Improving anaerobic co-digestion of different residual biomass sources readily available in Colombia by process parameters optimization

En el marco del desarrollo sostenible, existe una necesidad creciente de evaluar, modelar y optimizar la implementación de tecnologías de energía renovable como la codigestión anaeróbica de diferentes residuos orgánicos.Este trabajo estudió la influencia de algunos parámetros independientes en la pr...

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Autores:: Mosquera Tobar, Jhessica Daniela
Varela Lizarralde, Linda Jineth
Santis Navarro, Angélica María
Villamizar, Sergio
Acevedo Pabón, Paola Andrea
Cabeza Rojas, Iván Orlando

Tipo de recurso:: Article of journal

Fecha de publicación:: 2020

Institución:: Universidad Cooperativa de Colombia

Repositorio:: Repositorio UCC

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network_acronym_str	COOPER2
network_name_str	Repositorio UCC
repository_id_str
dc.title.spa.fl_str_mv	Improving anaerobic co-digestion of different residual biomass sources readily available in Colombia by process parameters optimization
title	Improving anaerobic co-digestion of different residual biomass sources readily available in Colombia by process parameters optimization
spellingShingle	Improving anaerobic co-digestion of different residual biomass sources readily available in Colombia by process parameters optimization Modelo empírico Codigestión anaeróbica Residuos orgánicos Método superficie de respuesta Empirical model Anaerobic co-digestion Organic residues Response surface method
title_short	Improving anaerobic co-digestion of different residual biomass sources readily available in Colombia by process parameters optimization
title_full	Improving anaerobic co-digestion of different residual biomass sources readily available in Colombia by process parameters optimization
title_fullStr	Improving anaerobic co-digestion of different residual biomass sources readily available in Colombia by process parameters optimization
title_full_unstemmed	Improving anaerobic co-digestion of different residual biomass sources readily available in Colombia by process parameters optimization
title_sort	Improving anaerobic co-digestion of different residual biomass sources readily available in Colombia by process parameters optimization
dc.creator.fl_str_mv	Mosquera Tobar, Jhessica Daniela Varela Lizarralde, Linda Jineth Santis Navarro, Angélica María Villamizar, Sergio Acevedo Pabón, Paola Andrea Cabeza Rojas, Iván Orlando
dc.contributor.author.none.fl_str_mv	Mosquera Tobar, Jhessica Daniela Varela Lizarralde, Linda Jineth Santis Navarro, Angélica María Villamizar, Sergio Acevedo Pabón, Paola Andrea Cabeza Rojas, Iván Orlando
dc.subject.spa.fl_str_mv	Modelo empírico Codigestión anaeróbica Residuos orgánicos Método superficie de respuesta
topic	Modelo empírico Codigestión anaeróbica Residuos orgánicos Método superficie de respuesta Empirical model Anaerobic co-digestion Organic residues Response surface method
dc.subject.other.spa.fl_str_mv	Empirical model Anaerobic co-digestion Organic residues Response surface method
description	En el marco del desarrollo sostenible, existe una necesidad creciente de evaluar, modelar y optimizar la implementación de tecnologías de energía renovable como la codigestión anaeróbica de diferentes residuos orgánicos.Este trabajo estudió la influencia de algunos parámetros independientes en la producción de biogás mediante codigestión anaeróbica de residuos orgánicos específicos, ampliamente disponibles en Colombia (estiércol de cerdo -PM-, lodos de depuradora –SS–, fracción orgánica de residuos sólidos urbanos -OFMSW-, residuos de la industria de bebidas de frutas embotelladas -RBFDI-, y residuos de la industria del cacao -CIR-). El potencial bioquímico de metano (BMP) de diferentes mezclas fue evaluado mediante un diseño experimental Box-Behnken, donde los parámetros fueron: contenido de sólidos volátiles (0.5, 1,25 y 2 g de VS), relación C / N (25, 35 y 45) y fuente de nitrógeno (0% solo para SS, 50% para residuos PM y SS,y 100% si solo PM). Los resultados permitieron la construcción de modelos empíricos utilizando polinomios de segundo orden y MARSplines. Según la maximización, la mejor mezcla para la producción de biogás es la que contiene RBFDI, OFMSW y SS, una relación C / N de 40 y 0,5 g VS; la producción estimada fue de alrededor de 382,17 ml de CH4 g-1 VS, MARSplines demostró tener los mejores valores de predicción y el coeficiente de determinación más alto (94–97%). Además, se evidenció que el contenido de g VS es una variable crítica en la producción de metano, la relación C / N debe estar en valores medios, y ambas fuentes de nitrógeno son adecuadas para ser utilizadas en el proceso de codigestión, dependiendo de la disponibilidad del área de interés.
