Data-Based Modelling of Chemical Oxygen Demand for Industrial Wastewater Treatment

Abstract

The aim of wastewater treatment plants (WWTPs) is to clean wastewater before it is discharged into the environment. Real-time monitoring and control will become more essential as the regulations for effluent discharges are likely to become stricter in the future. Model-based soft sensors provide a promising solution for estimating important process variables such as chemical oxygen demand (COD) and help in predicting the performance of WWTPs. This paper explores the possibility of using interpretable model structures for monitoring the influent and predicting the effluent of paper mill WWTPs by systematically finding the best model parameters using an exhaustive algorithm. Experimentation was conducted with regression models such as multiple linear regression (MLR) and partial least squares regression (PLSR), as well as LASSO regression with a nonlinear scaling function to account for nonlinearities. Some autoregressive time series models were also built. The results showed decent modelling accuracy when tested with test data acquired from a wastewater treatment process. The most notable test results included the autoregressive model with exogenous inputs for influent COD (correlation 0.89, mean absolute percentage error 8.1%) and a PLSR model for effluent COD prediction (correlation 0.77, mean absolute percentage error 7.6%) with 20 h prediction horizon. The results show that these models are accurate enough for real-time monitoring and prediction in an industrial WWTP.