Predicting bod levels of wastewater with neural network time series

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The quality of treated wastewater has always been an important issue, but it becomes even more critical as human populations increase. An accurate well-timed measurement of quality variables is essential to the successful monitoring and controlling of wastewater treatment systems. Unfortunately, current ability to monitor and control effluent quality from a wastewater treatment process is primitive. Control is difficult because wastewater treatment consists of complex multivariate processes with nonlinear relationships and time varying dynamics. Because the measurements of these variables are difficult and often involve large time delays, there is a critical need for forecasting models that are effective in predicting wastewater effluent quality. In this paper, predictive models based on artificial neural networks are presented. Water quality measurements and process data from an urban wastewater treatment plant were used to develop models to predict biochemical oxygen demand (BOD). The results provide evidence that nonlinear neural network time series models achieve accurate forecast of wastewater effluent quality.
A thesis submitted to the Institute of Distance Learning, Kwame Nkrumah University of Science and Technology in partial fulfillment of the requirements for the degree of Master of Science in Environmental Science