APPLICATION OF ARTIFICIAL NERUON NETWORK (ANN) FOR MODELING OF NITRATE ADSORPTION ONTO GRANULAR ACTIVATED CARBON (GAC)

APPLICATION OF ARTIFICIAL NERUON NETWORK (ANN) FOR MODELING OF NITRATE ADSORPTION ONTO GRANULAR ACTIVATED CARBON (GAC)

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Part of #APPLICATION OF ARTIFICIAL NERUON NETWORK (ANN) FOR MODELING OF NITRATE ADSORPTION ONTO GRANULAR ACTIVATED CARBON (GAC)# :

Publishing year : 2008

Conference : Twelfth National Congress of Chemical Engineering of Iran

Number of pages : 12

Abstract: High concentrations of N-containing compounds in drinking water cause health problems such as cyanosis among children and cancer of the alimentary canal. Therefore, removal of nitrate from water samples is important from the health and environmental point of view. In this work, we investigated the effective parameters on removal of nitrate by adsorption process, which were the amount of granular activated carbon (GAC), initial concentration, contact time, pH and temperature. The removal process was monitored using an on-line spectrophotometric analysis system. Our results showed that the content of adsorption was followed by a decreasing order: m = 10 5> 2 gg, C0 = 20> 15 25> 10 ppm, pH = 4> 7 10> 1 and T = 25> 35 45 ; 55 C. The Three-layered feed back forward propagation neural network was used for modeling nitrate adsorption on granular activated carbon. The comparison between the proposed ANN model and experimental data proved that nitrate adsorption process modeling using artificial neuron networks was a good and accurate method for predicting nitrate adsorption across GAC under different conditions.