Control adaptativo de la tasa de dilución para estabilidad en un biodigestor

dc.contributor.advisorJaramillo Matta, Adolfo Andrés
dc.contributor.authorGutiérrez Huertas, Adrián
dc.contributor.orcidJaramillo Matta, Adolfo Andrés [0000-0002-9743-5638]
dc.date.accessioned2025-05-27T20:45:59Z
dc.date.available2025-05-27T20:45:59Z
dc.date.created2025-05-06
dc.descriptionLa digestión anaerobia es un proceso biológico que produce biogás a partir de materia orgánica en ausencia de oxígeno, pero es sensible a la inestabilidad operativa debido a factores como la acumulación de ácidos grasos volátiles (AGV). La tasa de dilución es un parámetro clave para mantener la estabilidad del sistema, ya que su mal control puede provocar desequilibrios microbianos y fallos en el proceso. Este trabajo propone un modelo matemático con dinámica no lineal y el diseño de un controlador adaptativo que regula la tasa de dilución con base en la medición del caudal de metano, buscando una operación continua y estable del biodigestor.
dc.description.abstractAnaerobic digestion is a biological process that produces biogas from organic matter in the absence of oxygen, but it is sensitive to operational instability due to factors such as the accumulation of volatile fatty acids (VFAs). The dilution rate is a key parameter for maintaining system stability, as poor control can lead to microbial imbalances and process failure. This work proposes a mathematical model with nonlinear dynamics and the design of an adaptive controller that regulates the dilution rate based on methane flow rate measurements, aiming for continuous and stable biodigester operation.
dc.format.mimetypepdf
dc.identifier.urihttp://hdl.handle.net/11349/95734
dc.language.isospa
dc.publisherUniversidad Distrital Francisco José de Caldas
dc.relation.referencesP. Weiland, “Biogas production: Current state and perspectives,” Appl. Microbiol. Biotechnol., vol. 85, no. 4, pp. 849–860, 2010, doi: 10.1007/s00253-009-2246-7.
dc.relation.referencesF. Haugen, R. Bakke, and B. Lie, “Adapting dynamic mathematical models to a pilot anaerobic digestion reactor,” Model. Identif. Control, vol. 34, no. 2, pp. 35–54, 2013, doi: 10.4173/mic.2013.2.1.
dc.relation.referencesK. W. Szewczyk and J. Bukowski, “Modelling of a batch anaerobic digestion,” Polish J. Chem. Technol., vol. 10, no. 1, pp. 45–48, 2008, doi: 10.2478/v10026-008-0011-9.
dc.relation.referencesK. C. Draa, H. Voos, M. Alma, and M. Darouach, “Adaptive control of the methane flow rate in biogas plants,” 3rd Int. Conf. Control. Eng. Inf. Technol. CEIT 2015, no. 1, 2015, doi: 10.1109/CEIT.2015.7233155.
dc.relation.referencesJ. D. Browne and J. D. Murphy, “Assessment of the resource associated with biomethane from food waste,” Appl. Energy, vol. 104, pp. 170–177, 2013, doi: 10.1016/j.apenergy.2012.11.017.
dc.relation.referencesU. A. Abubakar et al., “Evaluation of traditional and machine learning approaches for modeling volatile fatty acid concentrations in anaerobic digestion of sludge: potential and challenges,” Environ. Sci. Pollut. Res., no. 0123456789, 2024, doi: 10.1007/s11356-024-33281-2.
dc.relation.referencesM. Vítězová, A. Kohoutová, T. Vítěz, N. Hanišáková, and I. Kushkevych, “Methanogenic microorganisms in industrial wastewater anaerobic treatment,” Processes, vol. 8, no. 12, pp. 1–27, 2020, doi: 10.3390/pr8121546.
dc.relation.referencesV. N. Beschkov and I. K. Angelov, “Volatile Fatty Acid Production vs. Methane and Hydrogen in Anaerobic Digestion,” Fermentation, vol. 11, no. 4, 2025, doi: 10.3390/fermentation11040172.
