Application of Neural Network on the Loading of Copper onto Clinoptilolite
The study investigated the implementation of the
Neural Network (NN) techniques for prediction of the loading of Cu
ions onto clinoptilolite. The experimental design using analysis of
variance (ANOVA) was chosen for testing the adequacy of the
Neural Network and for optimizing of the effective input parameters
(pH, temperature and initial concentration). Feed forward, multi-layer
perceptron (MLP) NN successfully tracked the non-linear behavior of
the adsorption process versus the input parameters with mean squared
error (MSE), correlation coefficient (R) and minimum squared error
(MSRE) of 0.102, 0.998 and 0.004 respectively. The results showed
that NN modeling techniques could effectively predict and simulate
the highly complex system and non-linear process such as ionexchange.
[1] R.E. Clement,G.A. Eiceman, C.J. Koester, "Environmental analysis”. J.
Anal. Chem. 1995,vol.67,pp.221-255.
[2] E. Muzenda, J. Kabuba,F. Ntuli, M. Mollagee and A.F. Mulaba-
Bafubiandi, "Cu (II) Removal from Synthetic Waste Water by Ion
Exchange Process”, Proceedings of the WCECS, vol. II, 2011, Oct. 19-
21, San Francisco, USA.
[3] A. Amankwah,J. Kabuba , A.F. Mulaba-Bafubiandi, "Modeling of Ion
exchange process using time delayed Neural Networks”. Proceedings of
the ICMET, 2011, vol. 1, pp. 447-451.
[4] J. Kabuba, A.F. Mulaba-Bafubiandi, "Modeling of Co-Cu elution from
clinoptilolite using Neural Network”, World Academy of Science,
Engineering and Technology, 2012, vol. 68, pp. 1222-1225.
[5] E.S. Elmolla, M. Chaudhuri, M.M. Eltoukhy,"The use of artificial
neural network (ANN) for modeling of COD removal from antibiotic
aqueous solution by the Fenton process”, J. Haz. Mat., 2010, vol 179:
127-134.
[6] V.K. Pareek, M.P. Brungs, A.A. Adesina, R. Sharma. "Artificial neural
network modeling of a multiphase photodegradation system”, J.
Photochem. Photobiol. A: Chem., 2002, vol. 149, pp. 139-146.
[7] M. Cote M, B.P.A. Grandjean, P. Lessard, J. Thibault, "Dynamic
modeling of activated sludge process: improving prediction using neural
networks”, water Res., 1995, vol. 29, pp. 995-1004.
[8] I. Machon, H. Lopez, J. Rodriguez-Iglesias, E. Maranan, I. Vazquez, "
Simulation of a coke wastewater nitrification process using a feedforward
neuronal net” , Environ. Model Softw., 2007, vol. 22, pp.1382-
1387.
[9] A. Aleboyeb, M.B. Kasiri, M.E. Olya, H. Aleboyeh, " Prediction of azo
dye decolorization by UV/H2O2 usingartificial neural networks”, Dyes
Pigments, 2008, vol. 77, pp. 288-294.
[10] N. Prakash, S.A. Manikandan, L. Govindarajan, V. Vijayagopal.
"Prediction of biosorption efficiency for the removal of copper (II) using
artificial neural networks”, J. of Hazardous Materials, 2008, pp.1268-
1275.
[11] N. Daneshvar, A.R. Kahataee, N. Djafarzadeh, "The use of artificial
neural networks (ANN) for modeling of decolorization of textile dye
solution containing C.I. basic Yellow 28 by electrocoagulation process”,
J. of Hazardous Materials, 2006, vol. B 137, pp. 1788-1795.
[12] A. Amankwah, J. Kabuba, "Comparison of Neural Networks and
Kalman Filter for the modeling of Ion Exchange Process”, Life Sci J.,
2013, vol. 10, pp. 1012-1015.
[13] S.A. Abdulkareen, E. Muzenda, A.S. Afolabi, J. Kabuba, " Treatment of
clinoptilolite as an Adsorbent for the Removal of copper Ion from
Synthetic Wastewater solution”, Arab. J. Sci. Eng., 2013, vol. 38, pp.
2263-2272.
[14] B.B. Mamba, D.W. Nyembe and A.F. Mulaba-Bafubiandi, "The effect
of conditioning with NaCl, KCl and HCl on the performance of natural
clinoptilolite’s removal efficiency of Cu2+ and Co2+ Synthetic solution”,
Water SA., 2009, vol. 36, pp. 437-444.
[15] E. Oguz,A. Tortum, B. Keskinler. "Determination of the apparent rate
constants of the degradation of humic substances by ozonation and
modeling of the removal of humic substances from the aqueous
solutions with neural network”, J. of Hazardous Materials, 2008, vol.
157, pp. 455-463.
[16] F.L. Toma, S. Guessasma, D. Klein, G. Montavon, G. Bertrand,C.
Coddet,"Neural computation to predict TiO2photocatalytic efficiency for
nitrogen oxides removal. J. Photochem. Photobiol. A; Chem.2004, vol.
165, pp. 91-96.
[17] D.R. Baughman and Y.A. Liu, "Neural Networks in Bioprocessing and
Chemical Engineering”. Academic Press, San Diego, California, USA,
1995.
[18] M. Sadrzadeh, T. Mohammadi, J. Ivakpour, N. Kasiri, "Separation of
lead ions from wastewater using electrodialysis: Comparing
mathematical and neural network modeling”, Chemical Engineering
Journal, 2008, vol. 144, pp. 431-441.
[19] M. Horsfall, A.I. Spiff, "Effects of metals ion concentration on the
biosorption of Pb2+ and Cd2+ from aqueous solution by Caladium bicolor
(Wild Cocoyan)”, Afr. J. Biotechnol. 2005, vol. 4, pp. 191-196.
[1] R.E. Clement,G.A. Eiceman, C.J. Koester, "Environmental analysis”. J.
Anal. Chem. 1995,vol.67,pp.221-255.
[2] E. Muzenda, J. Kabuba,F. Ntuli, M. Mollagee and A.F. Mulaba-
Bafubiandi, "Cu (II) Removal from Synthetic Waste Water by Ion
Exchange Process”, Proceedings of the WCECS, vol. II, 2011, Oct. 19-
21, San Francisco, USA.
[3] A. Amankwah,J. Kabuba , A.F. Mulaba-Bafubiandi, "Modeling of Ion
exchange process using time delayed Neural Networks”. Proceedings of
the ICMET, 2011, vol. 1, pp. 447-451.
[4] J. Kabuba, A.F. Mulaba-Bafubiandi, "Modeling of Co-Cu elution from
clinoptilolite using Neural Network”, World Academy of Science,
Engineering and Technology, 2012, vol. 68, pp. 1222-1225.
[5] E.S. Elmolla, M. Chaudhuri, M.M. Eltoukhy,"The use of artificial
neural network (ANN) for modeling of COD removal from antibiotic
aqueous solution by the Fenton process”, J. Haz. Mat., 2010, vol 179:
127-134.
[6] V.K. Pareek, M.P. Brungs, A.A. Adesina, R. Sharma. "Artificial neural
network modeling of a multiphase photodegradation system”, J.
Photochem. Photobiol. A: Chem., 2002, vol. 149, pp. 139-146.
[7] M. Cote M, B.P.A. Grandjean, P. Lessard, J. Thibault, "Dynamic
modeling of activated sludge process: improving prediction using neural
networks”, water Res., 1995, vol. 29, pp. 995-1004.
[8] I. Machon, H. Lopez, J. Rodriguez-Iglesias, E. Maranan, I. Vazquez, "
Simulation of a coke wastewater nitrification process using a feedforward
neuronal net” , Environ. Model Softw., 2007, vol. 22, pp.1382-
1387.
[9] A. Aleboyeb, M.B. Kasiri, M.E. Olya, H. Aleboyeh, " Prediction of azo
dye decolorization by UV/H2O2 usingartificial neural networks”, Dyes
Pigments, 2008, vol. 77, pp. 288-294.
[10] N. Prakash, S.A. Manikandan, L. Govindarajan, V. Vijayagopal.
"Prediction of biosorption efficiency for the removal of copper (II) using
artificial neural networks”, J. of Hazardous Materials, 2008, pp.1268-
1275.
[11] N. Daneshvar, A.R. Kahataee, N. Djafarzadeh, "The use of artificial
neural networks (ANN) for modeling of decolorization of textile dye
solution containing C.I. basic Yellow 28 by electrocoagulation process”,
J. of Hazardous Materials, 2006, vol. B 137, pp. 1788-1795.
[12] A. Amankwah, J. Kabuba, "Comparison of Neural Networks and
Kalman Filter for the modeling of Ion Exchange Process”, Life Sci J.,
2013, vol. 10, pp. 1012-1015.
[13] S.A. Abdulkareen, E. Muzenda, A.S. Afolabi, J. Kabuba, " Treatment of
clinoptilolite as an Adsorbent for the Removal of copper Ion from
Synthetic Wastewater solution”, Arab. J. Sci. Eng., 2013, vol. 38, pp.
2263-2272.
[14] B.B. Mamba, D.W. Nyembe and A.F. Mulaba-Bafubiandi, "The effect
of conditioning with NaCl, KCl and HCl on the performance of natural
clinoptilolite’s removal efficiency of Cu2+ and Co2+ Synthetic solution”,
Water SA., 2009, vol. 36, pp. 437-444.
[15] E. Oguz,A. Tortum, B. Keskinler. "Determination of the apparent rate
constants of the degradation of humic substances by ozonation and
modeling of the removal of humic substances from the aqueous
solutions with neural network”, J. of Hazardous Materials, 2008, vol.
157, pp. 455-463.
[16] F.L. Toma, S. Guessasma, D. Klein, G. Montavon, G. Bertrand,C.
Coddet,"Neural computation to predict TiO2photocatalytic efficiency for
nitrogen oxides removal. J. Photochem. Photobiol. A; Chem.2004, vol.
165, pp. 91-96.
[17] D.R. Baughman and Y.A. Liu, "Neural Networks in Bioprocessing and
Chemical Engineering”. Academic Press, San Diego, California, USA,
1995.
[18] M. Sadrzadeh, T. Mohammadi, J. Ivakpour, N. Kasiri, "Separation of
lead ions from wastewater using electrodialysis: Comparing
mathematical and neural network modeling”, Chemical Engineering
Journal, 2008, vol. 144, pp. 431-441.
[19] M. Horsfall, A.I. Spiff, "Effects of metals ion concentration on the
biosorption of Pb2+ and Cd2+ from aqueous solution by Caladium bicolor
(Wild Cocoyan)”, Afr. J. Biotechnol. 2005, vol. 4, pp. 191-196.
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:67949", author = "John Kabuba", title = "Application of Neural Network on the Loading of Copper onto Clinoptilolite", abstract = "The study investigated the implementation of the
Neural Network (NN) techniques for prediction of the loading of Cu
ions onto clinoptilolite. The experimental design using analysis of
variance (ANOVA) was chosen for testing the adequacy of the
Neural Network and for optimizing of the effective input parameters
(pH, temperature and initial concentration). Feed forward, multi-layer
perceptron (MLP) NN successfully tracked the non-linear behavior of
the adsorption process versus the input parameters with mean squared
error (MSE), correlation coefficient (R) and minimum squared error
(MSRE) of 0.102, 0.998 and 0.004 respectively. The results showed
that NN modeling techniques could effectively predict and simulate
the highly complex system and non-linear process such as ionexchange.
", keywords = "Clinoptilolite, loading, modeling, Neural network.", volume = "8", number = "8", pages = "840-4", }
