Kinetic Data Analysis by MLR and ANN Models for Phenol Attenuation in Peat Soil
Publication: International Journal of Geomechanics
Volume 17, Issue 6
Abstract
The efficacy of phenol transport from the aqueous phase to peat soil for assessment of the attenuation capacity for migratory phenol in subsurface water pollution was investigated by the application of multiple linear regression (MLR) and artificial neural network (ANN) models. The batch kinetics study was performed, which revealed that the Freundlich isotherm model fits reasonably well with experimental results. A maximum value of 43% phenol removal efficiency was achieved in 200 g/L of soil, an initial concentration of phenol of 10 mg/L, and an equilibration time of 6 h. A sum total of 270 laboratory batch adsorption tests were conducted, and the results were applied in MLR and ANN models. Some of the influencing factors, such as pH, initial concentration, mass of soil, contact time, and so forth, on removal of sorbate by peat were also investigated in the present research. The experimental results exhibit a reasonable goodness of fit [higher coefficient of determination, R2, and lower root-mean-square error (RMSE), and mean absolute performance error (MAPE)] of the previous models.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The authors are thankful to the Director, National Institute of Technology, Durgapur, West Bengal, India, for providing the necessary assistance for performing the present research.
References
Aghav, R. M., Kumar, S., and Mukherjee, S. N. (2011). “Artificial neural network modeling in competitive adsorption of phenol and resorcinol from water environment using some carbonaceous adsorbents.” J. Hazard. Mater., 188(1–3), 67–77.
Ahmad, A. A., Hameed, B. H., and Aziz, A. (2007). “Adsorption of direct dye on palm ash: Kinetic and equilibrium modeling.” J. Hazard. Mater., 141(1), 70–76.
BIS (Bureau of Indian Standards). (1972). “Methods of test for soils: Determination of organic matter.” IS 2720f-Part 22, New Delhi, India.
BIS (Bureau of Indian Standards). (1973). “Methods of test for soils: Determination of water content.” IS 2720b-Part 2, New Delhi, India.
BIS (Bureau of Indian Standards). (1980). “Test for soils: Determination of specific gravity: Fine, medium and coarse grained soils.” IS 2720c-Part III-Sec 2, New Delhi, India.
BIS (Bureau of Indian Standards). (1983). “Methods of test for soils: Preparation of dry soil samples for various tests.” IS 2720a-Part 1, New Delhi, India.
BIS (Bureau of Indian Standards). (1985a). “Method of test for soils: Determination of liquid and plastic limit.” IS 2720e-Part 5, New Delhi, India.
BIS (Bureau of Indian Standards). (1985b). “Methods of test for soils: Grain size analysis.” IS 2720d-Part 4, New Delhi, India.
BIS (Bureau of Indian Standards). (1987). “Method of test for soils: determination of pH value.” IS 2720g-Part 26, New Delhi, India.
Cabalar, A. F., Cevik, A., Gokceoglu, C., and Baykal, G. (2010). “Neuro-fuzzy based constitutive modeling of undrained response of Leighton Buzzard sand mixtures.” J. Expert Syst. Appl., 40(1), 14–33.
Castillo, P. A., et al. (2012). “Determining the significance and relative importance of parameters of a simulated quenching algorithm using statistical tools.” Appl. Intell., 37(2), 239–254.
Chu, K. H. (2003). “Prediction of two-metal biosorption equilibria using a neural network.” Eur. J. Miner. Process. Environ. Prot., 3(1), 119–127.
Din, A. T. M., Hameed, B. H., and Ahmad, A. L. (2009). “Batch adsorption of phenol onto physiochemical- activated coconut shell.” J. Hazard. Mater., 161(2–3), 1522–1529.
Djebbar, M., Djafri, F., Bouchekara, M., and Djafri, A. (2012). “Adsorption of phenol on natural clay.” Afr. J. Pure Appl. Chem., 6(2), 15–25.
Dong, Y., Scholz, M., and Harrington, R. (2012). “Statistical modeling of contaminants removal in mature integrated constructed wetland sediments.” J. Environ. Eng., 1009–1017.