dc.relation.referencesIDAE, Biomasa: Digestores anaerobios. 2007. [Online]. Available: http://www.idae.es/uploads/documentos/documentos_10737_Biomasa_digestores_07_a996b846.pdf
dc.relation.referencesR. O. Owhondah et al., “Assessment and parameter identification of simplified models to describe the kinetics of semi-continuous biomethane production from anaerobic digestion of green and food waste,” Bioprocess Biosyst. Eng., vol. 39, no. 6, pp. 977–992, 2016, doi: 10.1007/s00449-016-1577-x.
dc.relation.referencesS. Fawzy, M. Saeed, A. Eladl, and M. El-Saadawi, “Adaptive Control System for Biogas Power Plant Using Model Predictive Control,” J. Mod. Power Syst. Clean Energy, vol. 9, no. 5, pp. 1193–1204, Sep. 2021, doi: 10.35833/MPCE.2019.000170.
dc.relation.referencesEuropean Biogas Association, “Digestate factsheet: the value of organic fertilisers for Europe’s economy, society and environment,” Dig. Factsheet, pp. 1–4, 2015, [Online]. Available: http://europeanbiogas.eu/wp-content/uploads/2015/07/Digestate-paper-final08072015.pdf%0Ahttp://european-biogas.eu/wp-content/uploads/2015/07/Digestate-paper-final08072015.pdf
dc.relation.referencesB. Friedland, Book Review: Control System Design—An Introduction to State Space Methods, vol. 23, no. 4. 1986. doi: 10.1177/002072098602300437.
dc.relation.referencesE. Petre and D. Selisteanu, “Adaptive and robust-adaptive control schemes for an anaerobic bioprocess with biogas production,” 2013 17th Int. Conf. Syst. Theory, Control Comput. ICSTCC 2013; Jt. Conf. SINTES 2013, SACCS 2013, SIMSIS 2013 - Proc., pp. 404–409, 2013, doi: 10.1109/ICSTCC.2013.6688992.
dc.relation.referencesA. Gelb, Applied optimal estimation, vol. 64, no. 4. 2008. doi: 10.1109/proc.1976.10175.
dc.relation.referencesN. J. Tissot, Pose, “Regulación y Control de Biogás en Edar mediante Sistema de Control y Monitorización de Siemens,” 2023. [Online]. Available: https://riunet.upv.es/bitstream/handle/10251/192548/Tissot - REGULACION Y CONTROL DE BIOGAS EN EDAR MEDIANTE SISTEMA DE CONTROL Y MONITORIZACION DE S....pdf?sequence=1&isAllowed=y
dc.relation.referencesK. J. Astrom and B. Wittenmark, “Adaptive Control (2nd ed.).,” New York: Dover Publication, Inc. 2008.
dc.relation.referencesS. Ayinde Azeez and D. B. Gwandangaji, “Modeling Anaerobic Co-digestion of Food Wastes and Cattle Manure in an Industrial Plant: a System Dynamic Approach,” no. 1, 2022, [Online]. Available: https://doi.org/10.21203/rs.3.rs-1167800/v1
dc.relation.referencesLukitawesa, R. J. Patinvoh, R. Millati, I. Sárvári-Horváth, and M. J. Taherzadeh, “Factors influencing volatile fatty acids production from food wastes via anaerobic digestion,” Bioengineered, vol. 11, no. 1, pp. 39–52, 2020, doi: 10.1080/21655979.2019.1703544.
dc.relation.referencesA. R. Toscano, A. P. Piñeres Castillo, J. C. Mojica Herazo, R. L. López, and R. R. Restrepo, “Management and control of variables for the generation of biogas from pig zungo,” in Procedia Computer Science, 2020, vol. 177, pp. 261–266. doi: 10.1016/j.procs.2020.10.036.
dc.relation.referencesM. C. Jose Andres and H. A. Jonh Paul, “Desarrollo de un Sistema de Control para el Acondicionamiento de Gas de Síntesis y Biogás en la Alimentación de un Motor de Combustión Interna,” 2017. [Online]. https://repository.uamerica.edu.co/bitstream/20.500.11839/6499/1/4121504-2017-2-IM.pdf
dc.relation.referencesK. Yoshida, K. Kametani, and N. Shimizu, “Adaptive identification of anaerobic digestion process for biogas production management systems,” Bioprocess Biosyst. Eng., vol. 43, no. 1, pp. 45–54, Jan. 2020, doi: 10.1007/s00449-019-02203-9.
