Prediction of Phenolic Compound Migration Process through Soil Media using Artificial Neural Network Approach
This study presents the application of artificial
neural network for modeling the phenolic compound
migration through vertical soil column. A three layered feed
forward neural network with back propagation training
algorithm was developed using forty eight experimental data
sets obtained from laboratory fixed bed vertical column tests.
The input parameters used in the model were the influent
concentration of phenol(mg/L) on the top end of the soil
column, depth of the soil column (cm), elapsed time after
phenol injection (hr), percentage of clay (%), percentage of
silt (%) in soils. The output of the ANN was the effluent
phenol concentration (mg/L) from the bottom end of the soil
columns. The ANN predicted results were compared with the
experimental results of the laboratory tests and the accuracy of
the ANN model was evaluated.
[1] F.Gonen and Z.Aksu,"A comparative adsorption/biosorption of phenol
to granular activated carbon and immobilized activated sludge in a
continuous packed bed reactor"; Chemical Engineering Communication;
190, (2003), 763-778.
[2] A.T.M.Din, B.H. Hameed and A.L. Ahmad, "Batch adsorption of phenol
onto physiochemical- activated coconut shell", Journal of Hazardous
Materials; 161(2-3) (2009), 1522-1529.
[3] P.P. Mitra, and T.K. Pal, "Treatment of effluent containing phenol and
catalytical conversion", Int. Chem. Eng., 1994, 41 (1), 26-30.
[4] T. Viraraghavan and J. Alfaro,"Removal of phenol from wastewater by
adsorption on peat, fly ash and bentonite", J. Environ. Eng, 1992, 5:
311- 317.
[5] M.R. Taha, T.O. Leng, A.B. Mohamad and A.A.H. Kadhum, "Batch
adsorption tests of phenol in soils", Bull Eng Geol Env, 2003, 62:251-
257
[6] G. Annadurai and J.F. Lee,"Application of artificial neural network
model for the development of optimized complex medium for phenol
degradation using seudomonas pictorum (NICM 2074)",
Biodegradation, 2007, 18:383-392.
[7] K.Yetilmezsoy and S. Demirel, "Artificial neural network (ANN)
approach for modeling of Pb (II) adsorption from aqueous solution by
Antep pistachio (Pistacia Vera L.) shells", Journal of Hazardous
Materials, 2008, 153:1288-1300.
[8] I.A. Basheer and Y.M. Najjar,"Predicting dynamic response of
adsorption columns with neural nets", Journal of Computing in Civil
Engineering, ASCE, Vol.10, No.1, January,1996.
[9] O. Gunadym, "Estimation of soil compaction parameters by using
statistical analyses and artificial neural networks", Environ Geol, 2009,
57:203-215.
[10] H.R. Maier and G.C. Dandy, "Neural Networks for the prediction and
forecasting of water resources variables. A review of modeling issues
and applications", Environmental Modeling & Software, 2000, 15, 101-
124.
[11] T. Masters,"Practical neural network recipes in C++", Academic Pres,
1993, San Diego, California.
[12] G. N. Smith,"Probability and statistics in civil engineering: An
introduction", Collins,1986, London.
[1] F.Gonen and Z.Aksu,"A comparative adsorption/biosorption of phenol
to granular activated carbon and immobilized activated sludge in a
continuous packed bed reactor"; Chemical Engineering Communication;
190, (2003), 763-778.
[2] A.T.M.Din, B.H. Hameed and A.L. Ahmad, "Batch adsorption of phenol
onto physiochemical- activated coconut shell", Journal of Hazardous
Materials; 161(2-3) (2009), 1522-1529.
[3] P.P. Mitra, and T.K. Pal, "Treatment of effluent containing phenol and
catalytical conversion", Int. Chem. Eng., 1994, 41 (1), 26-30.
[4] T. Viraraghavan and J. Alfaro,"Removal of phenol from wastewater by
adsorption on peat, fly ash and bentonite", J. Environ. Eng, 1992, 5:
311- 317.
[5] M.R. Taha, T.O. Leng, A.B. Mohamad and A.A.H. Kadhum, "Batch
adsorption tests of phenol in soils", Bull Eng Geol Env, 2003, 62:251-
257
[6] G. Annadurai and J.F. Lee,"Application of artificial neural network
model for the development of optimized complex medium for phenol
degradation using seudomonas pictorum (NICM 2074)",
Biodegradation, 2007, 18:383-392.
[7] K.Yetilmezsoy and S. Demirel, "Artificial neural network (ANN)
approach for modeling of Pb (II) adsorption from aqueous solution by
Antep pistachio (Pistacia Vera L.) shells", Journal of Hazardous
Materials, 2008, 153:1288-1300.
[8] I.A. Basheer and Y.M. Najjar,"Predicting dynamic response of
adsorption columns with neural nets", Journal of Computing in Civil
Engineering, ASCE, Vol.10, No.1, January,1996.
[9] O. Gunadym, "Estimation of soil compaction parameters by using
statistical analyses and artificial neural networks", Environ Geol, 2009,
57:203-215.
[10] H.R. Maier and G.C. Dandy, "Neural Networks for the prediction and
forecasting of water resources variables. A review of modeling issues
and applications", Environmental Modeling & Software, 2000, 15, 101-
124.
[11] T. Masters,"Practical neural network recipes in C++", Academic Pres,
1993, San Diego, California.
[12] G. N. Smith,"Probability and statistics in civil engineering: An
introduction", Collins,1986, London.
@article{"International Journal of Earth, Energy and Environmental Sciences:52318", author = "Supriya Pal and Kalyan Adhikari and Somnath Mukherjee and Sudipta Ghosh", title = "Prediction of Phenolic Compound Migration Process through Soil Media using Artificial Neural Network Approach", abstract = "This study presents the application of artificial
neural network for modeling the phenolic compound
migration through vertical soil column. A three layered feed
forward neural network with back propagation training
algorithm was developed using forty eight experimental data
sets obtained from laboratory fixed bed vertical column tests.
The input parameters used in the model were the influent
concentration of phenol(mg/L) on the top end of the soil
column, depth of the soil column (cm), elapsed time after
phenol injection (hr), percentage of clay (%), percentage of
silt (%) in soils. The output of the ANN was the effluent
phenol concentration (mg/L) from the bottom end of the soil
columns. The ANN predicted results were compared with the
experimental results of the laboratory tests and the accuracy of
the ANN model was evaluated.", keywords = "Modeling, Neural Networks, Phenol, Soil media", volume = "5", number = "3", pages = "122-4", }