Ekici, B. B., and Aksoy, U. T. (2009). “Prediction of building energy consumption by using artificial neural networks.” Adv. Eng. Software, 40(5), 356–362.
Faisal, A., and Ali, T. (2014). “Using granular dead anaerobic sludge as permeable reactive barrier for remediation of groundwater contaminated with phenol.” J. Environ. Eng., 04014072.
Hamdaoui, O., and Naffrechoux, E. (2007). “Modeling of adsorption isotherms of phenol and chlorophenols onto granular activated carbon: Part I. Two-parameter models and equations allowing determination of thermodynamic parameters.” J. Hazard. Mater., 147(1–2), 381–394.
Hanna, K., Lassabatere, L., and Bechet, B. (2012). “Transport of two naphthoic acids and salicylic acid in soil: Experimental study and empirical modeling.” Water Res., 46(14), 4457–4467.
Hassett, J. J., Means, J. C., Banwart, W. L., Wood, S. G., Ali, S., and Khan, A. (1980). “Sorption of dibenzothiophene by soils and sediments.” J. Environ. Qual., 9(2), 184–186.
Jalalifar, H., Mojedifar, S., Sahebi, A. A., and Nezamabadi-pour, H. (2011). “Application of the adaptive neuro-fuzzy inference system for prediction of a rock engineering classification system.” Comput. Geotech., 38(6), 783–790.
Jiang, X., Coles, A. C., and Asapo, E. S. (2008). “Review of pretreated peat applied in treating domestic wastewaters and oily waters.” Proc., GeoEdmonton, 2008, 668–674.
Johnson, R. A., and Whichern, D. W. (2008). Applied multivariate statistical analysis, 6th Ed., Pearson Education, New Delhi, India.
Khan, Z., and Anjaneyulu, Y. (2005). “Analysis of the distribution coefficients and mobility characteristics of phenols in different soil types.” Environ. Geol., 48(1), 1–5.
Kök, M. (2011). “Computational investigation of testing parameter effects on abrasive wear behaviour of Al2O3 particle-reinforced MMCS using statistical analysis.” Int. J. Adv. Manuf. Technol., 52(1–4), 207–215.
Kovoor, G., and Nandagiri, L. (2007). “Developing regression models for predicting pan evaporation from climatic data—A comparison of multiple least-squares, principal components, and partial least-squares approaches.” J. Irrig. Drain Eng., 444–454.
Kumar, K. V., and Porkodi, K. (2009). “Modelling the solid–liquid adsorption processes using artificial neural networks trained by pseudo second order kinetics.” Chem. Eng. J., 148(1), 20–25.
Mahvi, A. H., Maleki, A., and Eslami, A. (2004). “Potential of rice husk and rice husk ash for phenol removal in aqueous systems.” Am. J. Appl. Sci., 1(4), 321–326.
MATLAB [Computer software]. MathWorks, Natick, MA.
Mazumder, C., and Gupta, A. (2011). “Prediction of arsenic removal by electrocoagulation: Model development by factorial design.” J. Hazard. Toxic Radioact. Waste, 48–54.
Mortule, M. M., Abdalle, J., and Ghadban, A. (2010). “Modeling phosphorus removal process using artificial neural network.” Proc., BALWOIS-Ohrid, Republic of Macedonia.
Namasivayam, C., Radhika, R., and Suba, S. (2001). “Uptake of dyes by a promising locally available agricultural solid waste: Coir pith.” Waste Manage., 21(4), 381–387.
Nayak, P. S., and Singh, B. K. (2007). “Removal of phenol from aqueous solutions by sorption on low cost clay.” Desalination, 207(1–3), 71–79.
Pal, S., Adhikari, K., Ghosh, S., and Mukherjee, S. N. (2011). “Characterization of subsurface water near an industrial wastewater disposal site.” Int. J. Earth Sci. Eng., 4(6), 437–441.
Pal, S., Mukherjee, S. N., and Ghosh, S. (2013a). “Nonlinear kinetic analysis of phenol adsorption onto peat soil.” Environ. Earth Sci., 71(4), 1593–1603.
Pal, S., Mukherjee, S. N., and Ghosh, S. (2013b). “Optimum phenol sorption in peat by the response surface method.” Environ. Geotech., 1(3), 142–151.
