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

Ecological Engineering

Volumn 91, Issue , 2016, Pages 249-256

  Khandanlou, Roshanak ^a   Fard Masoumi, Hamid Reza ^b   Ahmad, Mansor B ^b   Shameli, Kamyar ^c   Basri, Mahiran ^b   Kalantari, Katayoon ^c  

^a UNIVERSITY OF MALAYA   (Malaysia)

^b UNIVERSITY PUTRA MALAYSIA   (Malaysia)

^c MALAYSIA JAPAN INTERNATIONAL INSTITUTE OF TECHNOLOGY   (Malaysia)

Author keywords

Adsorption; Artificial neural network (ANN); Heavy metal; Removal efficiency; Topologies

Indexed 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;

EID: 84960362307     PISSN: 09258574     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecoleng.2016.03.012     Document Type: Article

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