The present investigation was focused to compare chitosan based nano-adsorbents (CZnO and CTiO2) for efficient treatment of dairy industry wastewater using RSM and ANN models. The nano-adsorbents were synthesized using chemical precipitation method and characterized by using SEM-EDS and 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 BOD and 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 SSE, 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.

The present study emphasised the efficiency of chitosan anchored titanium dioxide nano-adsorbent on dairy industry effluent treatment. Chitosan titanium dioxide nano-adsorbent was synthesised by using chemical precipitation method and characterised for its particle size, surface morphology and texture. A four-factor-three-level Box–Behnken design along with response surface methodology was used to optimise the adsorption process parameters. Linear, two factor interaction, quadratic and cubic model techniques were used to demonstrate the influence of each parameter and their interaction effects on the responses. The quadratic models derived from the experimental data were used to predict the maximum per cent reduction of biological oxygen demand (BOD) and chemical oxygen demand (COD). The optimised treatment combination for maximum per cent reduction in BOD (90.48%) and COD (82.10%) was found to be initial concentration of 100 mg L−1, pH of 7, dosage of 1.25 mg L−1 and contact time of 100 min.

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The milk processing industry produces a large amount of effluent that contains a lot of organic contaminants. Effluents, if improperly disposed of, can have serious environmental and public health consequences. The goal of this study is to create chitosan-zinc oxide nano-adsorbent coated sand (CZOCS) for getting rid of milk processing industry wastewater (MPIW) in a safe way.The developed adsorbent was characterised, and the presence of a zinc coating on the sand surface was confirmed. The goal of this study was to reduce organic contaminants in MPIW. There has been no evidence of CZOCS being used for industrial wastewater treatment to date. The effectiveness of the adsorbent and the performance of the column were examined using column adsorption experiments. The influence of filtration time and height of the bed on breakthrough curves was also investigated. 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 model. Among these models, the Thomas model was shown to be the best fit. Breakthrough and exhaustion times were shown to be higher as the bed height increased. The CZOCS has high reusability and could be used for up to six cycles of organic pollutant adsorption.Aside from that, novel CZOCS was used to clean real MPIW, making it one of the most promising adsorbents.

Normally groundwater recharge is estimated using methods based on water balance, water table fluctuations, fixed factor of annual rainfall and tracer movement. In many of these methods water stored in the vadose zone and evapotranspiration are not accounted properly. These factors control groundwater recharge to a large extent, particularly in arid and semi-arid regions which are normally characterized by a deep water table, thick vadose zone and high evapotranspiration. In this study, HYDRUS-1D and MODFLOW models were used to assess the recharge flux and groundwater recharge in an area under a semi-arid region giving due consideration to important vadose zone processes. Cumulative recharge flux at water table in various sub-areas varied from 20.01 cm to 23.43 cm (29.26% to 34.26% of the monsoon rainfall). The average groundwater recharge was 22.2%. Total surface runoff in various sub-areas varied from 3.39 cm to 14.36 cm (5% to 21% of the monsoon rainfall). Evapotranspiration was found to be a major recharge controlling factor. Reference evapotranspiration varied from 37.19 cm to 45 cm (54% to 66% of the monsoon rainfall). Natural recharge under the prevailing pumping rate and pumping schedule was 23.3% of the monsoon rainfall. Simulation results revealed that if all the surface runoff is retained in the area, water table will rise by 1.46 m.