Comparison of the results of response surface methodology and artificial neural network for the biosorption of lead using black cumin

Bioresource Technology

Volumn 112, Issue , 2012, Pages 111-115

  Bingöl, Deniz ^a   Hercan, Merve ^a   Elevli, Sermin ^b   Kiliç, Erdal ^b  

^a KOCAELI UNIVERSITY   (Turkey)

^b ONDOKUZ MAYIS UNIVERSITY   (Turkey)

Author keywords

Artificial neural network (ANN); Biosorption; Black cumin; Lead removal; Response surface methodology (RSM)

Indexed keywords

BATCH SORPTION PROCESS; BIOSORBENTS; BLACK CUMIN; CENTRAL COMPOSITE DESIGNS; LEAD IONS; METAL BIOSORPTION; PREDICTIVE CAPABILITIES; PROCESS VARIABLES; RESPONSE SURFACE METHODOLOGY; ROOT MEAN SQUARE; TRAINING SETS; VALIDATION DATA;

BIOSORPTION; HEAVY METALS; LEAD REMOVAL (WATER TREATMENT); MEAN SQUARE ERROR; PH EFFECTS; SURFACE PROPERTIES;

NEURAL NETWORKS;

LEAD;

AQUEOUS SOLUTION; ARTIFICIAL NEURAL NETWORK; BIOACTIVITY; BIOTECHNOLOGY; DATA SET; DICOTYLEDON; ERROR ANALYSIS; EXPERIMENTAL DESIGN; LEAD; PH; POLLUTANT REMOVAL; PREDICTION; SEED; SORPTION;

ACCURACY; AQUEOUS SOLUTION; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOSORPTION; BLACK CUMIN; COMPARATIVE STUDY; CONTROLLED STUDY; MASS; PH; PLANT SEED; PREDICTION; PRIORITY JOURNAL; RESPONSE SURFACE METHOD; TEMPERATURE; WATER MANAGEMENT;

ADSORPTION; BIODEGRADATION, ENVIRONMENTAL; BIOTECHNOLOGY; LEAD; NEURAL NETWORKS (COMPUTER); NIGELLA SATIVA; REPRODUCIBILITY OF RESULTS; TIME FACTORS;

NIGELLA SATIVA;

EID: 84859210007     PISSN: 09608524     EISSN: 18732976     Source Type: Journal    
DOI: 10.1016/j.biortech.2012.02.084     Document Type: Article

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