Hybrid statistical-machine learning ammonia forecasting in continuous activated sludge treatment for improved process control

Abstract In this work, a statistical stability metric and novel hybrid statistical-machine learning ammonia forecasting model are developed to improve the accuracy and precision of municipal wastewater treatment. Aeration for biological nutrient removal is typically the largest energy expense for municipal wastewater treatment plants (WWTP). Ammonia-based aeration control (ABAC) is one approach designed to minimize excessive aeration by adjusting air blower output from online ammonia measurements rather than from a dissolved oxygen (DO) sensor, which is the conventional aeration control approach. We propose a quantitative stability metric, Total Sample Variance, to compare system-wide variability of competing aeration control strategies. Using this metric, the performance of traditional DO and ABAC control strategies with varying setpoints and control parameters were compared in a medium-sized WWTP, and the most stable strategy was identified and implemented at the facility. To further improve ABAC performance, ammonia forecasting models were constructed using both statistical and machine learning to improve the accuracy of the aeration control system. Diurnal, diurnal-linear, artificial neural network (ANN), and hybrid diurnal-linear-ANN forecasting models were trained on real-time plant-wide process data. The diurnal-linear and diurnal-linear-ANN forecasts were found to most accurately forecast ammonia; improving upon the existing ammonia measurement by up to 32% and 46%, respectively, whereas the ANN model forecast was only able to improve by up to 8%. This work demonstrates the ease and flexibility of integrating statistics and machine learning methods for developing new treatment models in conventional WWTP for features in full-scale conventional activated sludge systems.