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Dynamic modelling of an anaerobic reactor treating coffee wet wastewater via multiple regression model

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A multiple regression model approach was developed to estimate buffering indices, as well as biogas and methane productions in an upflow anaerobic sludge blanket (UASB) reactor treating coffee wet wastewater. Five input variables measured (pH, alkalinity, outlet VFA concentration, and total and soluble COD removal) were selected to develop the best models to identify their importance on methanation. Optimal regression models were selected based on four statistical performance criteria, viz. Mallow’s Cp statistic (Cp), Akaike information criterion (AIC), Hannan–Quinn criterion (HQC), and Schwarz–Bayesian information criterion (SBIC). The performance of the models selected were assessed through several descriptive statistics such as measure of goodness-of-fit test (coefficient of multiple determination, R2; adjusted coefficient of multiple determination, Adj-R2; standard error of estimation, SEE; and Durbin–Watson statistic, DWS), and statistics on the prediction errors (mean squared error, MSE; mean absolute error, MAE; mean absolute percentage error, MAPE; mean error, ME and mean percentage error, MPE). The estimated model reveals that buffering indices are strongly influenced by three variables (volatile fatty acids (VFA) concentration, soluble COD removal, and alkalinity); while, pH, VFA concentration and total COD removal were the most significant independent variables in biogas and methane production. The developed equation models obtained in this study, could be a powerful tool to predict the functionability and stability for the UASB system.
Wydawca
Rocznik
Tom
Strony
229--239
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
  • University of Granma, Study Center for Applied Chemistry, Cuba
  • Faculty of Architecture and Engineering, International SEK University, Quito, Ecuador
  • Language Center, University of Granma, Cuba
  • University of Granma, Study Center for Applied Chemistry, Cuba
  • College of Agricultural, Food and Biosystems Engineering, Technical University of Madrid, Spain
  • Faculty of Agronomy and Crop Science, University of Rostock, Germany
  • Faculty of Agronomy and Crop Science, University of Rostock, Germany
Bibliografia
  • ACQUAH H. 2010. Comparison of Akaike information criterion (AIC) and Bayesian information criterion (BIC) in selection of an asymmetric price relationship [online]. Journal of Development and Agricultural Economics. Vol. 2. Iss. 1 p. 1–6. [Access 15.07.2020]. Available at: http://www.academicjournals.org/app/ webroot/article/article1379662949_Acquah.pdf
  • AKKAYA E., AHMET D., GAMZE V. 2015. Estimation of biogas generation from a UASB reactor via multiple regression model. International Journal of Green Energy. Vol. 12 p. 185–189. DOI 10.1080/ 15435075.2011.651754.
  • ANTWI P., JIANZHENG L., PORTIA O.B., JIA M., EN S., KAIWEN D., FRANCIS K.B. 2017. Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with backpropagation artificial neural network. Bioresource Technology. Vol. 228 p. 106–115. DOI 10.1016/j.biortech.2016.12.045.
  • BARAMPOUTI E.M., MAI S.T., VLYSSIDES A.G. 2005. Dynamic modeling of biogas production in an UASB reactor for potato processing wastewater treatment. Chemical Engineering Journal. Vol. 106. Iss. 1 p. 53–58. DOI 10.1016/j.cej.2004.06.010.
  • CHONG S., SEN T.K., KAYAALP A., ANG H.M. 2012. The Performance Enhancements of UASB reactors for domestic sludge treatment – A state-of-the-art review. Water Research. Vol. 46 p. 3434–3470. DOI 10.1016/j.watres.2012.03.066.
  • GUARDIA-PUEBLA Y., JIMÉNEZ-HERNÁNDEZ J., PACHECO-GAMBOA R., RODRÍGUEZ-PÉREZ S., SÁNCHEZ-GIRÓN V. 2016. Multiple responses optimization on the anaerobic co-digestion of coffee wastewater with manures [online]. Ciencias Técnicas Agropecuarias. Vol. 25. Iss. 3 p. 54–64. [Access 15.07.2020]. Available at: http://scielo.sld. cu/pdf/rcta/v25n3/rcta06316.pdf
  • GUARDIA-PUEBLA Y., RODRÍGUEZ-PÉREZ S., CUSCÓ-VARONA Y., JIMÉNEZ- HERNÁNDEZ J., SÁNCHEZ-GIRÓN V. 2014a. Two-phase anaerobic digestion of coffee wet wastewater: Effect of recycle on anaerobic process performance [online]. Ciencias Técnicas Agropecuarias. Vol. 23. Iss. 1 p. 25–31. [Access 15.07.2020]. Available at: http:// scielo.sld.cu/pdf/rcta/v23n1/rcta04114.pdf
  • GUARDIA-PUEBLA Y., RODRÍGUEZ-PÉREZ S., JIMÉNEZ-HERNÁNDEZ J., SÁNCHEZ-GIRÓN V. 2013. Performance of a UASB reactor treating coffee wet wastewater [online]. Ciencias Técnicas Agropecuarias. Vol. 22. Iss. 3 p. 35–41. [Access 15.07.2020]. Available at: http:// scielo.sld.cu/pdf/rcta/v23n2/rcta09214.pdf
  • GUARDIA-PUEBLA Y., RODRÍGUEZ-PÉREZ S., JIMÉNEZ-HERNÁNDEZ J., SÁNCHEZ-GIRÓN V., MORGAN-SAGASTUME J., NOYOLA A. 2014b. Experimental design technique is useful tool to compare anaerobic systems. Renewable Bioresources. Vol. 2. Iss. 3 p. 1–12. DOI 10.7243/2052-6237-2-3.
