Parametric Analysis and ANN Prediction of Biogas Yield from Anaerobic Biochemical Reactions of Non-Uniform Organic Substrates under Mesophilic Regime

Aniekan Essienubong Ikpe; Imoh Ime Ekanem; Michael Okon  Bassey

doi:10.31181/sor41202759

Authors

Aniekan Essienubong Ikpe Department of Mechanical Engineering Technology, School of Engineering, Akwa Ibom State Polytechnic, Ikot Osurua, Nigeria Author https://orcid.org/0000-0001-9069-9676
Imoh Ime Ekanem Department of Mechanical Engineering Technology, School of Engineering, Akwa Ibom State Polytechnic, Ikot Osurua, Nigeria Author https://orcid.org/0000-0002-8973-9260
Michael Okon Bassey Department of Mechatronics Engineering Technology, School of Engineering, Akwa Ibom State Polytechnic, Ikot Osurua, Nigeria Author https://orcid.org/0000-0001-5433-7889

DOI:

https://doi.org/10.31181/sor41202759

Keywords:

Biogas Yield, Bio-digester, Anaerobic digestion, Organic substrates, ANN prediction, Mesophilic regime, Renewable energy, Waste management

Abstract

In an attempt to acquire affordable energy alternatives, without deviating from sustainable waste management goals, researchers in recent times have explored a number of options to expand the frontiers of research on biogas recovery. Thus, this study explores the optimization of biogas production through mesophilic anaerobic digestion (AD) of heterogeneous, non-uniform substrates. By comparing mono-digestion with co-digestion techniques, a thorough parametric analysis experimentally assessed the effects of temperature, moisture content, and pH on biogas yield. Under these intricate and fluctuating substrate conditions, an ANN model was developed to predict the biogas-yielding output from the same experimental conditions. The results of the experiment revealed different ideal temperature ranges: co-digestion produced larger yields throughout a wider range of 28.2–30.6°C, whereas mono-digestion peaked between 26°C and 28°C. Mono-digestion produced a maximum of 0.22 g/day at 26.4–29.3°C at 15% moisture content, whereas co-digestion increased production to 0.26 g/day at 28.2–31.6°C. A maximum experimental output of 0.26 g/day for co-digestion was found by pH adjustment. The ANN results showed remarkable accuracy, nearly matching pH-driven co-digestion yields (prediction: 0.27 g/day vs. experimental: 0.26 g/day) and reproducing experimental moisture-driven yields (0.22 g/day for mono-digestion). This demonstrates how well the model captures the non-linear interactions present in the digestion of various feedstock. It was further observed that biogas yield and temperature increase in pari passu. Biogas yield was also observed to be significant at pH values within the neutral range, indicating rich substrate content and a favourable bio-digester environment for microbial activities. Comparatively, an increase in moisture content led to a more significant biogas yield than other experimental conditions. The study unequivocally demonstrates that co-digestion is one of the best methods for optimizing biogas production from irregular substrates when operating under mesophilic conditions. An important contribution to the sustainable management of various organic wastes was achieved by validating the experimental outputs with ANN predictions, which offers a trustworthy predictive tool for maximizing AD performance.

Downloads

Download data is not yet available.

References

Kabeyi, M. J. B., & Olanrewaju, O. A. (2022). Biogas production and applications in the sustainable energy transition. Journal of Energy, 2022(1), 8750221. https://doi.org/10.1155/2022/8750221

Rodrigues, B. C. G., de Mello, B. S., Grangeiro, L. C., Dussan, K. J., & Sarti, A. (2025). The most important technologies and highlights for biogas production worldwide. Journal of the Air & Waste Management Association, 75(2), 87-108. https://doi.org/10.1080/10962247.2024.2393192

Hidalgo, D., Urueña, A., Martín-Marroquín, J. M., & Díez, D. (2025). Integrated approach for biomass conversion using thermochemical routes with anaerobic digestion and syngas fermentation. Sustainability, 17(8), 3615. https://doi.org/10.3390/su17083615

Kim, D., & Kim, J. (2025). Predicting biogas production from organic waste through anaerobic co-digestion. Journal of Cleaner Production, 496, 145122. https://doi.org/10.1016/j.jclepro.2025.145122

Khaliq, H., Younas, M., Sharif, M., Hussain, S., Younas, U., Sajid, M., Qudratullah, M. F., Khan, M. U., & Ullah, Q. (2025). Comparative analysis of biogas production efficiency from anaerobic digestion of cow dung and mixed dung substrates. Sarhad Journal of Agriculture, 41(2), 881-893. https://dx.doi.org/10.17582/journal.sja/2025/41.2.881.893

