Investigation of process parameters and development of a mathematical model for the purposes of control design and implementation for a wastewater treatment process
Du Plessis, Sydney Charles
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The problem for effective and optimal control of wastewater treatment plants is very important recently because of the increased requirements to the quality of the effluent. The activated sludge process is a type of wastewater process characterized with complex dynamics and because of this proper control design and implementation strategies are necessary and important for its operation. Since the early seventies, when a major leap forward was made by the widespread introduction of dissolved oxygen control, little progress has been made. The most critical phase in the solution of any control problem is the modelling stage. The primary building block of any modern control exercise is to construct and identify a model for the system to be controlled. The existing full Activated Sludge Model 1 (ASM1) and especially University of Cape Town (UCT) models of the biological processes in the activated sludge process, called in the thesis biological models, are highly complex because they are characterised with a lot of variables that are difficult to be measured on-line, complex dependencies and nonlinear interconnections between the biological variables, many kinetic parameters that are difficult to be determined, different time scales for the process dynamics. The project considers reduction of the impact of the complexity of the process model over the methods for control design and proposes a solution to the above difficulties by development of a reduced model with small number of variables, but still with the same characteristics as the original full model for the purposes of real time control design. Then the research problem can be stated in the following way: To develop mathematical methods, algorithms and programmes for transformation of the complex biological and mass balance models of the activated sludge process into simpler reduced models of the process convenient to be used for optimal parameter estimation, control design and control implementation in real- time.