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Artificial neural network (ANN) modeling of COD reduction from landfill leachate by the ultrasonic process

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In the study, the use of an artificial neural network (ANN) has been applied for the prediction of COD removal from landfill leachate by the ultrasonic process. The configuration of the backpropagation neural network giving the lowest mean square error (MSE) was a three-layer ANN with a tangent sigmoid transfer function (tansig) at a hidden layer with 14 neurons, linear transfer function (purelin) at the output layer and the Levenberg–Marquardt backpropagation training algorithm (LMA). The ANN predicted results are very close to the experimental data with the correlation coefficient (R2) of 0.992 and the MSE of 0.000331. The sensitivity analysis showed that all studied variables (contact time, pH, ultrasound frequency and power) have strong effect on COD removal. In addition, ultrasound power is the most influential parameter with relative importance of 25.8%. The results showed that modeling neural network could effectively predict COD removal from landfill leachate by ultrasonic process.
Rocznik
Strony
59--73
Opis fizyczny
Bibliogr. 25 poz., tab., rys.
Twórcy
autor
  • Vice-Chancellery for Food and Drug, Shahroud University of Medical Sciences, Shahroud, Iran
autor
  • Department of Environmental Health Engineering, School of Public Health, Shahroud University of Medical Sciences, Shahroud, Iran
autor
  • Center for Health-Related Social and Behavioral Sciences Research, Shahroud University of Medical Sciences, Shahroud, Iran
Bibliografia
  • [1] MAHVI A., ROUDBARI A., NABIZADEH R., NASERI S., DEHGHANI M., ALIMOHAMMADI M., Improvement of landfill leachate biodegradability with ultrasonic process, Eur. J. Chem., 2012, 9, 766.
  • [2] MAHVI A., ROUDBARI A., Survey on the effect of landfill leachate of Shahroud city of Iran on ground water Quality, J. Appl. Technol. Environ. San., 2011, 1, 17.
  • [3] RENOU S., GIVAUDAN J., POULAIN S., DIRASSOUYAN F., MOULIN P., Landfill leachate treatment: review and opportunity, J. Hazard. Mater., 2008, 150, 468.
  • [4] RAJAN G., NALLADURAI D., PUTHIVA N., SREEKRISHNAPERUMAL R., SUBRAMANIAM K., Use of combined coagulation process as pretreatment of landfill leachate, Ir. J. Environ. Health Sci. Eng., 2013, 10, 24.
  • [5] SAMADI T., ESFAHANI Z., NADDAFI K., Comparison of the efficacy of Fenton and nZVI + H2O2 processes in municipal solid waste landfill leachate treatment. Ccase study: Hamadan landfill leachate, Int. J. Environ. Res., 2013, 7, 187.
  • [6] ABDOLI A., KARBASSI R., SAMIEE-ZAFARGHANDI R., RASHIDI Z., GITIPOUR S., PAZOKI M., Electricity generation from leachate treatment plant, Int. J. Environ. Res., 2012, 7, 493.
  • [7] ZHA G., ZHANG X., XU C., Treatment of landfill leachate by sonolysis followed by fenton process, Desalination Water. Treat., 2013, 1.
  • [8] ALEBOYEH A., KASIRI B., OLYA E., ALEBOYEH H., Prediction of azo dye decolorization by UV/H2O2 using artificial neural networks, Dyes Pigments, 2008, 77, 288.
  • [9] YETILMEZSOY K., DEMIREL S., Artificial neural network (ANN) approach for modeling of Pb(II) adsorption from aqueous solution by Antep pistachio (PistaciaVera L.) shells, J. Hazard. Mater., 2008, 153, 1288.
  • [10] OGUZA E., TORTUM A., KESKINLER B., 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., 2008, 157, 455.
  • [11] FULAZZAKY A., Measurement of biochemical oxygen demand of the leachates, Environ. Monit. Assess., 2013, 185, 4721.
  • [12] GUIMARAES C., FILHO R., SIQUEIRA F., FILHO I., SILVA B., Optimization of the AZO dyes decoloration process through neural networks: determination of the H2O2 addition critical point, Chem. Eng. J., 2008, 141, 35.
  • [13] ELMOLLAA S., CHAUDHURIA M., ELTOUKHY M., The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process, J. Hazard. Mater., 2010, 179, 127.
  • [14] SINGH P., BASANT A., MALIK A., JAIN G., Artificial neural network modeling of the river water quality. A case study, Ecol. Model., 2009, 220, 888.
  • [15] MJALLI S., AL-ASHEH S., ALFADALA E., Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance, J. Environ. Manage., 2007, 83, 329.
  • [16] HERNANDEZ RAMIREZ A., HERRERA-LÓPEZ J., RIVERA L., REAL-OLVERA D., Artificial neural network modeling of slaughterhouse wastewater removal of COD and TSS by electrocoagulation, Stud. Fuzz. Soft. Comp., 2014, 312, 273.
  • [17] VYAS M., MODHERAB B., SHARMA K., Artificial neural network based model in effluent treatment process, Int. J. Adv. Engine. Technol., 2011, 2, 271.
  • [18] TURAN G., MESCI B., OZGONENEL O., The use of artificial neural networks (ANN) for modeling of adsorption of Cu (II) from industrial leachate by pumice, Chem. Eng. J., 2011, 171, 1091.
  • [19] WANG L., WU H., WANG S., LI F., TAO J., Removal of organic matter and ammonia nitrogen from landfill leachate by ultrasound, Ultrason. Sonochem., 2008, 15, 933.
  • [20] GIROTO A., GUARDANI R., TEIXEIRA C., NASCIMENTO O., Study on the photo-Fenton degradation of polyvinyl alcohol in aqueous solution, Chem. Eng. Proc., 2006, 45, 523.
  • [21] LEGUBE B., VEL LEITNER K., Heterogeneous photocatalytic treatment of pharmaceutical micropollutants: effects of wastewater effluent matrix and catalyst modifications, Appl. Catal. B Environ., 2014, 147, 8.
  • [22] SHU Y., CHANG C., Decolorization effects of six azo dyes by O3, UV/O3 and UV/H2O2 processes, Dyes. Pigments, 2005, 65, 25.
  • [23] MHURCHÚ N., FOLEY G., Dead-end filtration of yeast suspensions: correlating specific resistance and flux data using artificial neural networks, J. Memb. Sci., 2006, 281, 325.
  • [24] CHEN S., SUN D., CHUNG S., Simultaneous removal of COD and ammonium from landfill leachate using an anaerobic–aerobic moving bed biofilm reactor system, Waste Manage., 2008, 28, 339.
  • [25] KURNIAWAN A., LO H., CHAN G., SILLANPÄÄ T., Biological processes for treatment of landfill leachate, J. Environ. Monit., 2010, 12, 2032.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-eb62facf-28c7-45a3-8fc3-012632f94bbc
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