Ćurčić, S., Milićević, D., Kilibarda, N., & Peulić, A. (2025). Assessing biogas production potential from organic waste and livestock byproducts in a Serbian municipality: Implications for sustainable food systems. Sustainability, 17(7), 3144. https://doi.org/10.3390/su17073144

Koval, V., Atstāja, D., Filipishyna, L., Udovychenko, V., Kryshtal, H., & Gontaruk, Y. (2025). Sustainability assessment and resource utilization of agro-processing waste in biogas energy production. Climate, 13(5), 99. https://doi.org/10.3390/cli13050099

Shafana F. M., Muñoz, R., Narayanan, R., & García-Depraect, O. (2025). Enhancing Bioplastic Degradation in Anaerobic Digestion: A Review of Pretreatment and Co-Digestion Strategies. Polymers, 17(13), 1756. https://doi.org/10.3390/polym17131756

Ikpe, A. E., Nkom, E. W., & Bassey, M. O. (2025). Comparative Experimental Analysis of Biogas Recovery Rate from Anaerobic Digestion of Single and Multiple Organic Feedstock. International Journal of Advanced Scientific and Technical Research 15(4), 240-253. https://dx.doi.org/10.5281/zenodo.16900447

Osei-Owusu, B. A., Arthur, R., Baidoo, M. F., Oduro-Kwarteng, S., & Amenaghawon, A. N. (2024). Anaerobic co-digestion of human excreta, food leftovers and kitchen residue: 1 ternary mixture design, synergistic effects and RSM approach. Heliyon, 10, e24080.

Ebubechi, O. D., Rapheal, A. A., Alain, Z. F., Fiyinfoluwa, A. M., Okechi, I. N. F., Nuhu, A. H., Bethany, W. T., Joseph, E. O., Kaycee, A. K., Oluwakorede, O. E., Divine, D. U., Aduragbmi, O. S., Omomolawa, O. E., Goodluck, O. A., Emmanuel, O. T., & Ubanioshave, A. S. (2024). Enhancing Anaerobic Co-Digestion with Locally Sourced Biochar: A Kinetic Analysis of Biogas Production Efficiency. Progress in Chemical and Biochemical Research, 7(3), 305-322. https://doi.org/10.48309/PCBR.2024.453684.1351

Ikpe, A. E., Ekanem, K. R., & Bassey, M. O. (2024). Experimental investigation of biogas yielding rate from anaerobic codigestion of multiple organic feedstocks using a bio-digester. Journal of Advances in Environmental Health Research, 12(4), 228-239. https://doi.org/10.34172/jaehr.1354

Ona, I. J., & Agogo, H. O. (2022). Biogas Production from the Co-Digestion of Rice straw with Cow Dung and Piggery Manure. ChemSearch Journal 13(1), 1–8.

Ofon, U. A., Ndubuisi-Nnaji, U. U., Udo, I. M., Udofia, E. S., Fatunla, O. K., & Shaibu, S. E. (2024). Biogas Production Potential from Anaerobic Co-Digestion of Food Waste and Animal Manure. Journal of Chemical Society Nigeria, 49(1), 079–093. https://doi.org/10.46602/jcsn.v49i1.949

Szaja, A., Czarnota, J., Masłoń, A., & Lebiocka, M. (2025). An Effective Energetic Application of Orange Waste in Multi-Component Co-Digestion with Municipal Sewage Sludge. Applied Sciences, 15, 1537. https://doi.org/10.3390/app15031537

Oladejo, O. S., Ogunbunmi, S. I., Sanni, K. A., Olanrewaju, O. F., & Azeez, Y. O. (2025). Evaluation of Biogas Yield from Co-Digestion of Varying Particle Sizes of Corncob with Poultry Manure and Process Parameters Optimization Study. LAUTECH Journal of Engineering and Technology, 19(2), 10-20. https://doi.org/10.36108/laujet/5202.91.0220

Ikpe, A. E., Ndon, A. E., & Etim, P. J. (2020). Fuzzy Modelling and Optimization of Anaerobic Co-Digestion Process Parameters for Effective Biogas Yield from Bio-Wastes. The International Journal of Energy & Engineering Sciences, 5(2), 43-61.

