Neuro-fuzzy controller to accelerate biogas production in a biodigester
DOI:
https://doi.org/10.37636/10.37636/recit.v9n3e456Keywords:
Anaerobic digestion, Biogas production, Neuro-fuzzy control, Artificial neural networks, ANFIS, Biodigester, Renewable energy, Intelligent controlAbstract
The anaerobic digestion of domestic organic waste represents a sustainable alternative for renewable energy production through biogas generation. However, the inherently nonlinear and multivariable nature of biodigestion processes limits the effectiveness of conventional control strategies, reducing process efficiency and increasing production times. This study proposes the development and evaluation of an intelligent neuro-fuzzy control strategy aimed at accelerating biogas production in a batch anaerobic biodigester. A dynamic mathematical model of the biodigester was implemented in MATLAB®/Simulink to represent the biological behavior associated with biomass degradation and methane generation. Subsequently, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was designed and trained using process data obtained from dynamic simulations. The controller combined fuzzy logic decision rules with the adaptive learning capabilities of artificial neural networks, enabling automatic adjustment of control actions according to process operating conditions. The control architecture was implemented in a closed-loop configuration to regulate critical variables influencing anaerobic digestion performance. Simulation results demonstrated that the neuro-fuzzy controller significantly improved system stability, reduced dynamic oscillations, and maintained key process variables closer to their reference values. In comparison with open-loop operation, the proposed strategy increased biogas production and reduced the time required to reach stable operating conditions. Furthermore, the controller exhibited a significantly faster response in biogas production, achieving up to 71% improvement in efficiency compared with the open-loop system and reducing the time required to reach equivalent production levels by approximately 70 days, indicating high predictive and adaptive capability. These improvements are attributed to the controller’s ability to compensate for disturbances and continuously maintain favorable conditions for microbial activity and methane production. The findings confirm that the integration of artificial intelligence techniques into biodigester control systems constitutes a viable and promising approach for enhancing anaerobic digestion efficiency, increasing renewable energy generation, and supporting sustainable waste management. Future work will focus on the experimental implementation of the proposed controller using industrial sensors, actuators, and real-time embedded control platforms.
Downloads
References
[1] A. Ishola, "Global renewable energy transition in fossil fuel dependent regions," World Journal of Advanced Research and Reviews, vol. 24, no. 1, pp. 1138–1373, 2024, doi: 10.30574/wjarr.2024.24.1.3046. DOI: https://doi.org/10.30574/wjarr.2024.24.1.3046
[2] G. Kataya, D. Cornu, M. Bechelany, A. Hijazi, and M. Issa, "Biomass waste conversion technologies and its application for sustainable environmental development—a review," Agronomy, vol. 13, no. 11, Art. no. 2833, 2023, doi: 10.3390/agronomy13112833. DOI: https://doi.org/10.3390/agronomy13112833