publishDate	2020
dc.date.accessioned.none.fl_str_mv	2020-12-04T16:10:00Z
dc.date.available.none.fl_str_mv	2020-12-04T16:10:00Z
dc.date.issued.none.fl_str_mv	2020-11-01
dc.type.none.fl_str_mv	Artículo
dc.type.coar.fl_str_mv	http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.none.fl_str_mv	http://purl.org/coar/resource_type/c_6501
dc.type.coarversion.none.fl_str_mv	http://purl.org/coar/version/c_970fb48d4fbd8a85
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dc.type.version.none.fl_str_mv	info:eu-repo/semantics/publishedVersion
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dc.identifier.issn.spa.fl_str_mv	09619534
dc.identifier.uri.spa.fl_str_mv	https://doi.org/10.1016/j.biombioe.2020.105790
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/20.500.12494/28390
dc.identifier.bibliographicCitation.spa.fl_str_mv	Mosquera, J., Varela, L., Santis, A., Villamizar, S., Acevedo, P., y Cabeza, I. (2020). Improving anaerobic co-digestion of different residual biomass sources readily available in Colombia by process parameters optimization. Biomasa y bioenergía, 142, 105790. https://doi.org/10.1016/j.biombioe.2020.105790
identifier_str_mv	09619534 Mosquera, J., Varela, L., Santis, A., Villamizar, S., Acevedo, P., y Cabeza, I. (2020). Improving anaerobic co-digestion of different residual biomass sources readily available in Colombia by process parameters optimization. Biomasa y bioenergía, 142, 105790. https://doi.org/10.1016/j.biombioe.2020.105790
url	https://doi.org/10.1016/j.biombioe.2020.105790 https://hdl.handle.net/20.500.12494/28390
dc.relation.isversionof.spa.fl_str_mv	https://www.sciencedirect.com/science/article/abs/pii/S0961953420303251#!
dc.relation.ispartofjournal.spa.fl_str_mv	Biomass and Bioenergy
dc.relation.references.spa.fl_str_mv	L. Casas Godoy, G. Sandoval Fabia, Enzimas en la valorización de residuos agroindustriales, Rev. Digit. Univ. 15 (2014). J. Mata-Alvarez, J. Dosta, M.S. Romero-Güiza, X. Fonoll, M. Peces, S. Astals, A critical review on anaerobic co-digestion achievements between 2010 and 2013, Renew. Sustain. Energy Rev. 36 (2014) 412–427. S. Karellas, I. Boukis, G. Kontopoulos, Development of an investment decision tool for biogas production from agricultural waste, Renew. Sustain. Energy Rev. 14 (2010) 1273–1282 I. Angelidaki, D.J. Batstone, Anaerobic digestion: process. Solid Waste Technology & Management, 2010, pp. 583–600 F. Alatriste-Mondrag´on, P. Samar, H.H.J. Cox, B.K. Ahring, R. Iranpour, Anaerobic codigestion of municipal, farm, and industrial organic wastes: a survey of recent literature, Water Environ. Res. 78 (2006) 607–636. K. Hagos, J. Zong, D. Li, C. Liu, X. Lu, Anaerobic co-digestion process for biogas production: progress, challenges and perspectives, Renew. Sustain. Energy Rev. 76 (2017) 1485–1496. X. G´omez, M.J. Cuetos, J. Cara, A. Moran, A.I. García, Anaerobic co-digestion of primary sludge and the fruit and vegetable fraction of the municipal solid wastes: conditions for mixing and evaluation of the organic loading rate, Renew. Energy 31 (2006) 2017–2024. C. Mao, Y. Feng, X. Wang, G. Ren, Review on research achievements of biogas