Indexing
All Issues
Submission
Author Guidelines
Reviewers
Editorial Members
Publication Fee
Vol.5 , No. 1, Publication Date: Feb. 27, 2018, Page: 1-13
 was investigated in this study using the plant material <i>Luffa cylindrica</i>. The applicability of some selected kinetic models was tested. Characterization like the surface area, chemical bonds, bulk density, Pore size distribution, microstructures, composition, morphology and elemental composition were determined. The coefficient of determination (R<sup>2</sup>) of all the models studied were mostly greater than 0.9. In most cases these coefficients were found to be close to one. This indicates that all the kinetic models adequately describe the experimental data of the biosorption of Zn (II) ions. Kinectic models were developed mathematically, and also, Artificial Neural Network (ANN) was applied to develop a Multiple Input Single Output (MISO) back propagation neural network model which was validated. The RMSE value was found to be 0.5912 and 1.6267 for MISO Zn-1 and MISO Zn-2 respectively. Artificial neural network was able to predict the sorption capacity quite reasonably for the model.&picture=http://www.aascit.org/aascit/decorators/img/journal/902.jpg)
| [1] | Oboh Innocent O., Department of Chemical and Petroleum Engineering, University of Uyo, Uyo, Nigeria. |
| [2] | Aluyor Emmanuel O., Department of Chemical Engineering, University of Benin, Benin City, Nigeria. |
Biosorption experiment for Zn (II) was investigated in this study using the plant material Luffa cylindrica. The applicability of some selected kinetic models was tested. Characterization like the surface area, chemical bonds, bulk density, Pore size distribution, microstructures, composition, morphology and elemental composition were determined. The coefficient of determination (R2) of all the models studied were mostly greater than 0.9. In most cases these coefficients were found to be close to one. This indicates that all the kinetic models adequately describe the experimental data of the biosorption of Zn (II) ions. Kinectic models were developed mathematically, and also, Artificial Neural Network (ANN) was applied to develop a Multiple Input Single Output (MISO) back propagation neural network model which was validated. The RMSE value was found to be 0.5912 and 1.6267 for MISO Zn-1 and MISO Zn-2 respectively. Artificial neural network was able to predict the sorption capacity quite reasonably for the model.
Keywords
Artificial Neural Network, Kinectic Model, Luffa cylindrica, Biosorption, Waste Water
Reference
| [01] | Gupta, N., Prasad, M., Singhal, N. and Kumar, V. (2009). Modeling the Adsorption Kinetics of Divalent Metal Ions onto Pyrophyllite Using the Integral Method. Ind. Eng. Chem. Res., 48 (4), 2125-2128. |
| [02] | Abdelnaeim, M. Y., El Sherif, I. Y., Attia, A. A., Fathy, N. A., El-Shahat. (2016). Impact of chemical activation on the adsorption performance of common reed towards Cu(II) and Cd(II). International Journal of Mineral Processing. 157: 80-88. |
| [03] | Rakesh N., Kalpana P., Naidu T. V. R and Venkateswara Rao M. (2010). Removal of Zinc Ions from Aqueous Solution by Ficus Benghalensis L.: Equilibrium and Kinetic Studies. International Journal of Engineering Studies. Volume 2, Number 1, pp. 15-28. |
| [04] | Luqman, C. A., Muhammad, Saidatul S. J. and Thomas S. Y. C. (2010). Modelling of Single and Binary Adsorptions of Heavy metals onto Activated carbon- Equilibrium studies. Pertanika Journal of Science and Technology 18 (1): 83-93. |
| [05] | Mohan, D., Singh, K. P. and Singh, V. K. (2005). Removal of hexavalent chromium from aqueous solution using low-cost activated carbons derived from agricultural waste materials and activated carbon fabric cloth, Ind. Eng. Chem. Res. 44, 1027-1042. |
