Prediction of adsorption efficiencies of Ni (II) in aqueous solutions with perlite via artificial neural networks

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This study investigates the estimated adsorption efficiency of artificial Nickel (II) ions with perlite in an aqueous solution using artificial neural networks, based on 140 experimental data sets. Prediction using artificial neural networks is performed by enhancing the adsorption efficiency with the use of Nickel (II) ions, with the initial concentrations ranging from 0.1 mg/L to 10 mg/L, the adsorbent dosage ranging from 0.1 mg to 2 mg, and the varying time of effect ranging from 5 to 30 mins. This study presents an artificial neural network that predicts the adsorption efficiency of Nickel (II) ions with perlite. The best algorithm is determined as a quasi-Newton back-propagation algorithm. The performance of the artificial neural network is determined by coefficient determination (R2), and its architecture is 3-12-1. The prediction shows that there is an outstanding relationship between the experimental data and the predicted values.

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Archives of Environmental Protection

The Journal of Institute of Environmental Engineering and Committee of Environmental Engineering of Polish Academy of Sciences

Journal Information

IMPACT FACTOR 2016: 0.708
5-year IMPACT FACTOR: 0.835

CiteScore 2017: 1.01

SCImago Journal Rank (SJR) 2017: 0.371
Source Normalized Impact per Paper (SNIP) 2017: 0.737


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