Adsorption modelling and fixed-bed column study on milk processing industry wastewater treatment using chitosan zinc-oxide nano-adsorbent-coated sand filter bed.

This study aims to develop a chitosan-zinc oxide nano-adsorbent-coated sand (CZOCS) filter bed for getting rid of milk processing industry wastewater (MPIW) in a safe way. Chitosan zinc oxide nano-adsorbent (CZnO) of 88.35 ± 2.89 d.nm size was used to coat naturally available sand through a calcination process at 200 °C for a heating rate of 5 °C/min. The characteristics of the diffraction peaks and functional groups are attributed to the nature of CZnO loading on the sand surface. The increase in breakthrough and exhaustion times is caused by the biological oxygen demand (BOD) and chemical oxygen demand (COD) of influent having more time to interact with the adsorbent as bed height increases, resulting in higher reduction efficiencies. Lower bed heights (10 cm) which saturate faster than higher bed heights (40 cm) result in a shorter mass transfer time. Adsorption capacity at the 95% column exhaustion point increased from 611.04 to 1089.63 mg/g for BOD and from 4512.29 to 5600.81 mg/g for COD. Different kinds of kinetic models have been used to forecast breakthrough curves employing experimental data. Statistical and error function parameters were used to choose the best-fit model. Among the models investigated, the Thomas model was shown to be the best-fit with the highest R-square values of 0.9942 and 0.9939. The CZOCS has high reusability and could be used for up to six cycles of organic pollutant adsorption. Aside from that, novel CZOCS could be used to clean real MPIW, and making it one of the most promising adsorbents.

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 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.