Sequencing batch reactor (SBR) is an activated sludge process that has been used successfully in municipal and industrial wastewater treatment. SBR process is operated in a batch mode where different batch phases run successively in a single tank or several tanks operating in parallel. Many studies on real-time control strategies have been employed to check the effects of parameters on SBR operation. However, due to highly nonlinear nature and time variations, fluctuations in hydraulics and components and possible equipment unreliability, a single control strategy based on multiple indirect parameters may not be successful. Therefore, using model-based approaches represents an advantage when defining and evaluating the control strategies and consequently saving time and money. Intelligent control strategy (ICS) such as fuzzy logic, artificial neural network (ANN) and also Gaussian process (GP) model can be used as an advanced form of real-time control strategy to optimize the SBR process. Besides, mathematical models and model based optimization can be developed for an effective control of nutrient and other contaminants in combined biological processes like SBRs. This project will examine different modeling approaches to optimize different SBR systems. Effects of operational parameters, climate and other environmental changes will also be considered in the modeling.
Engineering - chemical / biological
University of British Columbia
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