dc.relation.referencesM. O. Okwu et al., “Application of Fuzzy Mamdani Model for Biogas Yield Prediction in Anaerobic Co-Digestion of Decomposable Wastes,” in Procedia Computer Science, 2024, vol. 232, pp. 22592268. doi: 10.1016/j.procs.2024.02.045.
dc.relation.referencesR. E. Precup, C. A. Bojan-Dragos, M. Barbu, and S. Caraman, “Fuzzy control of an anaerobic digestion process,” in 2015 19th International Conference on System Theory, Control and Computing, ICSTCC 2015 - Joint Conference SINTES 19, SACCS 15, SIMSIS 19, 2015, pp. 69–74. doi: 10.1109/ICSTCC.2015.7321271.
dc.relation.referencesW. Ahmed and J. Rodríguez, “A model predictive optimal control system for the practical automatic start-up of anaerobic digesters,” Water Res., vol. 174, May 2020, doi: 10.1016/j.watres.2020.115599.
dc.relation.referencesL. Appels, J. Baeyens, J. Degrève, and R. Dewil, “Principles and potential of the anaerobic digestion of waste-activated sludge,” Prog. Energy Combust. Sci., vol. 34, no. 6, pp. 755–781, 2008, doi: 10.1016/j.pecs.2008.06.002.
dc.relation.referencesL. L. Shyan et al., “Effort to Mitigate Volatile Fatty Acid Inhibition by Using Mixed Inoculum and Compost for the Degradation of Food Waste and the Production of Biogas,” Sustain. , vol. 15, no. 2, 2023, doi: 10.3390/su15021185.
dc.relation.referencesL. A. Yaniris, O. Abreu, and C. Ma, “La Digestión Anaerobia: Aspectos Teóricos parte I,” Icidca, vol. 1, pp. 35–48, 2005, [Online]. Available: http://www.redalyc.org/articulo.oa?id=223120659006
dc.relation.referencesJ. Guadalupe, “MODELADO Y SIMULACIÓN DEL PROCESO DE DIGESTIÓN ANAEROBIA DE DESECHOS AGROINDUSTRIALES.” Boca del Río, Veracruz, 2018.
dc.relation.referencesD. J. Batstone and J. Keller, “Industrial applications of the IWA anaerobic digestion model No. 1 (ADM1),” Water Sci. Technol., vol. 47, no. 12, pp. 199–206, 2003, doi: 10.2166/wst.2003.0647.
dc.relation.referencesS. P. Graef, J. F. Andrews, and F. Andrews, “Stability and Control of Anaerobic Digestion,” Water Pollut. Control Fed., vol. 46, no. 4, pp. 666–683, 1974.
dc.relation.referencesJ. F. ANDREWS and S. P. GRAEF, “Dynamic Modeling and Simulation of the Anaerobic Digestion Process,” 1971, pp. 126–162. doi: 10.1021/ba-1971-0105.ch008.
dc.relation.referencesJ. A. Arzate et al., “Anaerobic Digestion Model (AM2) for the Description of Biogas Processes at Dynamic Feedstock Loading Rates,” Chemie-Ingenieur-Technik, vol. 89, no. 5, pp. 686–695, 2017, doi:10.1002/cite.201600176.
dc.relation.referencesO. Bernard et al., “Dynamical model development and parameter identification for an anaerobic wastewater treatment process. Biotechnology and bioengineering 75.4 (2001) 424-438..pdf,” Biotechnol. Bioeng., vol. 75, no. 4, pp. 424–438, 2001.
dc.relation.referencesD. T. Hill, “A Comprehensive Dynamic Model for Animal Waste Methanogenesis,” Trans. ASAE, vol. 25, no. 5, pp. 1374–1380, 1982, doi: 10.13031/2013.33730.
