Statistical Modeling and Optimization of Biogas Yield under Different Seeding Conditions Using ANOVA and Response Surface Methodology

Authors

  • Andrew Ngbeneme Department of Mechanical Engineering, Faculty of Engineering, University of Delta, Agbor, Delta State, Nigeria.
  • Samuel Batet Department of Mechanical Engineering, Faculty of Engineering, Nigeria Maritime University, Okerenkoko, Delta State, Nigeria.
  • Efe Justic Igbagbon * Department of Mechanical Engineering, College of Engineering, Igbinedion University, Okada, Edo State, Nigeria. https://orcid.org/0009-0009-5464-549X

https://doi.org/10.22105/jeee.vi.60

Abstract

The optimization of Anaerobic Digestion (AD) is essential for improving renewable energy production and enhancing the sustainable management of biodegradable wastes. This study investigated the influence of different seeding conditions on biogas yield using Analysis of Variance (ANOVA) and Response Surface Methodology (RSM). Laboratory scale batch AD operated under mesophilic conditions (36–37 °C) were employed. The batch AD was used for the treatment of organic waste seeded with cow dung, organic waste seeded with Talinum triangulare, and an unseeded control. Biogas production was monitored through gas pressure measurements and flame combustion tests over the digestion period. Statistical analyses were performed to determine the significance of seeding conditions and to develop predictive models for process optimization. The results revealed that all seeded digesters enhanced biogas production compared with the unseeded system.  Also, cow dung exhibited the highest mean biogas pressure (10.01 psi), followed by Talinum triangulare (9.90 psi), while the control recorded the lowest value (9.38 psi). The RSM response surface and contour plots identified an optimum digestion period of approximately 20–35 days for maximum biogas generation. The developed models demonstrated satisfactory predictive capability and confirmed the significant interaction between retention time and seeding condition. The findings establish cow dung as the most effective inoculum for improving AD performance. 

Keywords:

Anaerobic digestion, Biogas yield, Response surface methodology, Analysis of variance, Seeding conditions, Cow dung inoculum

