Reduced order model monitoring and control of a membrane bioreactor system via delayed measurements

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Activated sludge treatment is one of the most widely used processes for wastewater treatment (WWT). These systems are built with sufficient design margin to allow changes in loading and process conditions. This is necessary and prudent to overcome limitations in measurement, monitoring and controlling of WWT process parameters at the desired frequency. Online sensors for mixed liquor suspended solids, chemical oxygen demand (COD), nitrogen, phosphorus, and other parameters available today are limited in application due to high cost and low reliability. Hence, many of the parameters are measured off-line when needed. This paper provides a framework to estimate parameters on-line using limited and delayed measurements. The proposed approach is based on the design of a Bayesian filter such as an extended Kalman filter (EKF), which measures and controls membrane bioreactor system using limited and delayed measurements. The objective is to estimate the states and parameters with limited and delayed measurements. Simulations show the efficacy of the proposed approach.

Keywords: Bayesian filter, delayed measurements, extended Kalman filter, Gaussian noise, membrane bioreactor system, model based estimation, soft-sensor for wastewater treatment

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