A data-mining approach to predict influent quality

In wastewater treatment plants, predicting influent water quality is important for energy management. The influent water quality is measured by metrics such as carbonaceous biochemical oxygen demand (CBOD), potential of hydrogen, and total suspended solid. In this paper, a data-driven approach for time-ahead prediction of CBOD is presented. Due to limitations in the industrial data acquisition system, CBOD is not recorded at regular time intervals, which causes gaps in the time-series data. Numerous experiments have been performed to approximate the functional relationship between the input and output parameters and thereby fill in the missing CBOD data. Models incorporating seasonality effects are investigated. Four data-mining algorithms-multilayered perceptron, classification and regression tree, multivariate adaptive regression spline, and random forest-are employed to construct prediction models with the maximum prediction horizon of 5 days.