References

  1. [1] United Nations Environment Programme. (2024). Emissions gap report 2024. United Nations Environment Programme. https://www.unep.org/resources/emissions-gap-report-2024?gad_source=1&utm_source=chatgpt.com
  2. [2] Orhorhoro, E. K., Lindsay, E. E., & Oyejide, J. O. (2022). Analysis and evaluation of a three-stage anaerobic digestion plant for management of biodegradable municipal solid waste. International Journal of Engineering Research in Africa, 60, 75–87. https://doi.org/10.4028/p-a24obb
  3. [3] Orhorhoro, E. K., & Oyejide, J. O. (2020). Modelling of biogas yield from anaerobic co-digestion of food waste and animal manure using artificial neural networks. Applications of Modelling and Simulation, 4, 81–88. https://arqiipubl.com/ojs/index.php/AMS_Journal/article/view/118/81
  4. [4] Abbaspour, S. A. H., Das, A. A. B., Esmaeilion, M., & Haj, M. E. (2021). A critical review of biogas production and usage with legislations framework across the globe ministry of finance. International Journal of Environmental Science and Technology. https://doi.org/10.1007/s13762-021-03301-6
  5. [5] Kunatsa, T., & Xia, X. (2022). A review on anaerobic digestion with focus on the role of biomass co-digestion, modelling and optimisation on biogas production and enhancement. Bioresource Technology, 344, 126311. https://doi.org/10.1016/j.biortech.2021.126311
  6. [6] Orhorhoro, E. K., Oloaluwa, D. E., & Ezugwu, O. M. (2025). Optimizing biogas production by digestion and co-digestion of poultry waste at mesophilic temperature and PH. Journal of Environmental Engineering and Energy, 2(1), 31–43. https://doi.org/10.22105/jeee.v2i1.38
  7. [7] Lima, D., Appleby, G., & Li, L. (2023). A scoping review of options for increasing biogas production from sewage sludge: Challenges and opportunities for enhancing energy self-sufficiency in wastewater treatment plants. Energies, 16(5), 2369. https://doi.org/10.3390/en16052369
  8. [8] Molua, O. C., Ukpene, A. O., Ighrakpata, F. C., Nwachuku, D. N., Ogwu, D. A., & Edobor, M. (2023). Optimization of biogas production from tree waste materials for bioresource recovery. Fudma Journal of Sciences, 7(6), 209–213. https://doi.org/10.33003/fjs-2023-0706-2118
  9. [9] Orhorhoro, E. K., Ebunilo, P. O., & Ikpe, A. E. (2016). Effect of PH on anaerobic digestion (AD) of organic municipal solid waste in Benin City, Nigeria. Journal of the Nigerian Association Of Mathematical Physics, 36(1), 369–374. https://doi.org/10.1504/IJETM.2016.10004547
  10. [10] Almendrala, M. C., Valenzuela, K. A. T., Santos, S. M. N. B., & Avena Ardeta, L. G. S. (2025). Enhanced biogas production via anaerobic co-digestion of slaughterhouse and food waste using ferric oxide as a sustainable conductive material. ArXiv Preprint Arxiv:2505.04635. https://doi.org/10.48550/arXiv.2505.04635
  11. [11] Orhorhoro, E. K., Orhorhoro, O. W., & Ebunilo, P. O. (2016). Analysis of the effect of carbon/nitrogen (C/N) ratio on the performance of biogas yields for non-uniform multiple feed stock availability and composition in Nigeria. International Journal of Innovative Science, Engineering & Technology, 3(5), 119–126. https://www.researchgate.net/publication/330452485
  12. [12] Shin, C., Yoon, S., & Chung, Y. G. (2026). Multiscale, techno-economic evaluation of isoreticular series of CALF-20 for biogas upgrading using a pressure/vacuum swing adsorption (PVSA) process. Molecular Systems Design & Engineering. https://doi.org/10.1039/D5ME00131E
  13. [13] Wang, R., Hu, J., Yang, T., Jia, J., Ma, Z., Tan, S., & Yin, Q. (2025). Modeling, optimization, and mass-energy evaluation of a coupled hydrothermal carbonization-anaerobic digestion process assisted by machine learning. Energy, 138749. https://doi.org/10.1016/j.energy.2025.138749
  14. [14] Ojo, O. M. (2021). Temperature and PH effects on biogas yield from co-digestion of food waste and cow-dung. ABUAD Journal of Engineering Research and Development, 4(2), 10–17. https://www.researchgate.net/publication/357805794
  15. [15] Ogunniyi, O. J., Mbohwa, C., Onu, P., Chikumba, S., & Phuluwa, H. (2026). Optimization of biogas generation from an anaerobic digester: A review. Materials Proceedings, 31(1), 5. https://doi.org/10.3390/materproc2026031005
