Modeling of methane oxidation in landfill cover soil using an artificial neural network

References

• Abushammala , M.F.M. , Basri , N.E.A. , Basri , H. , Kadhum , A.A.H. and El-Shafie , A.H. 2012 . Methane and carbon dioxide emissions from Sungai Sedu open dumping during wet season in Malaysia . Ecol. Eng , 49 : 254 – 263 . doi: 10.1016/j.ecoleng.2012.08.013
   Web of Science ®Google Scholar
• Abushammala , M.F.M. , Basri , N.E.A. , Basri , H. , Kadhum , A.A.H. and El-Shafie , A.H. 2013 . Empirical gas emission and oxidation measurement at cover soil of dumping site: Example from Malaysia . Environ. Monit. Assess , 185 : 4919 – 4932 . doi: 10.1007/s10661-012-2913-5
   PubMed Web of Science ®Google Scholar
• Abrahart , R.J. 2004 . “ Neural networks for hydrological modeling ” . In Neural Network Modelling , Edited by: Abrahart , R.J. , Kneale , P.E. and See , L.M. 15 – 37 . London : Taylor & Francis .
   Google Scholar
• Albanna , M. , Fernandes , L. and Warith , M. 2007 . Methane oxidation in landfill cover soil; the combined effects of moisture content, nutrient addition, and cover thickness . J. Environ. Eng. Sci , 6 : 191 – 200 . doi: 10.1139/s06-047
   Web of Science ®Google Scholar
• Al-Quraishi , A.A. and Shokir , E.M. 2011 . Artificial neural networks modeling for hydrocarbon gas viscosity and density estimation . J. King Saud Univ. Eng. Sci , 23 : 123 – 129 . doi: 10.1016/j.jksues.2011.03.004
   Google Scholar
• Bishop , C. 1995 . Neural Networks for Pattern Recognition , New York : Oxford University Press .
   Google Scholar
• Boeckx , P. , Cleemput , O.V. and Villaralvo , I. 1996 . Methane emission from a landfill and the methane oxidizing capacity of its covering soil . Soil Biol. Biochem , 28 : 1397 – 1405 . doi: 10.1016/S0038-0717(96)00147-2
   Web of Science ®Google Scholar
• Borjesson , G. , Chanton , J. and Svensson , B.H. 2001 . Methane oxidation in two Swedish landfill covers measured with carbon-13 to carbon-12 isotope ratios . J. Environ. Qual , 30 : 369 – 376 . doi: 10.2134/jeq2001.302369x
   PubMed Web of Science ®Google Scholar
• De Visscher , A. and Cleemput , O.V. 2003 . Simulation model for gas diffusion and methane oxidation in landfill cover soils . Waste Manage , 23 : 581 – 591 . doi: 10.1016/S0956-053X(03)00096-5
   PubMed Web of Science ®Google Scholar
• Elmolla , E.S. , Chaudhuri , M. and Eltoukhy , M.M. 2010 . The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process . J. Hazard. Mater , 179 : 127 – 134 . doi: 10.1016/j.jhazmat.2010.02.068
   PubMed Web of Science ®Google Scholar
• Hamed , M.M. , Khalafallah , M.G. and Hassanien , E.A. 2004 . Prediction of wastewater treatment plant performance using artificial neural networks . Environ. Modell. Softw , 19 : 919 – 928 . doi: 10.1016/j.envsoft.2003.10.005
   Web of Science ®Google Scholar
• Head , K.H. 1992 . Manual of Soil Laboratory Testing , London : Pentech Press .
