Design of a Soft Sensor for Monitoring Phosphorous Uptake in an EBPR Process

Abstract

Phosphorus (P) is a key nutrient targeted for removal by wastewater treatment, increasingly being achieved using biological processes such as enhanced biological phosphate removal (EBPR). However, commercial instrumentation for automated measurement of P is costly and provides only limited temporal resolution, constraining implementation of real-time controls in EBPR processes. This study designs a soft sensor for real-time controls using a suite of relatively low-cost sensors (ion-selective electrodes) to monitor P removal by measuring (1) secondary cations tightly coupled to bioP metabolism and (2) process bulk chemistry. Data collected from a highly instrumented lab scale reactor are used to evaluate which and how many sensors are required to achieve this goal. Machine learning (ML) approaches (support vector machines, nonlinear logistic regression, random forest, and Bayesian classification) are evaluated for sensor data fusion and coupled with a decision metric to operationalize the algorithm for reactor controls. Two key results emerge: (1) use of the slope of sensor data (mV/min) rather than raw data (mV) as the predictor signals significantly improves accuracy and resilience of the soft sensor-based system and (2) the K+ ion-selective electrode, in combination with any of the four ML algorithms, is sufficient to detect completion of P removal (within study sampling granularity of 4 min) with 100% accuracy in real time. High accuracy is maintained even as process chemistry is varied to increase the interference experienced by the sensors, indicating that this soft sensor is viable for use in real wastewater applications.