[3] F. Ali et al., "Fueling the future: Biomass applications for green and sustainable energy," Discover Sustainability, vol. 5, no. 1, Art. no. 156, 2024, doi: 10.1007/s43621-024-00309-z. DOI: https://doi.org/10.1007/s43621-024-00309-z
[4] R. A. Verzijlbergh, L. J. de Vries, G. P. J. Dijkema, and P. M. Herder, "Institutional challenges caused by the integration of renewable energy sources in the European electricity sector," Renewable and Sustainable Energy Reviews, vol. 75, pp. 660–667, 2017, doi: 10.1016/j.rser.2016.11.039. DOI: https://doi.org/10.1016/j.rser.2016.11.039
[5] F. Yang, J. C. Meerman, and A. P. C. Faaij, "Carbon capture and biomass in industry: A techno-economic analysis and comparison of negative emission options," Renewable and Sustainable Energy Reviews, vol. 144, Art. no. 111028, Jul. 2021, doi: 10.1016/j.rser.2021.111028. DOI: https://doi.org/10.1016/j.rser.2021.111028
[6] Q. Hassan et al., "The renewable energy role in the global energy transformations," Renewable Energy Focus, vol. 48, Art. no. 100545, 2024, doi: 10.1016/j.ref.2024.100545. DOI: https://doi.org/10.1016/j.ref.2024.100545
[7] A. Hussain, S. M. Arif, and M. Aslam, "Emerging renewable and sustainable energy technologies: State of the art," Renewable and Sustainable Energy Reviews, 2017, doi: 10.1016/j.rser.2016.12.033. DOI: https://doi.org/10.1016/j.rser.2016.12.033
[8] T. Kalak, "Potential use of industrial biomass waste as a sustainable energy source in the future," Energies, vol. 16, no. 4, Art. no. 1783, 2023, doi: 10.3390/en16041783. DOI: https://doi.org/10.3390/en16041783
[9] A. Thakur, A. Kumar, and E. Berdimurodov, "Biomass Wastes," in Biomass Wastes for Sustainable Industrial Applications: A Waste-to-Wealth Approach. Boca Raton, FL, USA: CRC Press, 2024, doi: 10.1201/9781003466833-4. DOI: https://doi.org/10.1201/9781003466833-2
[10] M. K. Jameel et al., "Biogas: Production, properties, applications, economic and challenges: A review," Results in Chemistry, vol. 7, Art. no. 101549, 2024, doi: 10.1016/j.rechem.2024.101549. DOI: https://doi.org/10.1016/j.rechem.2024.101549
[11] M. J. B. Kabeyi, O. A. Olanrewaju, and J. Akpan, "Biogas production and process control improvements," in From Biomass to Biobased Products. Rijeka, Croatia: IntechOpen, 2024, doi: 10.5772/intechopen.113061. DOI: https://doi.org/10.5772/intechopen.113061
[12] P. Satpathy, K. Wunder, M. Vaske, and S. Steinigeweg, "Converting sea weed to biogas energy: Utilization of synergism during co-digestion with maize silage," Energy Reports, vol. 8, pp. 1084–1093, Nov. 2022, doi: 10.1016/j.egyr.2022.11.002. DOI: https://doi.org/10.1016/j.egyr.2022.11.002
[13] S. V. Shah, B. Y. Lamba, A. K. Tiwari, and W.-H. Chen, "Sustainable biogas production via anaerobic digestion with focus on CSTR technology: A review," Journal of the Taiwan Institute of Chemical Engineers, vol. 162, Art. no. 105575, 2024, doi: 10.1016/j.jtice.2024.105575. DOI: https://doi.org/10.1016/j.jtice.2024.105575
[14] E. A. Mekonen, Y. T. Mekonnen, and S. O. Fatoba, "Thermodynamic prediction of biogas production and combustion: The spontaneity and energy conversion efficiency from photosynthesis to combustion," Scientific African, vol. 21, Art. no. e01776, 2023, doi: 10.1016/j.sciaf.2023.e01776. DOI: https://doi.org/10.1016/j.sciaf.2023.e01776
[15] A. Alengebawy, Y. Ran, A. I. Osman, K. Jin, M. Samer, and P. Ai, "Anaerobic digestion of agricultural waste for biogas production and sustainable bioenergy recovery: A review," Environmental Chemistry Letters, vol. 22, no. 6, pp. 2641–2668, 2024, doi: 10.1007/s10311-024-01789-1. DOI: https://doi.org/10.1007/s10311-024-01789-1
