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.

Continuous monitoring of chemical oxygen demand (COD) is essential to ensure efficient and sustainable wastewater treatment and regulatory compliance. However, traditional hardware measurements are laborious, infrequent and costly. In this research, a cost-effective real-time alternative is presented. ARMAX, ARX, Neural Network and PLSR model structures were identified and tested for finding data-based model for real-time estimation of effluent COD in a full-scale industrial WWTP. To aim for estimating effluent COD without physically measuring it, a novel chain of two estimators was created by connecting in series the identified influent and effluent COD models. A comprehensive and systematic model identification was carried out to find the model inputs, delays, parameters and training windows using an exhaustive search algorithm. The results showed that using solely linear model structure it is possible to identify sufficiently accurate (R: 0.67, MAPE: 7.33%) and practical (interpretable and implementable) data-based estimation model which has predictive ability even up to 20 h horizon. As the series-connected model structure reaches the required margin of error it has potential for real-world industrial usage alongside or even replacing the hardware online sensor. Estimation model enhances resiliency and provides real-time insights into effluent quality in varying operating conditions and during unexpected disturbances.

This paper discusses measurements, models and a splashing indicator for basic oxygen furnace (BOF). Also some ideas about the utilization of expert knowledge together with the proposal for model-based monitoring are given. Maintainability, modularity and transportability are briefly discussed. Research was done in co-operation with Ruukki's Raahe Steel Works in Finland.