Predicting Influent and Effluent Quality Parameters for a UASB-Based Wastewater Treatment Plant in Asia Covering Data Variations during COVID-19: A Machine Learning Approach

A region's population growth inevitably results in higher water consumption. This persistentrise in water use increases the region's wastewater production. Consequently, due to thisincrease in wastewater (influent), Wastewater Treatment Plants (WWTPs) are required to run effectivelyin order to handle the huge demand for treated/processed water (effluent). Knowingin advance the influent and effluent parameters increases the operational efficiency and enablescost-effective utilization of diverse resources at wastewater treatment plants. This paper is based on aprediction/forecasting of an influent quality parameter, namely total MLD, as well as effluent qualityparameters, namely MPN, BOD, DO, COD and pH for the real-time data collected pre-, during andpost-COVID-19 at the Bharwara WWTP in Lucknow, India. It is the largest UASB-based wastewatertreatment facility in Uttar Pradesh and the second largest in Asia. In this paper, we propose a novelmodel namely, wPred comprising extensions of SARIMA with seasonal order and ANN-based MLmodels to estimate the influent and effluent quality parameters, respectively, and compare it withthe existing machine learning models. The lowest sMAPE error for the influent parameters usingwPred is 2.59%. The findings of the paper show a strong correlation (R-value), up to 0.99, betweenthe effluent parameters actually measured and predicted. As a result, the model designed in thispaper has an acceptable level of accuracy and generalizability which efficiently predicts/forecaststhe performance of Bharwara WWTP.

Automated summary

Population growth leads to higher water consumption, resulting in increased wastewater production. Therefore, wastewater treatment plants need to function effectively to meet the high demand for treated water. Predicting influent and effluent parameters improves operational efficiency and allows cost-effective use of resources at these plants.