Forecasting the municipal sewage sludge amount generated at wastewater treatment plants using some machine learning methods

Abstract

Sludge management account for high economic costs and energy consumption in wastewater treatment. Accurate forecasting of sewage sludge generation thus can be important for the planning, operation and optimization of processes at wastewater treatment plant (WWTP). In this study data from a municipal treatment plant with a capacity of 88 thousand cubic meters of sewage per day located in south part of Poland were used to find a good forecasting model for sludge amount prediction. Among models an autoregressive integrated moving average (ARIMA) is one of the popular linear models in time series forecasting. Since the ARIMA model cannot capture the non-linear structure of the data research activities in forecasting suggest using neural networks. Long-short term memory (LSTM) recurrent neural network proved its usability in time series forecasting. Looking at the curve representing data of sludge amount generated in the previous years at WWTP the linear and non-linear patterns could be distinguished. To address these issue a hybrid methodology that combines advantage of ARIMA and LSTM was proposed and used for forecasting purpose. Experimental results showed that the combined model can be an effective way to improve the forecasting accuracy of sludge amount generated at this WWTP.