| [06] | Babel, S. and Kurniawan, T. A. (2003). Low-cost adsorbents for heavy metals uptake from contaminated water: a review, J. Hazard. Mater. 97, 219-243. |
| [07] | Basil J. L., Ev R. R., Milcharek C. D., Martins L. C., Pavan F. A., dos Santos, Jr. A. A., Dias S. L. P., Dupont J., Noreña C. P. Z., and Lima E. C. (2006). Statistical Design of Experiments as a tool for optimizing the batch conditions to Cr(VI) biosorption on Araucaria angustifolia wastes. J Hazard Mater; 133: 143-153. |
| [08] | Lima, E. C., Royer, B., Vaghetti, J. C. P., Brasil, J. L., Simon, N. M., dos Santos Jr., A. A., Pavan, F. A., Dias, S. L. P., Benvenutti, E. V. and da Silva, E. A. (2007). Adsorption of Cu(II) on Araucaria angustifoliawastes: determination of the optimal conditions by statistic designof experiments, J. Hazard. Mater. 140, 211-220. |
| [09] | Veglio, F. and Beolchini, F. (1997). Removal of metals by biosorption: a review. Hydrometallurgy, 44, 301-316. |
| [10] | Volesky, B. (2003) Sorption and Biosorption. Montreal-St. Lambert, Quebec, Canada, BV Sorbex Inc., 316 p. ISBN 0-9732983-0-8. |
| [11] | Rowell R. M., James S. H. and Jeffrey S. R. (2002). Characterization and factors effecting fibre properties, In Frollini E, Leao, AL, Mattoso LHC, (ed.), Natural polymers and agrofibres based composites. Embrapa Instrumentacao Agropecuaria, san Carlos, Brazil pp. 115-134. |
| [12] | Mazali I. O. and Alves O. L. (2005). Morphosynthesis: high fidelity inorganic replica of the fibrous network of loofa sponge (Luffa cylindrica). Anais da Academia Brasileira de Ciências, Vol. 77, No. 1, p. 25-31. |
| [13] | Ho, Y. S., Porter, J. F. and McKay, G. (2002). Equilibrium isotherm studies for the sorption of divalent metal ions onto peat: copper, nickel and lead single component systems, Water Air Soil Pollution, 141, 1-31. |
| [14] | Parisi D. R. and Laborde M. A., (2001) Modeling Steady State Heterogeneous Gas-Solid Reactor Using Feed Forward Neural Networks. Computer and Chemical Engineering. (25): 1241-1250. |
| [15] | Adeyinka A, Liang H. and Tina G (2007). Removal of Metal Ion form Waste Water with Natural Waste. School of Engineering and Technology. 1-8 (33): 4. |
| [16] | Marshall, W. E. and Champagne, T. E. (1995). Agricultural Byproducts as Adsorbents for Metal ions in Laboratory Prepared Solutions and in Manufacturing Wastewater, Journal of Environmental Science and Health, Part A: Environmental Science and Engineering. Vol. 30, No. 2, 241-261. |
| [17] | Jackson, P. V., Hunt, J. A. and Doherty, P. J. (2004). Hydrophilicity of 3-D biomaterials: The Washburn equation. Journal of Materials in Medicine. 15: 507-511. |
| [18] | Coates, J. (2000). Interpretation of Infrared Spectra, A Practical Approach. Encyclopedia of Analytical Chemistry. Meyers, R. A. (Ed.) pp. 10815-10837. |
| [19] | Okaka, J. C. and Potter, N. N (1979): “Physicochemical and Functional properties of cowpea powders processed to reduce beany flavours”. Journal of Food Science. 44: 1235-1240. |
| [20] | Hamoudi, S. and Kaliaguine, S. (2003). Sulfonic acid-functionalized periodic mesoporous organosilica. Micropor. Mesopor. Mater. 59, p. 195-204. |
| [21] | Passos C. G., Ribaski F., Simon N. M., dos Santos Jr. A. A., Vaghetti J. C. P. (2006). BenvenuttiE. V. and Lima, E. C. Use of statistical design of experiments to evaluate the sorption capacity of 7-amine-4-azahepthylsilica and 10-amine- 4-azadecylsilica for Cu(II), Pb(II) and Fe(III) adsorption. J Colloid Interface Sci; 302: 396-407. |
| [22] | Vaghetti J. C. P., Zat M., Bentes K. R. S., Ferreira L. S., Benvenutti E. V. and Lima E. C. (2003). 4-Phenylenediaminepropylsilica xerogel as a sorbent for copper determination in waters by slurry-sampling ETAAS. J Anal Atom Spectrom; 18: 376-380. |
