In this study, waste tires were used to develop activated carbon for the adsorption of Cr(VI) from aqueous solutions, and an artificial neural network (ANN) model was applied to predict the adsorption efficiency of waste-tire activated carbon (WTAC). SEM and FTIR were used to characterize the developed WTAC. A three-layer ANN with different training algorithms and hidden layers with different numbers of neurons was developed using 79 data sets gathered from batch adsorption experiments with different initial Cr(VI) ion concentrations, contact periods, temperatures, and doses. Conjugate gradient backpropagation of Powell-Beale restarts (traincgb) was found to be the best training algorithm among all the training algorithms, with an RMSE of 5.894 and an R2 of 0.985. The ANN topology had 4, 8, and 4 neurons in the input, hidden, and output layers. The correlation coefficient of the ANN models of Cr(VI) ion adsorption efficiency is 0.977.

For many years, especially in emerging nations like India, the environment has been threatened by the increased output of industrial wastes and heavy metal toxicity. The usage of inexpensive adsorbents has recently attracted a lot of attention in studies on the removal of heavy metals like nickel from industrial wastewater. The use of agro-based adsorbent is an alternative to conventionally used activated charcoal. In this research, adsorption experiments were carried out using agro-based adsorbent prepared from rice husk, wheat husk, and soybean husk to reduce nickel from industrial wastewater. The adsorption process is simple, economical, and effective is the most preferred method used for the removal of toxic metals like nickel from industrial wastewater. Adsorbents prepared from these husks can be effectively used for adsorption due to low cost & high availability. Characterization of agricultural material by various tests like XRF, proximate analysis, bulk density, and iodine number was conducted on agro-based adsorbents to know the co-relation between removal efficiency and adsorption capacity. The effect of turbidity and pH parameters on Ni removal efficiency is also studied. Results indicated that wheat husk adsorbent appeared to be the most effective for the adsorption of Ni from wastewater as compared to soybean husk and rice husk adsorbent. Wheat husk, soybean husk, and rice husk have removal efficiency in the range of 62.50 to 73.33. Composite absorbents CA-2 with the proportion of 50% wheat husk, 33% soybean husk, and 17% rice husk have 82.50% efficiency, and CA-3 has 80.83% efficiency in removing Nickel. Wheat husk adsorbent, CA-2, and CA-3 are more effectively and sustainably used for the treatment of industrial wastewater to remove heavy metals.