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

• Published in: Water science and technology Vol. 83; no. 5; pp. 1250 - 1264
• Main Authors: Dinesha, B. L., Hiregoudar, Sharanagouda, Nidoni, Udaykumar, Ramappa, K. T., Dandekar, Anilkumar, Ravi, M. V.
• Format: Journal Article
• Language: English
• Published: England IWA Publishing 01.03.2021
• Subjects: Adsorbents; Adsorption; Artificial neural networks; Atomic force microscopy; Biochemical oxygen demand; Caustic soda; Chemical oxygen demand; Chemical precipitation; Chemical synthesis; Chitosan; Dairy industry; Dairy industry wastewaters; Dairying; Goodness of fit; Metal oxides; Methods; modelling; nano-adsorbents; Neural networks; Neural Networks, Computer; Optimization; Oxygen requirement; Particle size; Pollutants; Response surface methodology; Root-mean-square errors; Scanning electron microscopy; Total oxygen demand; Waste Water; Wastewater treatment; Water Purification; Water treatment; Zinc oxides
• ISSN: 0273-1223; 1996-9732
• DOI: 10.2166/wst.2021.035

The present investigation was focused to compare chitosan based nano-adsorbents (CZnO and CTiO2) 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 %RBOD (96.71 and 87.56%) and %RCOD (90.48 and 82.10%) for CZnO and CTiO2 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), R2 and Adj. R2, respectively. The well trained ANN model was found superior over RSM in prediction of the treatment effect. Hence, the developed CZnO and CTiO2 nano-adsorbents could be effectively used for dairy industry wastewater treatment.