from anaerobic digestion, Renew. Sustain. Energy Rev. 45 (2015) 540–555. D. Divya, L.R. Gopinath, P. Merlin Christy, A review on current aspects and diverse prospects for enhancing biogas production in sustainable means, Renew. Sustain. Energy Rev. 42 (2015) 690–699. J.H. Ebner, R.A. Labatut, J.S. Lodge, A.A. Williamson, T.A. Trabold, Anaerobic codigestion of commercial food waste and dairy manure: characterizing biochemical parameters and synergistic effects, Waste Manag. 52 (2016) 286–294. A. Rodríguez, J. ´Angel, E. Rivero, P. Acevedo, A. Santis, I. Cabeza, et al., Evaluation of the biochemical methane potential of pig manure, organic fraction of municipal solid waste and cocoa industry residues in Colombia, Chem Eng Trans 57 (2017) 55–60. I. Cabeza, M. Thomas, A. Vásquez, P. Acevedo, Anaerobic co-digestion of organic residues from different productive sectors in Colombia: biomethanation potential assessment, Chem Eng Trans 49 (2016) 385–390. P.G. Kougias, I. Angelidaki, Biogas and its opportunities—a review, Front. Environ. Sci. Eng. 12 (2018). Y. Ahn, W. Lee, S. Kang, S.- Kim, Enhancement of Sewage Sludge Digestion by Codigestion with Food Waste and Swine Waste, Waste Biomass Valoris, 2019 P.V. Rao, S.S. Baral, Experimental design of mixture for the anaerobic co-digestion of sewage sludge, Chem. Eng. J. 172 (2011) 977–986. M.A. Gonzalez-Salazar, M. Morini, M. Pinelli, P.R. Spina, M. Venturini, M. Finkenrath, et al., Methodology for biomass energy potential estimation: projections of future potential in Colombia, Renew. Energy 69 (2014) 488–505. J. Arias-Gaviria, S.X. Carvajal-Quintero, S. Arango-Aramburo, Understanding dynamics and policy for renewable energy diffusion in Colombia, Renew. Energy (2019) 1111–1119. Anonymous Informe de Sostenibilidad 2018, Postobón S. A, 2018. F. Rojas, E.J. Sancristan Sánchez, Guía ambiental para el cultivo del cacao, Federación Nacional de Cacaoteros, 2013. Federación Nacional de Cacaoteros, Informe de gestión 2018, 2019. R. Rodríguez, P. Gauthier-Maradei, H. Escalante, Fuzzy spatial decision tool to rank suitable sites for allocation of bioenergy plants based on crop residue, Biomass Bioenergy 100 (2017) 17–30. Instituto Colombiano Agropecuario, Censo Pecuario Nacional 2019 (2019). C.J. Rangel, M.A. Hernández, J.D. Mosquera, Y. Castro, I.O. Cabeza, P.A. Acevedo, Hydrogen production by dark fermentation process from pig manure, cocoa mucilage, and coffee mucilage, Biomass Convers Biorefinery, 2020. H. Escalante Hernández, J. Orduz Prada, H.J. Zapata Lesmes, M.C. Cardona Ruiz, M. Duarte Ortega, Atlas del potential energético de la biomasa residual, Unidad de Planeación Minero Energética, 2011. F. Acosta, Y. Acuña, M. Butti, C. Hernández, R. Steinmetz, G. Zinola, Los biodigestores y la economía circular, Revista RedBioLAC 3 (2019) 6–9. L. Appels, J. Baeyens, J. Degreve, R. Dewil, Principles and potential of the anaerobic digestion of waste-activated sludge, Prog. Energy Combust. Sci. 34 (2008) 755–781. Comité de verificación de la Sentencia del río Bogotá, Ranking ASUR´IO, vigencia 2018, de PTARs de la cuenca del río Bogotá, según ´índice ASURIO de desempeño. Asociación de Usuarios de los Recursos Naturales Renovables y Defensa Ambiental de la Cuenca del Río Bogotá, 2019. I.O. Cabeza, R. López, M. Ruiz-Montoya, M.J. Díaz, Maximising municipal solid waste legume trimming residue mixture degradation in composting by control parameters optimization, J. Environ. Manag. 