[16] C. E. Onu, C. N. Nweke, and J. T. Nwabanne, "Modeling of thermo-chemical pretreatment of yam peel substrate for biogas energy production: RSM, ANN, and ANFIS comparative approach," Applied Surface Science Advances, vol. 11, Oct. 2022, doi: 10.1016/j.apsadv.2022.100299. DOI: https://doi.org/10.1016/j.apsadv.2022.100299
[17] A. Wellinger, J. Murphy, and D. Baxter, Waste to Energy Conversion Technology. Cambridge, U.K.: Woodhead Publishing, 2013, doi: 10.1533/9780857097415. DOI: https://doi.org/10.1533/9780857097415
[18] G. Gadirli, A. A. Pilarska, J. Dach, K. Pilarski, A. Kolasa-Więcek, and K. Borowiak, "Fundamentals, operation and global prospects for the development of biogas plants—A review," Energies, vol. 17, no. 3, Art. no. 568, 2024, doi: 10.3390/en17030568. DOI: https://doi.org/10.3390/en17030568
[19] C. Rosales-López, "Los bioprocesos en la biotecnología: Uso de biorreactores para la producción y el escalamiento de productos de interés comercial," Revista Tecnología en Marcha, Sep. 2019, doi: 10.18845/tm.v32i9.4626. DOI: https://doi.org/10.18845/tm.v32i9.4626
[20] F. A. Ortega Quintana, H. Álvarez, and H. A. Botero Castro, "Enfrentando el modelado de bioprocesos: Una revisión de las metodologías de modelado," Revista ION, vol. 30, no. 1, pp. 73–90, Jul. 2017, doi: 10.18273/revion.v30n1-2017006. DOI: https://doi.org/10.18273/revion.v30n1-2017006
[21] V. L. Castillo, "Definición de bioprocesos: características y etapas," Enciclopedia.net, 2023. [Online]. Available: https://enciclopedia.net/bioprocesos/
[22] M. Khaleel, A. A. Ahmed, and A. Alsharif, "Artificial intelligence in engineering," Brilliance: Research of Artificial Intelligence, vol. 3, no. 1, pp. 32–42, 2023, doi: 10.47709/brilliance.v3i1.2170. DOI: https://doi.org/10.47709/brilliance.v3i1.2170
[23] W. Ertel, Introduction to Artificial Intelligence. Cham, Switzerland: Springer Nature, 2024, doi: 10.1007/978-3-658-43102-0. DOI: https://doi.org/10.1007/978-3-658-43102-0
[24] P. Ponce Cruz, Inteligencia Artificial: Con Aplicaciones a la Ingeniería, 2010. [Online]. Available: www.FreeLibros.me.
[25] R. Sissodia, M. S. Rauthan, V. Barthwal, and V. Dwivedi, "Fuzzy logic," in Recent Theories and Applications for Multi-Criteria Decision-Making. Hershey, PA, USA: IGI Global, 2024, p. 279, doi: 10.4018/979-8-3693-6502-1.ch010. DOI: https://doi.org/10.4018/979-8-3693-6502-1.ch010
[26] R. Saatchi, "Fuzzy logic concepts, developments and implementation," Information, vol. 15, no. 10, Art. no. 656, 2024, doi: 10.3390/info15100656. DOI: https://doi.org/10.3390/info15100656
[27] A. Varshney and V. Goyal, "Re-evaluation on fuzzy logic-controlled system by optimizing the membership functions," Materials Today: Proceedings, 2023, doi: 10.1016/j.matpr.2023.03.799. DOI: https://doi.org/10.1016/j.matpr.2023.03.799
[28] MATLAB, "Introducción a las redes neuronales." [Online]. Available: https://la.mathworks.com/discovery/neural-network.html
[29] P. Viñuela and I. Galván, Redes de Neuronas Artificiales: Un Enfoque Práctico, 2004. [Online]. Available: http://sedici.unlp.edu.ar/handle/10915/9491
[30] P. E. Saw and E. Song, "RNA: An Introduction," in RNA Therapeutics in Human Diseases. Singapore: Springer, 2025, pp. 23–45, doi: 10.1007/978-981-96-3041-7. DOI: https://doi.org/10.1007/978-981-96-3041-7_2
