Abstract
Online monitoring of water quality parameters can provide better control over various operations in wastewater treatment plants. However, a lack of physical online sensors, the high price of the available online water-quality analyzers, and the need for regular maintenance and calibration prevent frequent use of online monitoring. Soft-sensors are viable alternatives, with advantages in terms of price and flexibility in operation. As an example, this work presents the development, tuning, implementation, and validation of an Extended Kalman Filter (EKF) on a grey-box model to estimate the concentration of volatile fatty acids (VFA), soluble phosphates (PO4-P), ammonia nitrogen (NH4-N) and nitrate nitrogen (NO3-N) using simple and inexpensive sensors such as pH and dissolved oxygen (DO). The EKF is implemented in a sequential batch moving bed biofilm reactor (MBBR) pilot scale unit used for biological phosphorus removal from municipal wastewater. The grey-box model, used for soft sensing, was constructed by fitting the kinetic data from the pilot plant to a reduced order version of ASM2d model. The EKF is successfully validated against the standard laboratory measurements, which confirms its ability to estimate various states during the continuous operation of the pilot plant.
Highlights
There has been a rapid increase in the implementation of advanced control strategies in process industries, including water resource recovery facilities (WRRF)
The aim of this work is to develop a dynamic stateestimator to provide real-time estimations of PO4-P, volatile fatty acids (VFA), NH4-N, and NO3-N in a sequential batch reactor, by using data obtained from online sensors such as pH and dissolved oxygen (DO)
It generates a possibility of adapting model parameters such that the soft-sensor algorithm can be implemented in any sequential batch reactor (SBR) plant with similar operational sequence
Summary
There has been a rapid increase in the implementation of advanced control strategies in process industries, including water resource recovery facilities (WRRF). These control strategies are essential for the optimal operation of process plants (O’Brien et al ). Advanced control strategies such as model predictive control (MPC) would require continuous, real-time information of various wastewater compositions (Liukkonen et al ). A number of online instruments for measuring nutrient composition are available in the market today. Their use is often limited to large-scale urban treatment facilities (Häck & Wiese ).
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