128 (2013) 266–273. V.K. Tyagi, L.A. Fdez-Güelfo, Y. Zhou, C.J. ´Alvarez-Gallego, L.I.R. Garcia, W.J. Ng, Anaerobic co-digestion of organic fraction of municipal solid waste (OFMSW): progress and challenges, Renew. Sustain. Energy Rev. 93 (2018) 380–399 G. Capson-Tojo, M. Rouez, M. Crest, J. Steyer, J. Delgen`es, R. Escudie, Food waste valorization via anaerobic processes: a review, Rev. Environ. Sci. Biotechnol. 15 (2016) 499–547. S. Xie, F.I. Hai, X. Zhan, W. Guo, H.H. Ngo, W.E. Price, et al., Anaerobic codigestion: a critical review of mathematical modelling for performance optimization, Bioresour. Technol. 222 (2016) 498–512. C. Zhang, H. Su, J. Baeyens, T. Tan, Reviewing the anaerobic digestion of food waste for biogas production, Renew. Sustain. Energy Rev. 38 (2014) 383–392. A. Kovalovszki, M. Alvarado-Morales, I.A. Fotidis, I. Angelidaki, A systematic methodology to extend the applicability of a bioconversion model for the simulation of various co-digestion scenarios, Bioresour. Technol. 235 (2017) 157–166. H.- Kim, H.- Shin, S.- Han, S.- Oh, Response surface optimization of substrates for thermophilic anaerobic codigestion of sewage sludge and food waste, J. Air Waste Manag. Assoc. 57 (2007) 309–318. J. Jiménez, M.E. Cisneros-Ortiz, Y. Guardia-Puebla, J.M. Morgan-Sagastume, A. Noyola, Optimization of the thermophilic anaerobic co-digestion of pig manure, agriculture waste and inorganic additive through specific methanogenic activity, Water Sci. Technol. 69 (2014) 2381–2388. S. Mani, J. Sundaram, K.C. Das, Process simulation and modeling: anaerobic digestion of complex organic matter, Biomass Bioenergy 93 (2016) 158–167. S.S. Baral, G. Surendran, N. Das, P.V. Rao, Statistical design of experiments for the cr (vi) adsorption on Weed Salvinia cucullata, Environmental Engineering and Management Journal 12 (2013) 465–474. G. Surendran, B. Viswanath Sasank, S.S. Baral, Modeling and simulation for the adsorptive removal of Cr(VI) from aqueous solution, Desalin Water Treat 52 (2014) 5652–5662. A. Rodríguez, A. Muñoz, L. Tique, J. Ladino, A.M. Santis, I. Cabeza, et al., Influence of the use of Co-Substrates on the anaerobic Co-Digestion of municipal solid waste, cocoa industry waste and bottled beverage industry waste, Chem Eng Trans 65 (2018) 541–546. F. Raposo, M.A. De la Rubia, V. Fernandez-Cegrí, R. Borja, Anaerobic digestion of solid organic substrates in batch mode: an overview relating to methane yields and experimental procedures, Renew. Sustain. Energy Rev. 16 (2012) 861–877. S. Dechrugsa, D. Kantachote, S. Chaiprapat, Effects of inoculum to substrate ratio, substrate mix ratio and inoculum source on batch co-digestion of grass and pig manure, Bioresour. Technol. 