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Enhancement of heavy metals sorption via nanocomposites of rice straw and Fe3O4 nanoparticles using artificial neural network (ANN)

Khandanlou, R. and Fard Masoumi, H. R. and Ahmad, M. B. and Shameli, K. and Basri, M. and Kalantari, K. (2016) Enhancement of heavy metals sorption via nanocomposites of rice straw and Fe3O4 nanoparticles using artificial neural network (ANN). Ecological Engineering, 91 . pp. 249-256. ISSN 0925-8574

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Abstract

The artificial neural network (ANN) modeling of adsorption of Pb(II) and Cu(II) was carried out for determination of the optimum values of the variables to get the maximum removal efficiency. The input variables were initial ion concentration, adsorbent dosage, and removal time, while the removal efficiency was considered as output. The performed experiments were designed into two data sets including training, and testing sets. To acquire the optimum topologies, ANN was trained by quick propagation (QP), Batch Back Propagation (BBP), Incremental Back Propagation (IBP), genetic algorithm (GA) and Levenberg-Marquardt (LM) algorithms for testing data set. The topologies were defined by the indicator of minimized root mean squared error (RMSE) for each algorithm. According to the indicator, the IBP-3-9-2 was selected as the optimized topologies for heavy metal removal, due to the minimum RMSE and maximum R-squared.

Item Type:Article
Uncontrolled Keywords:Adsorption, Backpropagation, Efficiency, Genetic algorithms, Heavy metals, Lead, Mean square error, Neural networks, Statistical tests, Topology, Artificial neural network models, Heavy metal removal, Ion concentrations, Levenberg-Marquardt algorithm, Optimized topology, Optimum topologies, Removal efficiencies, Root mean squared errors, Backpropagation algorithms, adsorption, algorithm, artificial neural network, bioremediation, experimental study, heavy metal, iron oxide, nanoparticle, pollutant removal, rice, straw, topology
Subjects:T Technology > T Technology (General)
Divisions:Malaysia-Japan International Institute of Technology
ID Code:71614
Deposited By: Widya Wahid
Deposited On:20 Nov 2017 08:28
Last Modified:20 Nov 2017 08:28

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