   Google Scholar
• Huber-Humer , M. 2004 . International research into landfill gas emissions and mitigation strategies-IWWG working group “CLEAR” . Waste Manage , 24 : 425 – 427 . doi: 10.1016/j.wasman.2004.02.005
   PubMed Web of Science ®Google Scholar
• Huber-Humer , M. , Gebert , J. and Hilger , H. 2008 . Biotic systems to mitigate landfill methane emissions . Waste Manage. Res , 26 : 33 – 46 . doi: 10.1177/0734242X07087977
   PubMed Web of Science ®Google Scholar
• Jugnia , L.-B. , Cabral , A.R. and Greer , C.W. 2008 . Biotic methane oxidation within an instrumented experimental landfill cover . Ecol. Eng , 33 : 102 – 109 . doi: 10.1016/j.ecoleng.2008.02.003
   Web of Science ®Google Scholar
• Karacan , C.O. 2008 . Modeling and prediction of ventilation methane emissions of U.S. longwall mines using supervised artificial neural networks . Int. J. Coal Geol , 73 : 371 – 387 . doi: 10.1016/j.coal.2007.09.003
   Web of Science ®Google Scholar
• Kardam , A. , Raj , K.R. , Arora , J.K. , Srivastava , M.M. and Srivastava , S. 2010 . Artificial neural network modeling for sorption of cadmium from aqueous system by shelled Moringa oleifera seed powder as an agricultural waste . J. Water Res. Protect , 2 : 339 – 344 . doi: 10.4236/jwarp.2010.24039
   Google Scholar
• Kiese , R. and Butterbach-Bahl , K. 2002 . N2O and CO2 emissions from three different tropic forest sites in the wet tropics of Queensland, Australia . Soil Biol. Biochem , 34 : 975 – 987 . doi: 10.1016/S0038-0717(02)00031-7
   Web of Science ®Google Scholar
• Li , H. , Sanchez , R. , Joe Qin , S. , Kavak , H.I. , Webster , I.A. , Tsotsis , T.T. and Sahimi , M. 2011 . Computer simulation of gas generation and transport in landfills. V: Use of artificial neural network and the genetic algorithm for short- and long-term forecasting and planning . Chem. Eng. Sci , 66 : 2646 – 2659 . doi: 10.1016/j.ces.2011.03.013
   Web of Science ®Google Scholar
• Mjalli , F.S. , Al-Asheh , S. and Alfadala , H.E. 2007 . Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance . J. Environ. Manage , 83 : 329 – 338 . doi: 10.1016/j.jenvman.2006.03.004
   PubMed Web of Science ®Google Scholar
• Nasr , M.S. , Moustafa , M.A.E. , Seif , H.A.E. and El Kobrosy , G. 2012 . Application of Artificial Neural Network (ANN) for the prediction of EL-AGAMY wastewater treatment plant performance-EGYPT . Alexandria Eng. J , 51 : 37 – 43 . doi: 10.1016/j.aej.2012.07.005
   Google Scholar
• Oguz , E. , Tortum , A. and Keskinler , B. 2008 . 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. Hazard. Mater , 157 : 455 – 463 . doi: 10.1016/j.jhazmat.2008.01.018
   PubMed Web of Science ®Google Scholar
• Onkal-Engin , G. , Demir , I. and Engin , S.N. 2005 . Determination of the relationship between sewage odour and BOD by neural networks . Environ. Modell. Softw , 20 : 843 – 850 . doi: 10.1016/j.envsoft.2004.04.012
   Web of Science ®Google Scholar
• Oonk , H. 2010 . “ The Netherlands: Oonkay! ” . In Literature Review: Methane from Landfills, Methods to Quantify Generation, Oxidation and Emission. Final Report Apeldoorn
   Google Scholar
• Ozkaya , B. , Demir , A. and Bilgili , M.S. 2007 . Neural network prediction model for the methane fraction in biogas from field-scale landfill bioreactors . Environ. Modell. Softw , 22 : 815 – 822 . doi: 10.1016/j.envsoft.2006.03.004
