Forecasting influent flow rate and composition with occasional data for supervisory management system by time series model.

The information on the incoming load to wastewater treatment plants is not often available to apply modelling for evaluating the effect of control actions on a full-scale plant. In this paper, a time series model was developed to forecast flow rate, COD, NH4(+)-N and PO4(3-)-P in influent by using 250 days data of field plant operation data. The data for 150 days and 100 days were used for model development and model validation, respectively. The missing data were interpolated by the spline method and the time series model. Three different methods were proposed for model development: one model and one-step to seven-step ahead forecasting (Method 1); seven models and one-step-ahead forecasting (Method 2); and one model and one-step-ahead forecasting (Method 3). Method 3 featured only one-step-ahead forecasting that could avoid the accumulated error and give simple estimation of coefficients. Therefore, Method 3 was the reliable approach to developing the time series model for the purpose of this research.