Author/Authors :
MAHMOOD, QAISAR COMSATS Institute of Information Technology - Department of Environmental Sciences, Pakistan , HAYAT, YOUSAF NWFP Agricultural University - Department of Mathematics, Statistics and Computer Science, Pakistan , JILANI, GHULAM PMAS Arid Agriculture University - Department of Soil Sciences and Water Conservation, Pakistan , HUSSAIN, ZAHID NWFP Agricultural University - Department of Mathematics, Statistics and Computer Science, Pakistan , AZIM, MUHAMMAD RASHID Federal Government Post Graduate College - Department of Botany, Pakistan , WASEEM, MOHAMMAD Allama Iqbal Open University - Department of Biology, Islamabad
Abstract :
During the present investigation the data collected from a lab-scale Anoxic Sulfide Oxidizing (ASO) reactor was used in a neural network system to predict performance. Five uncorrelated components of the influent wastewater were used as inputs to the artificial neural network model to predict the final effluent concentrations using backpropagation and general regression algorithms. The best prediction performance was achieved when the data was fed to a back propagated neural network. Within the range of experimental conditions tested, it was concluded that the ANN model gave predictable results for sulfide and nitrite removal from wastewater through the ASO process. The model did not predict the formation of sulfate in an acceptable manner.
Keywords :
ASO reactor , Back propagation neural network analysis , effluent sulfide prediction , effluent nitrite prediction , Wastewater treatment.