  • HOUBRON E., LARRINAGA A., RUSTRIAN E. 2003. Liquefaction and methanization of solid and liquid coffee wastes by two phase anaerobic digestion process [online]. Water Science & Technology. Vol. 48. Iss. 6 p. 255–262. [Access 15.07.2020]. Available at: https://pubmed.ncbi.nlm.nih.gov/14640226
  • JUNG K.-W., KIM D.-H., LEE M.-Y., SHIN H.-S. 2012. Two-stage UASB reactor converting coffee drink manufacturing wastewater to hydrogen and methane. International Journal of Hydrogen Energy. Vol. 37 p. 7473–7481. DOI 10.1016/j.ijhydene .2012.01.150.
  • LAHAV O., MORGAN B. 2004. Titration methodologies for monitoring of anaerobic digestion in developing countries – A review. Journal of Chemical Technology and Biotechnology. Vol. 79 p. 1331– 1341. DOI 10.1002/jctb.1143.
  • MONTGOMERY D. 2013. Design and analysis of experiments. 8th ed. Hoboken. John Wiley & Sons, Inc. ISBN 978-1118-14692-7 pp. 757.
  • PÉREZ A, TORRES P. 2008. Indices de alcalinidad para el control del tratamiento anaerobio de aguas residuales fácilmente acidific-ables [Alkalinity indices for control of anaerobic treatment of readily acidifiable wastewaters]. Ingeniería y Competitividad. Vol. 10. Iss. 2 p. 41–52. DOI 10.25100/iyc.v10i2.2473.
  • RAMESH N., VENNILA G., ABDUL B.J., RAMESH S., MAGESH K.P. 2015. Energy production through organic fraction of municipal solid waste a multiple regression modeling approach. Ecotoxicology and Environmental Safety. Vol. 134 p. 350–357. DOI 10.1016/j. ecoenv.2015.08.027.
  • SANTOS DOS J.S, SANTOS DOS M.L., CONTI M.M., SANTOS DOS S.N., OLIVEIRA DE E. 2009. Evaluation of some metals in Brazilian coffees cultivated during the process of conversion from conventional to organic agriculture. Food Chemistry. Vol. 115 p. 1405–1410. DOI 10.1016/j.foodchem.2009.01.069.
  • SELVAMURUGAN M., DORAISAMY P., MAHESWARI M., NANDAKUMAR N.B. 2010. High rate anaerobic treatment of coffee processing wastewater using upflow anaerobic hybrid reactor [online]. Iran Journal of Environmental Health and Science Engineering. Vol. 7. Iss. 2 p. 129–136. [Access 10.07.2020]. Available at: https://www. sid.ir/FileServer/JE/102620100204.pdf
  • SHITTU O., ASEMOTA M. 2009. Comparison of criteria for estimating the order of autoregressive process: A Monte Carlo approach [online]. European Journal of Scientific Research. Vol. 30. Iss. 3 p. 409–416. [Access 10.07.2020]. Available at: http://www. eurojournals.com/ejsr.htm
  • SINGH K.P., BASANT N., MALIK A., JAIN G. 2010. Modeling the performance of “up flow anaerobic sludge blanket” reactor based wastewater treatment plant using linear and nonlinear approaches – A case study. Analytica Chimica Acta. Vol. 658 p. 1–11. DOI 10.1016/j.aca.2009.11.001.
  • TURKDOGAN-AYDINOL F.I., YETILMEZSOY K. 2010. A fuzzy-logic-based model to predict biogas and methane production rates in a pilot-scale mesophilic UASB reactor treating molasses wastewater. Journal of Hazardous Materials. Vol. 182 p. 460–471. DOI 10.1016/j.jhazmat.2010.06.054.
  • WANG Y., LIU Q. 2006. Comparison of Akaike information criterion (AIC) and Bayesian information criterion (BIC) in selection of stock – Recruitment relationships. Fisheries Research. Vol. 77 p. 220–225. DOI 10.1016/j.fishres.2005.08.011.
  • YETILMEZSOY K. 2012. Integration of kinetic modeling and desirability function approach for multi-objective optimization of UASB reactor treating poultry manure wastewater. Bioresource Technology. Vol. 118 p. 89–101. DOI 10.1016/j.biortech.2012.05.088.
  • YETILMEZSOY K., SAKAR S. 2008. Development of empirical models for performance evaluation of UASB reactors treating poultry manure wastewater under different operational conditions. Journal of Hazardous Materials. Vol. 153 p. 532–543. DOI 10.1016/j.jhazmat.2007.08.087.
  • YETILMEZSOY K., SAPCI-ZENGIN Z. 2009. Stochastic modeling applications for the prediction of COD removal efficiency of UASB reactors treating diluted real cotton textile wastewater. Stochastic Environmental Research and Risk Assessment. Vol. 23 p. 13– 26. DOI 10.1007/s00477-007-0191-5.
  • ZUCCARO C. 1992. Mallow’s Cp statistic and model selection in multiple linear regression. International Journal of Market Research. Vol. 34. Iss. 2 p. 1–13. DOI 10.1177/147078539203400204.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d1711359-b957-4856-b478-b536be23ab24
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