  16. [16] Orhorhoro, E. K., Ebunilo, P. O., & Sadjere, E. G. (2017). Development of a predictive model for biogas yield using artificial neural networks (ANNs) approach. American Journal of Energy and Power Engineering, 4(6), 71–77. http://www.aascit.org/journal/ajepe
  17. [17] Ling, J. Y. X., Chan, Y. J., Chen, J. W., Chong, D. J. S., Tan, A. L. L., Arumugasamy, S. K., & Lau, P. L. (2024). Machine learning methods for the modelling and optimisation of biogas production from anaerobic digestion: A review. Environmental Science and Pollution Research, 31(13), 19085–19104. https://doi.org/10.1007/s11356-024-32435-6%0A%0A
  18. [18] Kegl, T., Jiménez, E. T., Kegl, B., Kralj, A. K., & Kegl, M. (2025). Modeling and optimization of anaerobic digestion technology: Current status and future outlook. Progress in Energy and Combustion Science, 106, 101199. https://doi.org/10.1016/j.pecs.2024.101199
  19. [19] Blanco, V., Hinojosa, Y., & Zavala, V. M. (2024). The waste-to-biomethane logistic problem: A mathematical optimization approach. ACS Sustainable Chemistry & Engineering, 12(22), 8453–8466. https://doi.org/10.1021/acssuschemeng.4c01429
  20. [20] Orhorhoro, E. K., Ebunilo, P. O. B., & Sadjere, E. G. (2017). Design of bio-waste grinding machine for anaerobic digestion (AD) system. European Journal of Advances in Engineering And Technology, 4(7), 560–568. https://www.researchgate.net/profile/Ejiroghene-Orhorhoro/publication/335224804
  21. [21] Zupančič, M., Možic, V., Može, M., Cimerman, F., & Golobič, I. (2022). Current status and review of waste-to-biogas conversion for selected European countries and worldwide. Sustainability, 14(3), 1823. https://doi.org/10.3390/su14031823
  22. [22] Adeleke, A. J., Ajunwa, O. M., Golden, J. A., Antia, U. E., Adesulu Dahunsi, A. T., Adewara, O. A., & Luka, Y. (2023). Anaerobic digestion technology for biogas production: Current situation in Nigeria (A review). UMYU Journal of Microbiology Research, 8(2), 153-164. https://doi.org/10.47430/ujmr.2382.018?urlappend=%3Futm_source%3Dresearchgate.net%26utm_me
  23. [23] Nwokolo, N., Mukumba, P., Obileke, K., & Enebe, M. (2020). Waste to energy: A focus on the impact of substrate type in biogas production. Processes, 8(10), 1224. https://doi.org/10.3390/pr8101224
  24. [24] Ebunilo, P. O., Ukwuaba, S. I., Owunna, I. B., Sadgere, E. G., & Orhorhoro, E. K. (2016). Evaluation of cow dung and talinum triangulare as a seeding agent for the production of biogas from domestic wastes in warri metropolis. International Journal of Scientific and Engineering Research, 7(3), 633–641. https://afribary.com/works/evaluation-of-cow-dung-and-talinum-triangulare-as-a-seeding-agent-for-the-production-of-biogas-from-domestic-wastes-in-warri-metropolis-1
  25. [25] Orhorhoro, E. K., Ebunilo, P. O., & Sadjere, G. E. (2017). Experimental determination of effect of total solid (TS) and volatile solid (VS) on biogas yield. American Journal of Modern Energy, 3(6), 131–135. https://doi.org/10.11648/j.ajme.20170306.13
  26. [26] Orhorhoro, E. K., Ebunilo, P. O., & Sadjere, G. E. (2018). Effect of organic loading rate (OLR) on biogas yield using a single and three-stages continuous anaerobic digestion reactors. International Journal of Engineering Research in Africa, 39, 147–155. https://doi.org/10.4028/www.scientific.net/JERA.39.147
  27. [27] Orhorhoro, E. K., Orhorhoro, O. W., & Eruero, V. A. (2018). Performance evaluation of design AD system biogas purification filter. International Journal of Mathematical, Engineering and Management Sciences, 3(1), 17. https://dx.doi.org/10.33889/IJMEMS.2018.3.1-003
  28. [28] FAO. (2024). The state of food and agriculture 2024: Value-driven transformation of agrifood systems. Food and Agriculture Organization of the United Nations. https://doi.org/10.4060/cd2616en
  29. [29] Abdurrakhman, A., Sutiarso, L., Ainuri, M., Ushada, M., & Islam, M. P. (2025). A multilayer perceptron feedforward neural network and particle swarm optimization algorithm for optimizing biogas production. Energies, 18(4), 1002. https://doi.org/10.3390/en18041002
