario.