A Critics Study of Neural Networks Applied to ion-Exchange Process
This paper presents a critical study about the
application of Neural Networks to ion-exchange process. Ionexchange
is a complex non-linear process involving many factors
influencing the ions uptake mechanisms from the pregnant solution.
The following step includes the elution. Published data presents
empirical isotherm equations with definite shortcomings resulting in
unreliable predictions. Although Neural Network simulation
technique encounters a number of disadvantages including its “black
box", and a limited ability to explicitly identify possible causal
relationships, it has the advantage to implicitly handle complex
nonlinear relationships between dependent and independent
variables. In the present paper, the Neural Network model based on
the back-propagation algorithm Levenberg-Marquardt was developed
using a three layer approach with a tangent sigmoid transfer function
(tansig) at hidden layer with 11 neurons and linear transfer function
(purelin) at out layer. The above mentioned approach has been used
to test the effectiveness in simulating ion exchange processes. The
modeling results showed that there is an excellent agreement between
the experimental data and the predicted values of copper ions
removed from aqueous solutions.
[1] J. Warchol, R. Petrus, "modeling of heavy metal removal dynamics in
clinoptilolite packed beds," Microporous and Mesoporous Materials.
vol. 93, 2006, pp. 29-39.
[2] B. B. Mamba, D.W. Nyembe and A.F. Mulaba-Bafubiandi. "Removal
of copper and cobalt from aqueous solutions using natural clinoptilolite"
Water SA, vol. 3-35, 2009, pp. 307-314.
[3] S. Kesraoui-Ouki, C.R. Cheeseman, R. Perry, "Natural Zeolite
utilization in pollution control: A review of applications to metals
effluents," J. Chem.Tech. Biotechnol. vol. 59, 1994, pp.121-126.
[4] P. Kittisupakom, P. Tangteerasunun and P. Thitiyasook, "Dynamic
Neural Network Modeling for Hydrochloric Acid Recovery Process",
Korean J. Chem. Eng. vol. 22(6), 2005, pp. 813-821.
[5] M. M. Van den Bosch. "Simulation of ion exchange processes using
neuro-fuzzy reasoning". Cape Peninsula University of Technology,
South Africa, Theses & Dissertation. 2009
[6] S. Haykin. "A Comprehensive Foundation, Prentice Hall". 1999.
[7] X. Du, J. Yuan, Y. Zhao, Y. Li. "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. vol. A 23, 2007, pp.165-174.
[8] W. Gao and S. Engell. "Estimation of general nonlinear adsorption
isotherms from chromatograms". Comput. Chem.Eng. vol. 29, pp. 2242-
2255.
[9] J. S. J. Deventer van, S. P. Liebenbert, L. Lorenzen. "Dynamic modeling
of competitive elution of activated carbo in columns using neural
networks." Miner. Eng., vol. 8, 1995, pp. 1489-1501.
[1] J. Warchol, R. Petrus, "modeling of heavy metal removal dynamics in
clinoptilolite packed beds," Microporous and Mesoporous Materials.
vol. 93, 2006, pp. 29-39.
[2] B. B. Mamba, D.W. Nyembe and A.F. Mulaba-Bafubiandi. "Removal
of copper and cobalt from aqueous solutions using natural clinoptilolite"
Water SA, vol. 3-35, 2009, pp. 307-314.
[3] S. Kesraoui-Ouki, C.R. Cheeseman, R. Perry, "Natural Zeolite
utilization in pollution control: A review of applications to metals
effluents," J. Chem.Tech. Biotechnol. vol. 59, 1994, pp.121-126.
[4] P. Kittisupakom, P. Tangteerasunun and P. Thitiyasook, "Dynamic
Neural Network Modeling for Hydrochloric Acid Recovery Process",
Korean J. Chem. Eng. vol. 22(6), 2005, pp. 813-821.
[5] M. M. Van den Bosch. "Simulation of ion exchange processes using
neuro-fuzzy reasoning". Cape Peninsula University of Technology,
South Africa, Theses & Dissertation. 2009
[6] S. Haykin. "A Comprehensive Foundation, Prentice Hall". 1999.
[7] X. Du, J. Yuan, Y. Zhao, Y. Li. "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. vol. A 23, 2007, pp.165-174.
[8] W. Gao and S. Engell. "Estimation of general nonlinear adsorption
isotherms from chromatograms". Comput. Chem.Eng. vol. 29, pp. 2242-
2255.
[9] J. S. J. Deventer van, S. P. Liebenbert, L. Lorenzen. "Dynamic modeling
of competitive elution of activated carbo in columns using neural
networks." Miner. Eng., vol. 8, 1995, pp. 1489-1501.
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:55059", author = "John Kabuba and Antoine Mulaba-Bafubiandi and Kim Battle", title = "A Critics Study of Neural Networks Applied to ion-Exchange Process", abstract = "This paper presents a critical study about the
application of Neural Networks to ion-exchange process. Ionexchange
is a complex non-linear process involving many factors
influencing the ions uptake mechanisms from the pregnant solution.
The following step includes the elution. Published data presents
empirical isotherm equations with definite shortcomings resulting in
unreliable predictions. Although Neural Network simulation
technique encounters a number of disadvantages including its “black
box", and a limited ability to explicitly identify possible causal
relationships, it has the advantage to implicitly handle complex
nonlinear relationships between dependent and independent
variables. In the present paper, the Neural Network model based on
the back-propagation algorithm Levenberg-Marquardt was developed
using a three layer approach with a tangent sigmoid transfer function
(tansig) at hidden layer with 11 neurons and linear transfer function
(purelin) at out layer. The above mentioned approach has been used
to test the effectiveness in simulating ion exchange processes. The
modeling results showed that there is an excellent agreement between
the experimental data and the predicted values of copper ions
removed from aqueous solutions.", keywords = "Copper, ion-exchange process, neural networks,
simulation", volume = "6", number = "8", pages = "727-4", }