Kana, E. B. G., Oloke, J. K., Lateef, A., & Adesiyan, M. O. (2012). Modelling and optimization of biogas production on saw dust and other co-substrates using Artificial Neural network and Genetic Algorithm. Renewable Energy, 46, 276-281. https://doi.org/10.1016/j.renene.2012.03.027

Tufaner, F., Dalkılıç, K., & Uğurlu, A. (2025). Artificial intelligence-based modelling of biogas production in a combined microbial electrolysis cell-anaerobic digestion system using artificial neural networks and adaptive neuro-fuzzy inference system. Environmental Science and Pollution Research, 32, 12524-12546. https://doi.org/10.3390/app15031537

Machineni, L., & Anupoju, G. R. (2023). Optimization of biomethane production from sweet sorghum bagasse using artificial neural networks combined with particle swarm algorithm. Environmental Science and Pollution Research, 30, 114095-114110. https://doi.org/10.1007/s11356-023-30451-6

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

Kumar, S., Kumar, D. R., Sharma, D., & Wipulanusat, W. (2025). Machine learning-based optimization of biogas and methane yields in UASB reactors for treating domestic wastewater. Biodegradation, 36(4), 1-21. https://doi.org/10.1007/s10532-025-10152-2

El-Mesery, H. S., Omran, A. A., Adelusi, O. A., Kaveh, M., Okechukwu, V. O., Hu, Z., & Badawy, S. A. (2025). Evaluating and predicting the impact of storage conditions and packaging materials on the physical properties of paddy rice using machine learning approaches and artificial neural networks. Journal of Stored Products Research, 112, 102659. https://doi.org/10.1016/j.jspr.2025.102659

Khatir, A., Capozucca, R., Khatir, S., Magagnini, E., Le Thanh, C., & Riahi, M. K. (2025). Advancements and emerging trends in integrating machine learning and deep learning for SHM in mechanical and civil engineering: a comprehensive review. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 47(9), 1-34. https://doi.org/10.1007/s40430-025-05697-5

Chang, P. Y., Chan, Y. J., Arumugasamy, S. K., Wan, Y. K., & Lim, J. W. (2025). Optimisation of anaerobic digestion of palm oil mill effluent with biochar addition: Synergistic application of Artificial neural network and response Surface Methodology. Fuel, 398, 135514. https://doi.org/10.1016/j.fuel.2025.135514

Rutland, H., You, J., Liu, H., & Bowman, K. (2025). Application of machine learning for FOS/TAC soft sensing in bio-electrochemical anaerobic digestion. Molecules, 30(5), 1092. https://doi.org/10.3390/molecules30051092

Mohamed, Y., Elghadban, A., Lei, H. I., Shih, A. A., & Lee, P. H. (2025). Quantum machine learning regression optimisation for full-scale sewage sludge anaerobic digestion. npj Clean Water, 8(1), 17. https://doi.org/10.1038/s41545-025-00440-y

Ahmad, A., Yadav, A. K., Singh, A., & Singh, D. K. (2024). A comprehensive machine learning-coupled response surface methodology approach for predictive modelling and optimization of biogas potential in anaerobic Co-digestion of organic waste. Biomass and Bioenergy, 180, 106995. https://doi.org/10.1016/j.biombioe.2023.106995

Dybiński, O., Kurkus, T., Szablowski, L., Szczęśniak, A., Milewski, J., Martsinchyk, A., & Shuhayeu, P. (2025). Artificial Neural Network-Based Mathematical Model of Methanol Steam Reforming on the Anode of Molten Carbonate Fuel Cell Based on Experimental Research. Energies, 18(11), 2901. https://doi.org/10.3390/en18112901

Ishaq, M. I., Halliru, I., Olaoye, D. B., Barka, J. D., & Ibrahim, Y. (2024). Construction and Performance Evaluation of Locally Built PVC-Based Biogas Digesters. ASRIC Journal on Engineering Sciences, 5(1), 28-40.

Ikpe, A. E., Imonitie, D. I., & Ndon, A. E. (2019). Investigation of Biogas Energy Derivation from Anaerobic Digestion of Different Local Food Wastes in Nigeria. Academic Platform Journal of Engineering and Science, 7(2), 332-340. https://doi.org/10.21541/apjes.441166

Ebunilo, P. O., Okovido, J., & Ikpe, A. E. (2018). Investigation of the energy (biogas) production from co-digestion of organic waste materials. International Journal of Energy Applications and Technologies, 5(2), 68-75.

Eronmosele, P., Ejiro T. A., & Ikpe A. E. (2020). Comparative Study of the Kinetics of Biogas Yield from the Co-digestion of Poultry Droppings with Waterleaf and Poultry Droppings with Elephant Grass. Engineering Sciences, 15(3), 139-150.

Ikpe, A. E., Ndon, A. E., & Etim, P. J. (2020). A Conceptual Framework for Bio-thermal Variations in Municipal Solid Waste Landfill Under Mesophilic Temperature Regime. International Journal of Computational and Experimental Science and Engineering, 6(3), 152-164.