application of artificial neural network (ann) for the prediction of water treatment plant influent characteristics

Application of a reliable forecasting model for any water treatment plant (wtp) is essential in order to provide a tool for predicting influent water quality and to form a basis for controlling the operation of the process. this would minimize the operation and analysis costs, and assess the stability of wtp performances. this paper focuses on applying an artificial neural network (ann) approach with a feed-forward back-propagation non-linear autoregressive neural network to predict the influent water quality of sanandaj wtp. influent water quality data gathered over a 2-year period were used to building the prediction model. the study signifies that the ann can predict the influent water quality parameters with a correlation coefficient (r) between the observed and predicted output variables reaching up to 0.93. the prediction models developed in this work for alkalinity, ph, calcium, carbon dioxide, temperature, total hardness, turbidity, total dissolved solids, and electrical conductivity have an acceptable generalization capability and accuracy with coefficient of determination (r2) ranging from 0.86 for alkalinity to 0.54 for electrical conductivity. the predicting ann model provides an effective analyzing and diagnosing tool to understand and simulate the non-linear behavior of the influent water characteristics. the developed predicting models can be used by wtp operators and decision makers.