146 (2013) 101–108. T.G. Narayani, P. Gomathi Priya, Biogas production through mixed fruit wastes biodegradation, J. Sci. Ind. Res. 71 (2012) 217–220. C. Holliger, M. Alves, D. Andrade, I. Angelidaki, S. Astals, U. Baier, et al., Towards a standardization of biomethane potential tests, Water Sci. Technol. 74 (2016) 2515–2522. S. Akhnazarova, V. Kafarov, Experiment Optimization in Chemistry and Chemical Engineering, Mir Publishers, Moscow, 1982. H.F. Jerome, Multivariate adaptive regression splines, Ann. Stat. 19 (1991) 1–141 X. Wang, G. Yang, Y. Feng, G. Ren, X. Han, Optimizing feeding composition and carbon-nitrogen ratios for improved methane yield during anaerobic co-digestion of dairy, chicken manure and wheat straw, Bioresour. Technol. 120 (2012) 78–83. ] J.A. Alvarez, L. Otero, J.M. Lema, A methodology for optimising feed composition for anaerobic co-digestion of agro-industrial wastes, Bioresour. Technol. 101 (2010) 1153–1158. S. Luostarinen, S. Luste, M. Sillanp¨a¨a, Increased biogas production at wastewater treatment plants through co-digestion of sewage sludge with grease trap sludge from a meat processing plant, Bioresour. Technol. 100 (2009) 79–85. M.S. Lisboa, S. Lansing, Characterizing food waste substrates for co-digestion through biochemical methane potential (BMP) experiments, Waste Manag. 33 (2013) 2664–2669 R.A. Labatut, L.T. Angenent, N.R. Scott, Biochemical methane potential and biodegradability of complex organic substrates, Bioresour. Technol. 102 (2011) 2255–2264. R. Alvarez, G. Liden, Semi-continuous co-digestion of solid slaughterhouse waste, manure, and fruit and vegetable waste, Renew. Energy 33 (2008) 726–734 S. Pilli, T.T. More, S. Yan, R.D. Tyagi, R.Y. Surampalli, T.C. Zhang, Anaerobic digestion or co-digestion for sustainable solid waste treatment/management, in: Sustainable Solid Waste Management, 2016, pp. 187–232. K. Koch, Calculating the degree of degradation of the volatile solids in continuously operated bioreactors, Biomass Bioenergy 74 (2015) 79–83 T. Lee, I. Chen, A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines, Expert Syst. Appl. 28 (2005) 743–752. Y. Gan, Q. Duan, W. Gong, C. Tong, Y. Sun, W. Chu, et al., A comprehensive evaluation of various sensitivity analysis methods: a case study with a hydrological model, Environ. Model. Software 51 (2014) 269–285. I. Angelidaki, L. Ellegaard, Codigestion of manure and organic wastes in centralized biogas plants: status and future trends, Appl. Biochem. Biotechnol. Part A Enzyme Eng. Biotechnol. 109 (2003) 95–105. S. Kashi, B. Satari, M. Lundin, I.S. Horv´ath, M. Othman, Application of a mixture design to identify the effects of substrates ratios and interactions on anaerobic codigestion of municipal sludge, grease trap waste, and meat processing waste, J Environ Chem Eng 5 (2017) 6156–6164. S.S. Baral, P.V. Rao, G. Surendran, Pretreatment of organic composite waste mixtures for enhanced biomethanantion, Energy Sources Recovery Util Environ Eff 40 (2018) 1380–1387. B. Molinuevo-Salces, M.C. García-González, C. González-Fernández, M.J. Cuetos, A. Morán, X. Gómez, Anaerobic co-digestion of livestock wastes with vegetable processing wastes: a statistical analysis, Bioresour. Technol. 101 (2010) 9479–9485.
