Solar Photocatalytic Degradation of Organic Contaminants in Landfill Leachate using TiO2 Nanoparticles by RSM and ANN

In the present study, artificial neural network (ANN) and response surface methodology (RSM) models were used to investigate the heterogeneous photocatalysis performance in removal of chemical oxygen demand (COD) from landfill leachate using compound parabolic collector. Effect of the three parameters, i.e. pH, catalyst dosage, and irradiation time was studied for COD removal efficiency and these parameters are optimized by the RSM. The optimum values of pH 5, the dosage of 0.75 g/L and irradiation time of 100 minutes are capable to remove 32.19% of COD from the leachate. A good agreement is shown by the analysis of variance for the regression coefficient R2 for predicted value (0.92268) and adjusted value (0.9776). The proposed RSM and ANN model R2 values were found to be 0.9882 and 0.9974 respectively, which confirms the ideality of RSM and ANN. The results also confirm that the input and output data from RSM could be appropriate to build the ANN model. Further BOD5/COD ratio is studied for the biodegradability of leachate and it was found that increase of biodegradability value from 0.17 to 0.47 was at pH 3, catalyst dosage of 1 g/L and irradiation time of 150 minutes.