Development of experimental design approach and ANN-based models for determination of Cr(VI) ions uptake rate from aqueous solution onto the solid biodiesel waste residue

Bioresource Technology

Volumn 148, Issue , 2013, Pages 550-559

  Shanmugaprakash, M ^a   Sivakumar, V ^b  

^a KUMARAGURU COLLEGE OF TECHNOLOGY   (India)

^b KONGU ENGINEERING COLLEGE   (India)

Author keywords

Artificial neural networks; Biosorption; Cr(VI) ions; DPOC; Response surface methodology

Indexed keywords

BACKPROPAGATION ALGORITHMS; BIOSORPTION; MATHEMATICAL MODELS; MATLAB; MEAN SQUARE ERROR; NEURAL NETWORKS;

ABSOLUTE AVERAGE DEVIATION; CENTRAL COMPOSITE DESIGNS; COEFFICIENT OF DETERMINATION; CRIII IONS; DPOC; EXPERIMENTAL DESIGN APPROACHES; MULTI-LAYER FEED-FORWARD NETWORKS; RESPONSE SURFACE METHODOLOGY;

CHROMIUM COMPOUNDS;

BIODIESEL; CHROMIUM; OIL;

AQUEOUS SOLUTION; ARTIFICIAL NEURAL NETWORK; BIOFUEL; CHROMIUM; DATA ACQUISITION; ERROR ANALYSIS; OPTIMIZATION; PARAMETERIZATION; PERFORMANCE ASSESSMENT;

ALGORITHM; AQUEOUS SOLUTION; ARTICLE; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; BIOSORPTION; COMPUTER PROGRAM; CONTROLLED STUDY; FLOW RATE; MATHEMATICAL MODEL; PONGAMIA; PREDICTIVE VALUE; PRIORITY JOURNAL; RESPONSE SURFACE METHOD; TEMPERATURE; WASTE WATER;

PONGAMIA;

ARTIFICIAL NEURAL NETWORKS; BIOSORPTION; CR(VI) IONS; DPOC; RESPONSE SURFACE METHODOLOGY;

ADSORPTION; BIOFUELS; BIOTECHNOLOGY; CHROMIUM; IONS; MODELS, THEORETICAL; NEURAL NETWORKS (COMPUTER); PLANT OILS; PONGAMIA; REPRODUCIBILITY OF RESULTS; SOLID WASTE; SOLUTIONS; WATER POLLUTANTS, CHEMICAL;

EID: 84884719999     PISSN: 09608524     EISSN: 18732976     Source Type: Journal    
DOI: 10.1016/j.biortech.2013.08.149     Document Type: Article

References (34)