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spelling	Mosquera Tobar, Jhessica DanielaVarela Lizarralde, Linda JinethSantis Navarro, Angélica MaríaVillamizar, SergioAcevedo Pabón, Paola AndreaCabeza Rojas, Iván Orlando1422020-12-04T16:10:00Z2020-12-04T16:10:00Z2020-11-0109619534https://doi.org/10.1016/j.biombioe.2020.105790https://hdl.handle.net/20.500.12494/28390Mosquera, J., Varela, L., Santis, A., Villamizar, S., Acevedo, P., y Cabeza, I. (2020). Improving anaerobic co-digestion of different residual biomass sources readily available in Colombia by process parameters optimization. Biomasa y bioenergía, 142, 105790. https://doi.org/10.1016/j.biombioe.2020.105790En el marco del desarrollo sostenible, existe una necesidad creciente de evaluar, modelar y optimizar la implementación de tecnologías de energía renovable como la codigestión anaeróbica de diferentes residuos orgánicos.Este trabajo estudió la influencia de algunos parámetros independientes en la producción de biogás mediante codigestión anaeróbica de residuos orgánicos específicos, ampliamente disponibles en Colombia (estiércol de cerdo -PM-, lodos de depuradora –SS–, fracción orgánica de residuos sólidos urbanos -OFMSW-, residuos de la industria de bebidas de frutas embotelladas -RBFDI-, y residuos de la industria del cacao -CIR-). El potencial bioquímico de metano (BMP) de diferentes mezclas fue evaluado mediante un diseño experimental Box-Behnken, donde los parámetros fueron: contenido de sólidos volátiles (0.5, 1,25 y 2 g de VS), relación C / N (25, 35 y 45) y fuente de nitrógeno (0% solo para SS, 50% para residuos PM y SS,y 100% si solo PM). Los resultados permitieron la construcción de modelos empíricos utilizando polinomios de segundo orden y MARSplines. Según la maximización, la mejor mezcla para la producción de biogás es la que contiene RBFDI, OFMSW y SS, una relación C / N de 40 y 0,5 g VS; la producción estimada fue de alrededor de 382,17 ml de CH4 g-1 VS, MARSplines demostró tener los mejores valores de predicción y el coeficiente de determinación más alto (94–97%). Además, se evidenció que el contenido de g VS es una variable crítica en la producción de metano, la relación C / N debe estar en valores medios, y ambas fuentes de nitrógeno son adecuadas para ser utilizadas en el proceso de codigestión, dependiendo de la disponibilidad del área de interés.In the framework of sustainable development, there is an increasing need to assess, model, and optimize the implementation of renewable energy technologies such as the anaerobic co-digestion of different organic residues. This work studied the influence of some independent parameters on the production of biogas through anaerobic co-digestion of specific organic residues widely available in Colombia (pig manure -PM-, sewage sludge –SS–, organic fraction of municipal solid waste -OFMSW-, residues from the bottled fruit drinks industry -RBFDI-, and cocoa industry residue -CIR-). The Biochemical Methane Potential (BMP) of different mixtures was assessed through a Box-Behnken experimental design, where the parameters were: volatile solids content (0.5, 1.25 and 2 g VS), C/N ratio (25, 35 and 45), and nitrogen source (0% for only SS, 50% both residues PM and SS, and 100% if only PM). The results allowed empirical model building by using second-order polynomial and MARSplines. According to the maximization, the best mixture for biogas production is the one containing RBFDI, OFMSW, and SS, a C/N ratio of 40 and 0.5 g VS; the estimated production was around 382.17 ml CH4 g-1 VS, MARSplines demonstrated to have the best predictions values and the highest determination coefficient (94–97%). Moreover, it