dc.relation.referencesD. T. Hill, “Simplified monod kinetics of methane fermentation of animal wastes,” Agric. Wastes, vol. 5, no. 1, pp. 1–16, 1983, doi: 10.1016/0141-4607(83)90009-4.
dc.relation.referencesO. Harker, A. A. Jaramillo, and P. U. Okoye, “Linear control strategy of the dilution rate for stability in the anaerobic digestion process,” Syst. Sci. Control Eng., vol. 12, no. 1, 2024, doi: 10.1080/21642583.2024.2367965.
dc.relation.referencesJ. H. Escamilla, APLICACION DE OSERVADORES DE ESTADO PARA VERIFICAR LAS MEDICIONES DE TEMPERATURA DE UN PROCESO DE EXTRUSION, vol. 43, no. Diciembre. Monterrey, 1998.
dc.relation.referencesG. Bastin and D. Dochain, “On-line estimation and adaptive control of bioreactors,” Anal. Chim. Acta, vol. 243, p. 324, 1991, doi: 10.1016/s0003-2670(00)82585-4.
dc.relation.referencesP. S. Maybeck and G. M. Siouris, “Stochastic Models, Estimation, and Control, Volume I,” IEEE Trans. Syst. Man Cybern., vol. 10, no. 5, p. 282, 1980, doi: 10.1109/TSMC.1980.4308494.
dc.relation.referencesJ. L. Crassidis and J. L. Junkins, Optimal Estimation of Dynamic Systems: Second Edition. 2011. doi: 10.1201/b11154.
dc.relation.referencesL. O. González Salcedo and Y. Olaya Arboleda, “Fundamentos para el diseño de Biodigestores,” p. 32, Jul. 2012, [Online]. Available: http://www.bdigital.unal.edu.co/7967/
dc.relation.referencesO. Harker-Sanchez, A. A. Jaramillo, and D. M. Arias, “Method to obtain parameters k2, k3 for dilution rate observer in AM2 model of the anaerobic digestion process in a batch reactor,” Energy Sources, Part A Recover. Util. Environ. Eff., vol. 46, no. 1, pp. 3110–3123, 2024, doi: 10.1080/15567036.2024.2311326.
dc.relation.referencesS. A. Frank, “Adaptive Control,” SpringerBriefs Appl. Sci. Technol., pp. 85–89, 2018, doi: 10.1007/978-3-319-91707-8_11.
dc.relation.referencesM. C.-D. la C. F. L. Picón M., “Control adaptativo por modelo de referencia de un sistema de levitación magnética Model-reference adaptive control of a magnetic levitation system,” Rev. Ing. UC, vol. 14, no. 2, pp. 7–13, 2007, [Online]. Available: revistaing@uc.edu.ve
dc.relation.referencesY. Wang, T. Huntington, and C. D. Scown, “Tree-Based Automated Machine Learning to Predict Biogas Production for Anaerobic Co-digestion of Organic Waste,” ACS Sustain. Chem. Eng., vol. 9, no. 38, pp. 12990–13000, 2021, doi: 10.1021/acssuschemeng.1c04612.
dc.rights.accesoRestringido (Solo Referencia)
dc.rights.accessrightsRestrictedAccess
dc.subjectDigestión anaerobia
dc.subjectControl adaptativo
dc.subjectTasa de dilución
dc.subjectEstabilidad de procesos
dc.subjectÁrboles de decisión
dc.subject.keywordAnaerobic digestion
dc.subject.keywordAdaptive control
dc.subject.keywordDilution rate
dc.subject.keywordProcess stability
dc.subject.keywordDecision trees
dc.subject.lembIngeniería Eléctrica -- Tesis y disertaciones académicas
dc.subject.lembBiodigestores
dc.subject.lembDigestión anaerobia
dc.subject.lembModelos matemáticos -- Sistemas biológicos
dc.titleControl adaptativo de la tasa de dilución para estabilidad en un biodigestor
dc.title.titleenglishAdaptive control of the dilution rate for stability in a biodigester
dc.typebachelorThesis
dc.type.degreeMonografía
dc.type.driverinfo:eu-repo/semantics/bachelorThesis

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