1. Apha A. WPCF. 1989. Standard Methods for the Examination of Water and Wastewater 2005, American Public Health Association, Washington DC. 17th ed.
2. Badkar, D.S., Pandey, K.S., Buvanashekaran, G., 2013. Development of RSM and ANN-based models to predict and analyze the effects of process parameters of laser-hardened commercially pure titanium on heat input and tensile strength. Int. J. Adv. Manuf. Technol., 1-20.
3. Bingol D., Hercan M., Elevli S., Kilic E. Comparison of the results of response surface methodology and artificial neural network for the biosorption of lead using black cumin. Bioresour. Technol. 2012, 112:111-115.
4. Bishnoi N.R., Kumar R., Kumar S., Rani S. Biosorption of Cr (III) from aqueous solution using algal biomass Spirogyra spp. J. Hazard. Mater. 2007, 145:142-147.
5. Box G.E.P., Hunter J.S. Multi-factor experimental designs for exploring response surfaces. Ann. Math. Stat. 1957, 28:195-241.
6. Box G.E.P., Wilson K.B. On the experimental attainment of optimum conditions. J. R. Stat. Soc. Ser. B (Methodological) 1951, 13:1-45.
7. Celekli A., Bozkurt H.S., Geyik F. Use of artificial neural networks and genetic algorithms for prediction of sorption of an azo-metal complex dye onto lentil straw. Bioresour. Technolo. 2012, 129:396-401.
8. Choong T.S.Y., Chuah T.G., Robiah Y., Gregory Koay F.L., Azni I. Arsenic toxicity, health hazards and removal techniques from water: an overview. Desalination 2007, 217:139-166.
9. DeCarlo, N. 2007. Lean six sigma.
10. Desai K.M., Survase S.A., Saudagar P.S., Lele S.S., Singhal R.S. Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: case study of fermentative production of scleroglucan. Biochem. Eng. J. 2008, 41:266-273.
11. Fausett, 1994. Fundamentals of Neural Network, Archictures, Algorthims and Applications. Englewood Clis. NJ. USA, pp. 155-178.
12. Geyikci F., Kilic E., Coruh S., Elevli S. Modeling of lead adsorption from industrial sludge leachate on red mud by using RSM and ANN. Chem. Eng. J. 2012, 183:53-59.
13. Gibb H.J., Lees P.S.J., Pinsky P.F., Rooney B.C. Lung cancer among workers in chromium chemical production. Am. J. Ind. Med. 2000, 38:115-126.
14. Gomez V., Callao M.P. Chromium determination and speciation since 2000. TrAC Trends Anal. Chem. 2006, 25:1006-1015.
15. Gupta V.K., Rastogi A. Biosorption of lead from aqueous solutions by green algae Spirogyra species: kinetics and equilibrium studies. J. Hazard. Mater. 2008, 152:407-414.
16. Hamsaveni D.R., Prapulla S.G., Divakar S. Response surface methodological approach for the synthesis of isobutyl isobutyrate. Process Biochem. 2001, 36:1103-1109.
17. Haykin S. Neural Netw 1998, A Comprehensive Foundation, Prentice Hall International, New Jersey.
18. Kumari P., Sharma P., Srivastava S., Srivastava M.M. Biosorption studies on shelled Moringa oleifera lamarck seed powder: removal and recovery of arsenic from aqueous system. Int. J. Miner. Process 2006, 78:131-139.
19. Kundu S., Gupta A.K. Analysis and modeling of fixed bed column operations on As (V) removal by adsorption onto iron oxide-coated cement (IOCC). J. Colloid Interface Sci. 2005, 290(1):52-60.
20. Limpon, R.P., 1987. An introduction to computing with neural nets. IEEE ASSP Magazine, 4-22.
21. Malkoc E., Nuhoglu Y., Dundar M. Adsorption of chromium (VI) on pomaceâ€"an olive oil industry waste: batch and column studies. J. Hazard. Mater. 2006, 138(1):142-151.
22. Mohan D., Pittman C.U. Arsenic removal from water/wastewater using adsorbents. A critical review. J. Hazard. Mater. 2007, 142:1-53.
23. Montgomery, D.C., Myers, R.H., 2002. Response surface methodology: process and product optimization using designed experiments. Response Surface Methodology: Process and Product Optimization Using Designed Experiments, A Wiley-Interscience Publications.
24. Muthusamy, S., Venkatachalam, S., Jeevamani, P., Rajarathinam, N., 2013. Biosorption of Cr(VI) and Zn (II) ions from aqueous solution onto the solid biodiesel waste residue: mechanistic, kinetic and thermodynamic studies. Environ. Sci. Pollut. Res., 1-16.
25. Nadaroglu H., Kalkan E., Demir N. Removal of copper from aqueous solution using red mud. Desalination 2010, 251(1):90-95.
26. Nielsen R.H. Neurocomputing picking the human brain. IEEE Spectr. 1998, 25(3):36-41.
27. Owlad M., Aroua M.K., Daud W.A.W., Baroutian S. Removal of hexavalent chromium-contaminated water and wastewater: a review. Water Air Soil Pollut. 2009, 200:59-77.
28. Ozdemir U., Azbay B., Veli S., Zor S. Modeling adsorption of sodium dodecyl benzene sulfonate (SDBS) onto polyaniline (PANI) by using multi linear regression and artificial neural networks. Chem. Eng. J. 2011, 178:183-190.
29. Prakash N., Manikandan S.A., Govindarajan L., Vijayagopal V. Prediction of biosorption efficiency for the removal of copper (II) using artificial neural networks. J. Hazard. Mater. 2008, 152:1268-1275.
30. Rafiq M.Y., Bugmann G., Easterbrook D.J. Neural network design for engineering applications. Comput. Struct. 2001, 79(17):1541-1552.
31. Raj, K.R., Kardam, A., Arora, J.K., Srivastava, S., 2012. An application of ANN modeling on the biosorption of arsenic. Waste Biomass Valorization. 1-7.
32. Ranjan D., Mishra D., Hasan S.H. Bioadsorption of arsenic: an artificial neural networks and response surface methodological approach. Ind. Eng. Chem. Res. 2011, 50(17):9852-9863.
33. Ravikumar K., Pakshirajan K., Swaminathan T., Balu K. Optimization of batch process parameters using response surface methodology for dye removal by a novel adsorbent. Chem. Eng. J. 2005, 105:131-138.
34. Shanmugaprakash, M., Sivakumar, V., Manimaran, M., Aravind, J., 2013. Batch and dynamics modeling of the biosorption of Cr(VI) from aqueous solutions by solid biomass waste from the biodiesel production. Environ. Prog. Sustainable Energy http://dx.DOI.org/doi.10.1002/ep.11781.