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Ultrasonic treatment of water contaminated with various pollutants onto copper nanowires loaded on activated carbon using response surface methodology and artificial intelligent
Author(s) -
Yousefi Fakhri,
Ghaedi Mehrorang,
Alekasir Ebtesam,
Asfaram Arash
Publication year - 2017
Publication title -
applied organometallic chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.53
H-Index - 71
eISSN - 1099-0739
pISSN - 0268-2605
DOI - 10.1002/aoc.3878
Subject(s) - response surface methodology , central composite design , adsorption , sigmoid function , chemistry , activated carbon , biological system , artificial neural network , transfer function , chromatography , artificial intelligence , computer science , organic chemistry , biology , electrical engineering , engineering
In this study, the potential application of copper nanowires loaded on activated carbon for simultaneous removal of Disulfine blue (DB), Crystal violet (CV) and Sunset yellow (SY) has been described. The relation between adsorption properties with variables such as solution pH, adsorbent value, contact time and initial dyes concentration was investigated and optimized. A three‐layer artificial neural network (ANN) model was utilized to predict dyes removal (%) by adsorbent following conduction of experiments. The training of network at above mention experimental data confirms its ability to forecast the removal performance with a linear transfer function (purelin) at output layer. The Levenberg–Marquardt algorithm and tangent sigmoid transfer function (tansig) with 16 neurons at the hidden layer was applied. Parameters were optimized by central composite design (CCD) combined with response surface methodology (RSM) and desirability function. The accuracy of ANN was judged according to both MSE and AAD% at optimal conditions and results indicate its superiority to RSM model in term of higher R 2 and lower AAD% values. This observation was also corroborated by the parity plots between the predicted and experimental values. The ANN model was better in both data fitting and prediction capability in comparison to RSM model.