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

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 of significant important from the health and environmental point of view. In this work, the effective parameters on removal of nitrate by adsorption process that were amount of granular activated carbon (GAC), initial concentration, contact time, pH and temperature were investigated. The removal process was monitored using an on-line spectrophotometric analysis system. Our results showed that the content of adsorption followed decreasing order: m= 10>5>2>1g, C0= 20>15>25>10 ppm, pH=4>7>10>1 and T=25>35>45>55oC. The Three layered feed forward back propagation neural network was used for modeling of nitrate adsorption on granular activated carbon. The comparison between the predicted results of the designed ANN model and experimental data proved that modeling of nitrate adsorption process using artificial neuron network was a good and precise method to predict adsorption extent of nitrate on GAC under different conditions.

Keywords:

Adsorption , Artificial neuron network , Granular activated carbon , Nitrate , Online analysis system , Water treatment