[31] J. Matich, "Cátedra: Informática Aplicada a la Ingeniería de Procesos – Orientación I. Redes Neuronales: Conceptos Básicos y Aplicaciones," 2001. [Online]. Available: https://gc.scalahed.com/recursos/files/r161r/w25199w/s5_redesneuronales.pdf
[32] V. M. Gerardo Ortiz Gallardo, A. González Ortiz, R. Ortiz Eleveño, G. M. Gómez Alvarado, C. A. Torres de Loera, and E. G. Hernández Montoya, Diagnóstico Tecnológico CEMIE-Bio Clúster Biocombustibles Gaseosos 2. Fondo Sectorial CONACYT-SENER-Sustentabilidad Energética, 2017. [Online]. Available: https://www.gob.mx/cms/uploads/attachment/file/329892/Diagnostico_Tecnologico_BIOGAS_Final.pdf
[33] H. López-Aguilar, A. Barrón, M. Franco, A. Paz, and A. Pérez-Hernández, "Modelación de la generación de metano a partir de suero lácteo y excreta de ganado en codigestión anaerobia," Nova Scientia, vol. 13, no. 27, 2021, doi: 10.21640/ns.v13i27.2791. DOI: https://doi.org/10.21640/ns.v13i27.2791
[34] A. A. Flores Hernández, M. Flores Montiel, J. Reyes Reyes, and C. M. Astorga Zaragoza, "Diseño y modelado de un bioreactor tipo batch y continuo para aplicaciones de control automático," 2013. [Online]. Available: https://www.researchgate.net/publication/277312156_Diseno_y_modelado_de_un_bioreactor_tipo_batch_y_continuo_para_aplicaciones_de_control_automatico
[35] N. Aros, M. Cifuentes, and J. Mardones, "Modelación, simulación y control de procesos de fermentación," 2011. [Online]. Available: https://revistas.uta.cl/pdf/1761/art06.pdf DOI: https://doi.org/10.4067/S0718-33052011000200006
[36] L. Alba and M. Varela, "Dinámica de un Biorreactor con Modelo de Crecimiento de Bacterias Lineal por Tramos," 2011. [Online]. Available: https://bffrepositorio.unal.edu.co/server/api/core/bitstreams/8d933a18-60e6-4bd7-a3b8-0fdbbfc0fb9c/content
[37] S. Whitaker, R. Flores-Estrella, A. Estrada-Baltazar, R. Femat, and L. Potosí, "A Mathematical Model and Dynamic Analysis of Anaerobic Digestion of Soluble Organic Fraction of Municipal Solid Waste Towards Control Design," 2016. [Online]. Available: www.rmiq.org
[38] J. A. Laghari, "How To Design Adaptive Neuro Fuzzy Inference System in MATLAB/Simulink? (Part-2)," YouTube, 2021. [Online]. Available: https://www.youtube.com/watch?v=W_8IncyvhbI&list=PLgowgr9LiYjrVQV-GolT5GawUIt-5_ZP-&index=2
Published
Data Availability Statement
We don't have made our research data available.
Issue
Section
Categories
License
Copyright (c) 2026 Alfredo Torres Lopez, Rodrigo Cadena Martinez, Martha Hernandez Ochoa
This work is licensed under a Creative Commons Attribution 4.0 International License.
The authors who publish in this journal accept the following conditions:
The authors retain the copyright and assign to the journal the right of the first publication, with the work registered with the Creative Commons Attribution license 4.0, which allows third parties to use what is published as long as they mention the authorship of the work and the first publication in this magazine.
Authors may make other independent and additional contractual agreements for the non-exclusive distribution of the version of the article published in this journal (eg, include it in an institutional repository or publish it in a book) as long as they clearly indicate that the work it was first published in this magazine.
Authors are allowed and encouraged to share their work online (for example: in institutional repositories or personal web pages) before and during the manuscript submission process, as it can lead to productive exchanges, greater and more quick citation of published work (see The Effect of Open Access).
How to Cite
