Prediction of biosorption efficiency for the removal of copper(II) using artificial neural networks

Journal of Hazardous Materials

Volumn 152, Issue 3, 2008, Pages 1268-1275

  Prakash, N ^a   Manikandan, S A ^a   Govindarajan, L ^a   Vijayagopal, V ^a  

^a ANNAMALAI UNIVERSITY   (India)

Author keywords

ANN; Biosorption; Copper(II) (Cu(II)); Neural network; Simulation

Indexed keywords

BIOSORPTION; COMPUTATION THEORY; COMPUTER SIMULATION; CONTAMINATION; COPPER; NEURAL NETWORKS; WASTEWATER TREATMENT;

BIOSORPTION EFFICIENCY; COPPER CONTAINING WASTEWATERS; EMPIRICAL CALCULATIONS;

METAL IONS;

CUPRIC ION; ORGANIC COMPOUND;

BIOSORPTION; COMPUTATION THEORY; COMPUTER SIMULATION; CONTAMINATION; COPPER; METAL IONS; NEURAL NETWORKS; WASTEWATER TREATMENT;

ADSORPTION; AQUEOUS SOLUTION; ARTIFICIAL NEURAL NETWORK; COLOR; COPPER; ORGANIC COMPOUND; POLLUTANT REMOVAL; PREDICTION; WASTEWATER; WATER TREATMENT;

AQUEOUS SOLUTION; ARTICLE; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; BIOSORPTION; COST; HEAVY METAL REMOVAL; PARAMETER; PREDICTION; SAWDUST; SIMULATION;

COPPER; MANGIFERA; NEURAL NETWORKS (COMPUTER); PARTICLE SIZE; TEMPERATURE;

EID: 40749090832     PISSN: 03043894     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhazmat.2007.08.015     Document Type: Article

References (15)

1. Chubar N., Carvalho J.R., and Joana Neiva Correia M. Cork biomass as biosorbent for Cu(II), Zn(II) and Ni(II). Colloids Surf. A: Physicochem. Eng. Aspects 230 (2003) 57-65
2. Tarley C.R.T., and Arruda M.A.Z. Biosorption of heavy metals using rice milling by-products. Characterization and application for removal of metals from aqueous effluents. Chemosphere 54 (2004) 987-995
3. Iqbal M., and Edyvean R.G.J. Biosorption of lead, copper and zinc ions on loofa sponge immobilized biomass of Phanerochaete chrysosporium. Miner. Eng. 17 (2004) 217-223
4. Cetinkaya Donmez G., Aksu Z., Ozturk A., and Kutsal T. A comparative study on heavy metal biosorption characteristics of some algae. Process Biochem. 34 (1999) 885-892
5. Susan Bailey E., Trudy Olin J., Mark Bricka R., and Dean Adrian D. A review of potentially low-cost sorbents for heavy metals. Water Res. 33 11 (1999) 2469-2479
6. Ajmal M., Khan A.H., Ahmad S., and Ahmad A. Role of sawdust in the removal of copper(II) from industrial wastes. Water Res. 32 (1998) 3088-3091
7. Pramanik K. Use of ANN for predication of cell mass and ethanol concentration in batch fermentation using saccbaromyces cerevisiae yeast. Instit. Eng. (India) 85 (2004) 31-35
8. Baughman D.R., and Liu Y.A. Neural Network in Bioprocessing and Chemical Engineering (1995), Academic Press, San Diego
9. Elman J.L. Finding structure in time. Cognitive Sci. 14 (1990) 179-211
10. Du X., Yuan Q., Zhao J., and Li Y. Comparison of general rate model with a new model-artificial neural network model in describing chromatographic kinetics of solanesol adsorption in packed column by macro porous resins. J. Chromatogr. A 23 (2007) 165-174
11. van Deventer J.S.J., Liebenberg S.P., Lorenzen L., and Aldrich C. Dynamic modelling of competitive elution of activated carbon in columns using neural networks. Miner. Eng. 8 (1995) 1489-1501
12. Gao W., and Engell S. Estimation of general nonlinear adsorption isotherms from chromatograms. Comput. Chem. Eng. 29 (2005) 2242-2255
13. Haykin S. Neural Networks: A Comprehensive Foundation (1999), Prentice Hall
14. Mandic D., and Chambers J. Recurrent Neural Networks (2001), Wiley
15. http://en.wikipedia.org/wiki/Artificial_neural_network#Other_types_of_networks