   Web of Science ®Google Scholar
• Palani , S. , Liong , S.-Y. and Tkalich , P. 2008 . An ANN application for water quality forecasting . Mar. Pollut. Bull , 56 : 1586 – 1597 . doi: 10.1016/j.marpolbul.2008.05.021
   PubMed Web of Science ®Google Scholar
• Perera , L.A.K. , Achari , G. and Hettiaratchi , J.P.A. 2002 . Determination of source strength of landfill gas: A numerical modeling approach . J. Environ. Eng , 128 : 461 – 471 . doi: 10.1061/(ASCE)0733-9372
   Web of Science ®Google Scholar
• Rene , E.R. and Saidutta , M.B. 2008 . Prediction of BOD and COD of a refinery wastewater using multilayer artificial neural networks . J. Urban Environ. Eng , 2 : 1 – 7 . doi: 10.4090/juee.2008.v2n1.001007
   Google Scholar
• Salari , D. , Daneshvar , N. , Aghazadeh , F. and Khataee , A.R. 2005 . Application of artificial neural networks for modeling of the treatment of wastewater contaminated with methyl tert-butyl ether (MTBE) by UV/H2O2 process . J. Hazard. Mater , 125 : 205 – 210 . doi: 10.1016/j.jhazmat.2005.05.030
   PubMed Web of Science ®Google Scholar
• Singh , G. and Panda , R.K. 2011 . Daily sediment yield modeling with artificial neural network using 10-fold cross validation method: A small agricultural watershed, Kapgari, India . Int. J. Earth Sci. Eng , 4 : 443 – 450 .
   Google Scholar
• Spokas , K.A. and Bogner , J.E. 2011 . Limits and dynamics of methane oxidation in landfill cover soils . Waste Manage , 31 : 823 – 832 . doi: 10.1016/j.wasman.2009.12.018
   PubMed Web of Science ®Google Scholar
• Stein , V.B. , Hettiaratchi , J.P.A. and Achari , G. 2001 . Numerical model for biological oxidation and migration of methane in soils . Pract. Period. Hazard. Toxic Radioact. Waste Manage , 5 : 225 – 234 . doi: 10.1061/(ASCE)1090-025X
   Google Scholar
• Stern , J.C. , Chanton , J. , Abichou , T. , Powelson , D. , Yuan , L. , Escoriza , S. and Bogner , J. 2007 . Use of biologically active cover to reduce landfill methane emissions and enhance methane oxidation . Waste Manage , 27 : 1248 – 1258 . doi: 10.1016/j.wasman.2006.07.018
   PubMed Web of Science ®Google Scholar
• Tashaouie , H.R. , Gholikandi , G.B. and Hazrati , H. 2012 . Artificial neural network modeling for predict performance of pressure filters in a water treatment plant . Desalinat. Water Treat , 39 : 192 – 198 . doi: 10.1080/19443994.2012.669175
   Web of Science ®Google Scholar
• Wilshusen , J.H. , Hettiaratchi , J.P.A. , Visscher , A.D. and Saint-Fort , R. 2004 . Methane oxidation and formation of EPS in compost: Effect of oxygen concentration . Environ. Pollut , 129 : 305 – 314 . doi: 10.1016/j.envpol.2003.10.015
   PubMed Web of Science ®Google Scholar
• Zhao , Z. , Chow , T.L. , Rees , H.W. , Yang , Q. , Xing , Z. and Meng , F.-R. 2009 . Predict soil texture distributions using an artificial neural network model . Comput. Electron. Agric , 65 : 36 – 48 . doi: 10.1016/j.compag.2008.07.008
   Web of Science ®Google Scholar
• Zhou , M. 2003 . “ Neural Network Techniques and Their Engineering Applications ” . In Neural Network Techniques and Their Engineering Applications , Edited by: Leondes , C.T. 1 – 19 . London : CRC Press .
   Google Scholar
• Zilouchian , A. 2001 . “ Fundamentals of Neural Networks ” . In Fundamentals of Neural Networks , Edited by: Zilouchian , A. and Jamshidi , M. Boca Raton, FL : CRC Press .
   Google Scholar