  30. [30] Gao, Z., Ren, Z., Cui, T., & Fu, Y. (2025). Machine learning-based analysis of microplastic-induced changes in anaerobic digestion parameters influencing methane yield. Journal of Environmental Management, 377, 124627. https://doi.org/10.1016/j.jenvman.2025.124627
  31. [31] Şenol, H., & Çolak, E. (2025). Optimizing methane production from lignocellulosic biomass: Low-temperature potassium ferrate pretreatment via response surface methodology. Processes, 13(9), 2768. https://doi.org/10.3390/pr13092768
  32. [32] Marycz, M., Turowska, I., Glazik, S., & Jasiński, P. (2025). Artificial intelligence in anaerobic digestion: A review of sensors, modeling approaches, and optimization strategies. Sensors, 25(22), 6961. https://doi.org/10.3390/s25226961
  33. [33] Castillejos Yuca, D. A., Luna, Y. D. C. P., Roque, Y. S., Trinidad, S. S., Hernández, R. B., Carballo, A. G. Z., & De Paz, P. L. (2025). Optimization of anaerobic digestion processes of organic waste in chiapas, Mexico, for biogas production. The Open Biotechnology Journal, 19(1), http://dx.doi.org/10.2174/0118740707389877250620070725
  34. [34] Bidiko, G. B., Sangib, E. B., & Gnaro, M. A. (2025). Optimization of biogas production through co-digestion of cafeteria food waste and cow dung using the response surface methodology. Frontiers in Energy Research, 13, 1568478. https://doi.org/10.3389/fenrg.2025.1568478
  35. [35] Montgomery, D. C. (2017). Design and analysis of experiments. John Wiley & Sons. https://www.scirp.org/reference/referencespapers?referenceid=2625248
  36. [36] Zhan, Y., & Zhu, J. (2024). Response surface methodology and artificial neural network-genetic algorithm for modeling and optimization of bioenergy production from biochar-improved anaerobic digestion. Applied Energy, 355, 122336. https://doi.org/10.1016/j.apenergy.2023.122336
  37. [37] Ao, T.J., Liu, C.G., Sun, Z.Y., Zhao, X.Q., Tang, Y.Q., & Bai, F.W. (2024). Anaerobic digestion integrated with microbial electrolysis cell to enhance biogas production and upgrading in situ. Biotechnology Advances, 73, 108372. https://doi.org/10.1016/j.biotechadv.2024.108372
  38. [38] Sidi Habib, S., Torri, S., & Mol S, K. (2024). New methodologies for the optimization of operational parameters of bio gas power plants: A review. Journal of Renewable Energy and Environment, 11(4), 9–27. https://doi.org/10.30501/jree.2024.444163.1840
  39. [39] Maakoul, O., & Boulal, A. (2025). Experimental and numerical optimization of biogas energy production using the RSM method. International Journal of Energy Production and Management (IJEPM), 10(3), 491–502. https://doi.org/10.56578/ijepm100311
  40. [40] Basak, S. R., Nury, A. H., Swarup, S. Das, Alam, M. J. Bin, & Kabir, M. I. (2025). Optimizing biogas production through the co-digestion of tannery fleshing, cowdung, and sewage water using response surface methodology. Cleaner Waste Systems, 12, 100332. https://doi.org/10.1016/j.clwas.2025.100332
  41. [41] Bensegueni, C., Kheireddine, B., Khalfaoui, A., Amrouci, Z., Bouznada, M. O., & Derbal, K. (2025). Optimization of biogas and biomethane yield from anaerobic conversion of pepper waste using response surface methodology. Sustainability, 17(6), 2688. https://doi.org/10.3390/su17062688
  42. [42] Alsebiey, M. M., Hatem, M. H., Bauomy, Y., & Tarabye, H. H. (2025). Biogas production: Effect of stirring speed and stirring/break periods on Anaerobic co-digestion of sugarcane bagasse and cow manure. Aswan Journal of Agricultural and Biological Sciences, 1(1), 49–63. https://doi.org/10.21608/alexja.2025.312456.1043

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Published

2026-03-10

Issue

Vol. 3 No. 1 (2026): Journal of Environmental Engineering and Energy (JEEE)

Section

Articles

How to Cite

Ngbeneme, A. ., Batet, S. ., & Igbagbon, E. J. . (2026). Statistical Modeling and Optimization of Biogas Yield under Different Seeding Conditions Using ANOVA and Response Surface Methodology. Journal of Environmental Engineering and Energy, 3(1), 23-34. https://doi.org/10.22105/jeee.vi.60

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