Integrated wastewater treatment plant performance evaluation using artificial neural networks

Proper operation of municipal wastewater treatment plants is important in p roducing an effluent which meets quality requirements of regulatory agencie s and in minimizing detrimental effects on the environment. This paper exam ined plant dynamics and modeling techniques with emphasis placed on the dig ital computing technology of Artificial Neural Networks (ANN). A backpropag ation model was developed to model the municipal wastewater treatment plant at Ardiya, Kuwait City, Kuwait. Results obtained prove that Neural Network s present a versatile tool in modeling full-scale operational wastewater tr eatment plants and provide an alternative methodology for predicting the pe rformance of treatment plants. The overall suspended solids (TSS) and organ ic pollutants (BOD) removal efficiencies achieved at Ardiya plant over a pe riod of 16 months were 94.6 and 97.3 percent, respectively. Plant performan ce was adequately predicted using the backpropagation ANN model. The correl ation coefficients between the predicted and actual effluent data using the best model was 0.72 for TSS compared to 0.74 for BOD. The best ANN structu re does not necessarily mean the most number of hidden layers. (C) 1999 IAW Q Published by Elsevier Science Ltd. All rights reserved.