Artificial neural network (ANN) and adaptive neuro-fuzzy interference system (ANFIS) modelling for nickel adsorption onto agro-wastes and commercial activated carbon

Artificial neural network (ANN) and adaptive neuro-fuzzy interference system (ANFIS) were applied to model and analyze the adsorption of four different agro-wastes, namely sugarcane bagasse, passion fruit waste, orange peel and pineapple peel, and commercial activated carbon, for Ni2+ removal from aqueous solutions. The capacity of adsorption ranged from 14.75 to 63.50 mg g(-1), and the results of the adsorption experiments revealed that sugarcane bagasse and orange peel presented the best adsorption performance for Ni2+ removal from aqueous solutions, even better than those of commercial activated carbon. The study also revealed that the adsorption capacity is affected by pH(z)(p)(c) and surface area. ANN and ANFIS were compared with the experimental data to determine the relationship of four input parameters on Ni2+ adsorption capacities: initial adsorbent concentration, adsorption time, pH(zp)(c) and surface area. The developed ANN and ANFIS could accurately predict the experimental data with correlation coefficient of 0.9926 and 0.9943, respectively. The Pearson's Chi-square measure was found to be 0.9508 for ANN and 0.5959 for ANFIS, indicating a small advantage of ANFIS over ANN.