was evidenced that the content of g VS is a critical variable on the methane production, C/N ratio must be in average values, and both nitrogen sources are suitable to be used in the co-digestion process depending on the availability of the area of interest.https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001535259https://orcid.org/0000-0002-9807-7828https://scienti.minciencias.gov.co/gruplac/jsp/visualiza/visualizagr.jsp?nro=00000000002960angelica.santisn@campusucc.edu.copaola.acevedop@ucc.edu.cohttps://scholar.google.com/citations?user=t2QURT0AAAAJ&hl=en9 p.Universidad Cooperativa de ColombiaElsevier LtdIngeniería IndustrialBogotáhttps://www.sciencedirect.com/science/article/abs/pii/S0961953420303251#!Biomass and BioenergyL. Casas Godoy, G. Sandoval Fabia, Enzimas en la valorización de residuos agroindustriales, Rev. Digit. Univ. 15 (2014).J. Mata-Alvarez, J. Dosta, M.S. Romero-Güiza, X. Fonoll, M. Peces, S. Astals, A critical review on anaerobic co-digestion achievements between 2010 and 2013, Renew. Sustain. Energy Rev. 36 (2014) 412–427.S. Karellas, I. Boukis, G. Kontopoulos, Development of an investment decision tool for biogas production from agricultural waste, Renew. Sustain. Energy Rev. 14 (2010) 1273–1282I. Angelidaki, D.J. Batstone, Anaerobic digestion: process. Solid Waste Technology & Management, 2010, pp. 583–600F. Alatriste-Mondrag´on, P. Samar, H.H.J. Cox, B.K. Ahring, R. Iranpour, Anaerobic codigestion of municipal, farm, and industrial organic wastes: a survey of recent literature, Water Environ. Res. 78 (2006) 607–636.K. Hagos, J. Zong, D. Li, C. Liu, X. Lu, Anaerobic co-digestion process for biogas production: progress, challenges and perspectives, Renew. Sustain. Energy Rev. 76 (2017) 1485–1496.X. G´omez, M.J. Cuetos, J. Cara, A. Moran, A.I. García, Anaerobic co-digestion of primary sludge and the fruit and vegetable fraction of the municipal solid wastes: conditions for mixing and evaluation of the organic loading rate, Renew. Energy 31 (2006) 2017–2024.C. Mao, Y. Feng, X. Wang, G. Ren, Review on research achievements of biogas from anaerobic digestion, Renew. Sustain. Energy Rev. 45 (2015) 540–555.D. Divya, L.R. Gopinath, P. Merlin Christy, A review on current aspects and diverse prospects for enhancing biogas production in sustainable means, Renew. Sustain. Energy Rev. 42 (2015) 690–699.J.H. Ebner, R.A. Labatut, J.S. Lodge, A.A. Williamson, T.A. Trabold, Anaerobic codigestion of commercial food waste and dairy manure: characterizing biochemical parameters and synergistic effects, Waste Manag. 52 (2016) 286–294.A. Rodríguez, J. ´Angel, E. Rivero, P. Acevedo, A. Santis, I. 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Technol. 101 (2010) 9479–9485.Modelo empíricoCodigestión anaeróbicaResiduos orgánicosMétodo superficie de respuestaEmpirical modelAnaerobic co-digestionOrganic residuesResponse surface methodImproving anaerobic co-digestion of different residual biomass sources readily available in Colombia by process parameters optimizationArtículohttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionAtribucióninfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2PublicationORIGINALbiomass & bioenergy 2020.pdfbiomass & bioenergy 2020.pdfArtículoapplication/pdf4012174https://repository.ucc.edu.co/bitstreams/ae4685c1-2921-41da-8f05-863779036c81/download6bdcff965d8d7a8b115553d85dbaa051MD51LICENSElicense.txtlicense.txttext/plain; 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

Improving anaerobic co-digestion of different residual biomass sources readily available in Colombia by process parameters optimization

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