Prediction of Biogas Production from Agriculture Waste Biomass Based on Backpropagation Neural Network

Open Access

Issue		BIO Web Conf. Volume 165, 2025 The 8^th International Conference on Green Agro-Industry and Bioeconomy (ICGAB 2024)


Article Number		06001
Number of page(s)		12
Section		Renewable Energy and Biorefinery
DOI		https://doi.org/10.1051/bioconf/202516506001
Published online		07 March 2025

M. J. B. Kabeyi, O. Oludolapo Akanni, and A. Joseph, Biogas Production and Process Control Improvements, Intech, vol. i, no. tourism, p. 15, (2016) [Online] [Google Scholar]
J. Frankowski and W. Czekała, Agricultural Plant Residues as Potential Co-Substrates for Biogas Production, Energies. 16, 11, (2023). https://doi.org/10.3390/en16114396 [Google Scholar]
K. Gioulounta, M. Matska, A. Piskilopoulos, and K. Stamatelatou, Greenhouse Residues’ Potential for Biogas Production. Appl. Sci. 13, 9 (2023). https://doi.org/10.3390/app13095445 [CrossRef] [Google Scholar]
C. Wang, H. Zhang, J. Liu, J. Sun, and L. Zhao, Effect of hydrothermal carbon on biogas production from agricultural waste by anaerobic fermentation. Highlights Sci. Eng. Technol. 90, 1 (2024). https://doi.org/10.54097/p2jtyp22 [CrossRef] [Google Scholar]
B. Singgih, B. Triono, N. Rusdi, M. Ulinuhayani, P. Nurhidayat, and M. Soedarjo, Financial Aspects Of Biogas Production From Livestock Waste To Meet Household Energy Needs. Proc. 1st Int. Conf. Soc. Sci. 3, 1 (2024). https://doi.org/10.59188/icss.v3i1.207 [Google Scholar]
N. Nwokolo, P. Mukumba, K. Obileke, and M. Enebe, Waste to energy: A focus on the impact of substrate type in biogas production, Processes. 8, 10 (2020). https://doi.org/10.3390/pr8101224 [CrossRef] [Google Scholar]
M. Saraswat, M. Garg, M. Bhardwaj, M. Mehrotra, and R. Singhal, Impact of variables affecting biogas production from biomass, IOP Conf. Ser. Mater. Sci. Eng. 691, 1 (2019). https://doi.org/10.1088/1757-899X/691/1/012043 [Google Scholar]
A. Kasinath, S. Fudala-Ksiazek, M. Szopinska, H. Bylinski, W. Artichowicz, A. Remiszewska-Skwarek, A. Luczkiewicz, Biomass in biogas production : Pretreatment and codigestion. Renewable and Sustainable Energy Reviews. 150, 1 (2021). https://doi.org/10.1016/j.rser.2021.111509 [CrossRef] [Google Scholar]
Y. Zhang, L. Li, Z. Ren, Y. Yu, Y. Li, J. Pan, Y. Lu, L. Feng, W. Zhang, and Y. Han, Plant-scale biogas production prediction based on multiple hybrid machine learning technique. Bioresour. Technol. 363, 1 (2022). https://doi.org/10.1016/j.biortech.2022.127899. [Google Scholar]
K. W. Seo, J. Seo, K. Kim, S. Ji Lim, and J. Chung, Prediction of biogas production rate from dry anaerobic digestion of food waste: Process-based approach vs. recurrent neural network black-box model. Bioresour. Technol. 341, 1 (2021). https://doi.org/10.1016/j.biortech.2021.125829 [Google Scholar]
Y. Sun, H. liang Dai, H. Moayedi, B. Nguyen Le, and R. Muhammad Adnan, Predicting steady-state biogas production from waste using advanced machine learningmetaheuristic approaches, Fuel, 355, 1 (2024). https://doi.org/10.1016/j.fuel.2023.129493. [Google Scholar]
S. Damadi, G. Moharrer, M. Cham, and J. Shen, The Backpropagation algorithm for a math student. Proc. Int. Jt. Conf. Neural Networks. (2023). https://doi.org/10.1109/IJCNN54540.2023.10191596. [Google Scholar]
S. Ozbay, Modified Backpropagation Algorithm with Multiplicative Calculus in Neural Networks. Elektron. ir Elektrotechnika, 29, 3 (2023). https://doi.org/10.5755/j02.eie.34105 [CrossRef] [Google Scholar]
N. A. Mohammed and A. Al-Bazi, An adaptive backpropagation algorithm for longterm electricity load forecasting. Neural Comput. Appl. 34, 1 (2022). https://doi.org/10.1007/s00521-021-06384-x [Google Scholar]
G. P. Zhang and D. M. Kline, “Quarterly time-series forecasting with neural networks. IEEE Trans. Neural Networks. 18, 6 (2007). https://doi.org/10.1109/TNN.2007.896859 [Google Scholar]
S. Y. Heng, W. M. Ridwan, P. Kumar, A. N. Ahmed, C. M. Fai, A. H. Birima, and A. El-Shafie, Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction. Sci. Rep. 12, 1 (2022). https://doi.org/10.1038/s41598-022-13532-3 [CrossRef] [Google Scholar]
M. Kiris, I. Demir, and N. Simsek, Comparative Analysis of Neural Networks in the Diagnosis of Emerging Diseases based on COVID-19. Konuralp J. Math. 9, 2 (2021) [Google Scholar]
A. Mosca and G. D. Magoulas, Adapting Resilient Propagation for Deep Learning, in Proceedings of the UK workshop on Computational Intelligence 2015 (UKCI), September (2015) [Google Scholar]
A. G. Asuero, A. Sayago, and A. G. González, The correlation coefficient: An overview. Crit. Rev. Anal. Chem. 36, 1, (2006). https://doi.org/10.1080/10408340500526766 [Google Scholar]
V. Plevris, G. Solorzano, N. P. Bakas, and M. E. A. Ben Seghier, Investigation of Performance Metrics in Regression Analysis and Machine Learning-Based Prediction Models. World Congr. Comput. Mech. ECCOMAS Congr. (2022) [Google Scholar]
M. F. and E. S., Prediction discrepancies for the evaluation of nonlinear mixed-effects models. J. Pharmacokinet. Pharmacodyn. 33, 3. (2006). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.