Pal, S., Mukherjee, S. N., and Ghosh, S. (2014a). “Estimation of the phenolic waste attenuation capacity of some fine-grained soils with the help of ANN modeling.” Environ. Sci. Pollut. Res. Int., 21(5), 3524–3533.
Pal, S., Mukherjee, S. N., and Ghosh, S. (2014b). “Application of HYDRUS 1D model for assessment of phenol–soil adsorption dynamics.” Environ. Sci. Pollut. Res. Int., 21(7), 5249–5261.
Park, Y., Ayoko, G. A., Horvath, E., Kurdi, R., Kristof, J., and Frost, R. L. (2013). “Structural characterisation and environmental application of organoclays for the removal of phenolic compounds.” J. Colloid Interface Sci., 393, 319–334.
Pu, H., and Fox, P. (2015). “Model for coupled large strain consolidation and solute transport in layered soils.” Int. J. Geomech., 04015064.
Pu, H., Fox, P., and Shackelford, C. D. (2016). “Assessment of consolidation- induced contaminant transport for compacted clay liner systems.” J. Geotech. Geoenviron. Eng., 04015091.
Rice, E. W., Baird, R. B., Eaton, A. D., Clesceri, L. S., eds. (2005). Standard methods for the examination of water and wastewater, 21st Ed., American Public Health Association, Washington, DC.
Saha, P., Datta, S., and Sanyal, S. K. (2010). “Application of natural clay soil as adsorbent for the removal of copper from wastewater.” J. Environ. Eng., 1409–1417.
Snigdha, K. (2013). “Modeling phenol adsorption in water environment using artificial neural network.” Int. Res. J. Environ. Sci., 2(7), 39–43.
SPSS 10 [Computer software]. IBM Corporation, Armonk, NY.
Srivastava, V. C., Swamy, M. M., Mall, I. D., Prasad, B., and Mishra, I. M. (2006). “Adsorptive removal of phenol by bagasse fly ash and activated carbon: Equilibrium, kinetics and thermodynamics.” Colloids Surf., A, 272, 89–104.
Stipicevic, S., Fingler, S., and Drevankar, V. (2009). “Effect of organic and mineral soil fractions on sorption behaviour of chlorophenol and triazine micropollutants.” Arh. Hig. Rada. Toksikol., 60(1), 43–52.
Tahir, H. (2005). “Comparative trace metal contents in sediments and liquid wastes from tanneries and the removal of chromium using Zeolite 5A.” Electron. J. Agric. Food Chem., 4(4), 1021–1032.
Vázquez, I., Iglesias, J. R., Marañón, E., Castrillón, L., and Alvarez, M. (2007). “Removal of residual phenols from coke wastewater by adsorption.” J. Hazard. Mater., 147(1–2), 395–400.
Viraraghavan, T., and Maria Alfaro, F. (1998). “Adsorption of phenol from wastewater by peat, fly ash and bentonite.” J. Hazard. Mater., 57(1–3), 59–70.
Yetilmezsoy, K., and Demirel, S. (2008). “Artificial neural network (ANN) approach for modeling of Pb(II) adsorption from aqueous solution by Antep pistachio (Pistacia Vera L.) shells.” J. Hazard. Mater., 153(3), 1288–1300.
Yilmaz, I., and Kaynar, O. (2011). “Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils.” J. Expert Syst. Appl., 38(5), 5958–5966.
Yilmaz, I., and Yuksek, G. (2009). “Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, and ANFIS models.” Int. J. Rock Mech. Min. Sci., 46(4), 803–810.
Yu, J. Y., Shin, M. Y., Noh, J. H., and Seo, J. J. (2004). “Adsorption of phenol and chlorophenols on Ca-montmorillonite in aqueous solutions.” Geosci. J., 8(2), 185–189.
Information & Authors
Information
Published In
International Journal of Geomechanics
Volume 17 • Issue 6 • June 2017
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Jun 10, 2014
Accepted: Aug 26, 2016
Published online: Oct 21, 2016
Discussion open until: Mar 21, 2017
Published in print: Jun 1, 2017
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.