Comparison of chitosan based nano-adsorbents for dairy industry wastewater treatment through response surface methodology and artificial neural network models.

The present investigation was focused to compare chitosan based nano-adsorbents (CZnO and CTiO) for efficient treatment of dairy industry wastewater using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) models. The nano-adsorbents were synthesized using chemical precipitation method and characterized by using scanning electron microscope with elemental detection sensor (SEM-EDS) and atomic force microscope (AFM). Maximum %R (96.71 and 87.56%) and %R (90.48 and 82.10%) for CZnO and CTiO nano-adsorbents were obtained at adsorbent dosage of 1.25 mg/L, initial biological oxygen demand (BOD) and chemical oxygen demand (COD) concentration of 100 and 200 mg/L, pH of 7.0 and 2.00, contact time of 100 and 60 min, respectively. The results obtained for both the nano-adsorbents were subject to RSM and ANN models for determination of goodness of fit in terms of sum of square errors (SSE), root mean square error (RMSE), R and Adj. R, respectively. The well trained ANN model was found superior over RSM in prediction of the treatment effect. Hence, the developed CZnO and CTiO nano-adsorbents could be effectively used for dairy industry wastewater treatment.