| [23] | Arenas L. T., Vaghetti J. C. P., Moro C. C., Lima E. C., Benvenutti E. V. and Costa T. M. H. (2004). Dabco/silica sol-gel hybrid material. The influence of the morphology on the CdCl2 adsorption capacity. Mater Lett; 58: 895-898. |
| [24] | Passos, C. G. Lima, E. C. Arenas, L. T. Simon, N. M. da Cunha, B. M. Brasil, J. L. Costa, T. M. H. and Benvenutti, E. V. (2008). Use of 7-amine-4-azahepthylsilica and 10-amine-4-azadecylsilica xerogels as adsorbent for Pb(II). Kinetic and equilibrium study, Colloids Surfaces. A 316; 297-306. |
| [25] | Pavia, D. L., Lampman, G. M. and Kriz, G. S. (1996). Introduction to Spectroscopy, 2nd edn., Saunders Golden Sunburst Series, New York. |
| [26] | Largegren, S. (1898) About the theory of so-called adsorption of soluble substances, Kungliga Suensk Vetenskapsakademiens Handlingar 241: 1-39. |
| [27] | Ho, Y. S. and Mckay, G. M. (1999). Pseudo-second order model for sorption process, Proc. Biochem. 34: 451-465. |
| [28] | Ayoob, S., Gupta, A. K., Bhakat, P. B. and Bhat, V. T. (2008). Investigations on the kinetics and mechanisms of sorptive removal of fluoride from water using alumina cement granules, Chemical Engineering Journal. 140: 6-14. |
| [29] | Weber Jr., W. J. and Morris, J. C. (1963). Kinetics of adsorption on carbon from solution, J. Sanit. Eng. Div. Am. Soc. Civil Eng. 89: 31-59. |
| [30] | Lopes, E. C. N., dos Anjos, F. S. C., Vieira, E. F. S. and Cestari, A. R. (2003). An alternative Avrami equation to evaluate kinetic parameters of the interaction of Hg(II) with thin chitosan membranes, J. Colloid Interface Sci. 263: 542-547. |
| [31] | Jacques, R. A. Bernardi, R. Caovila, M. Lima, E. C. Pavan, F. A. Vaghetti, J. C. P. and Airoldi, C. (2007). Removal of Cu (II), Fe(III) and Cr(III) from aqueous solution by aniline grafted silica gel, Separation Science and Technology. 42; 591-609. |
| [32] | Ho, Y. S. and G. Mckay (2000) The Kinetics of Sorption of Divalent Metal Ions onto Sphagnum Moss Peat, Wat. Res. 34 (3), 735-742. |
| [33] | Naja, G. M., Murphy, V. and Volesky B (2009). Biosorption, metals. Encyclopedia of Industrial Biotechnology (accepted, Dec 2007, in press). |
| [34] | Carstensen, J., Rauch, W. and Vanrolleghem, P. (1998). Modeling terminology and methodology for activated sludge modelling. Workshop at the Kollekolle conference 1998. |
| [35] | Pythia (2000). The Neural Network Designer manual by Runtime Software, 1-29. |
| [36] | Marengo E, Gianotti V, Angioi S. and Gennaro M. C (2004). Optimization by experimental design and artificial neural networks of the ion – interaction reversed-phase liquid chromatographic separation of twenty cosmetic preservatives. Journal of Chromatography A (1029): 57-65. |
| [37] | Alireza, K. and Ali, K. (2009). Modeling of Nitrate Adsorption on Granular Activated Carbon (GAC) using Artificial Neural Network (ANN) International Journal of Chemical Reactor Engineering Vol. 7, Article A5. |
| [38] | Goutam, D., Pankaj, J., Arnab, K. L. and Neeraj, M. (2006). Artificial Neural Network Models for Forecasting Stock Price Index in the Bombay Stock Exchange. Journal of Emerging Market Finance, Vol. 5, No. 3 283-295. |
| [39] | Khaing W. M. and Thinn T. N. (2008). Optimum Neural Network Architecturefor Precipitation Prediction of Myanmar. World Academy of Science, Engineering and Technology. 48, pp 130-134. |
| [40] | Najafi, G., Ghobadian, B., Yusaf, T. F. and Rahimi, H. (2007). Combustion Analysis of a CI Engine Performance Using Waste Cooking Biodiesel Fuel with an Artificial Neural Network Aid. American Journal of Applied Sciences 4 (10): 756-764. |
