Economic Model Predictive Control for optimal struvite recovery.

Resource recovery from municipal wastewater has been a prime focus for a decade. Although several recovery processes already exist in the market today, the high cost of material, inherent disturbance in the influent quality, lack of real time monitoring of critical parameters, and lack of a robust automation system may result in suboptimal performance. This work attempts to construct a model based predictive control for optimal operation of a struvite recovery unit in a full scale WRRF. A multi-parameter based predictive control has been developed by implementing an Economic Model Predictive Controller (EMPC) for optimal dosing of magnesium hydroxide in a struvite recovery unit. The EMPC used customized objective function for real-time optimization of performance and economical parameters of the crystallization unit. The effectiveness of the proposed EMPC controller is verified through tests conducted on the Benchmark Simulation Model No. 2 (BSM2d.). The results obtained from the simulator-based evaluation of EMPC demonstrate a significant improvement in resource recovery at reduced operational costs. The economic advantages of implementing an EMPC compared to proportional and constant magnesium dosage has also been enumerated.

Implementing an Extended Kalman Filter for estimating nutrient composition in a sequential batch MBBR pilot plant.

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.