Artificial neural network (ANN) and adaptive neuro-fuzzy interference system (ANFIS) modelling for nickel adsorption onto agro-wastes and commercial activated carbon. Issue 6 (December 2018)

• Record Type: Journal Article
• Authors: Souza, P.R.; Dotto, G.L.; Salau, N.P.G.
• Is Part Of: Journal of environmental chemical engineering. Volume 6:Issue 6(2018)
• Page Start: 7152
• Page End: 7160
• Publication Date: 2018-12
• Subjects: Nickel -- Agro-wastes -- Adsorption -- Artificial neural network -- Adaptive neuro-fuzzy interference system; Chemical engineering -- Environmental aspects -- Periodicals; Environmental engineering -- Periodicals; Chemical engineering -- Environmental aspects; Environmental engineering; Periodicals; 660.0286
• DOI: 10.1016/j.jece.2018.11.013
• Languages: English
• ISSNs: 2213-2929

Abstract

Graphical abstract: Highlights: Four agro-wastes were prepared and compared with commercial activated carbon to adsorb Ni 2+ . Sugarcane bagasse and orange peel showed best adsorption performance for Ni 2+ removal. ANN and ANFIS models were used to predict the Ni 2+ adsorption capacity. Initial adsorbate concentration, adsorption time, pHZPC and surface area of each adsorbent were defined as input data. The developed ANFIS can be successfully used for forecasting the adsorption capacity of Ni 2+ . Abstract: 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 Ni 2+ 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 Ni 2+ removal from aqueous solutions, even better than those of commercial activated carbon. The study also revealed that the adsorption capacity is affected by pHZPC and surface area. ANN and ANFIS were compared with the experimental data to determine the relationship of four input parameters on Ni 2+ adsorption capacities: initial adsorbent concentration, adsorption time, pHZPC 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.