Prediction of biogas yield from anaerobic digesters using meta-regression

• Autorzy: Organiściak Patryk , Turek Anna , Stęchły Arkadiusz , Rysz Joanna , Vanivska Veronika , Masłoń Adam , Wadiak Bartosz

Identyfikatory

DOI

10.5604/01.3001.0055.0610

• Języki publikacji: EN

Abstrakty

EN

The production of biogas in wastewater treatment plants (WWTPs), often considered critical facilities, is a significant element of energy and environmental security. Given increasing demands to reduce greenhouse gas emissions and the pursuit of energy self-sufficiency, the role of biogas in the energy sector keeps steadily growing, granting it strategic potential. In Poland and other European Union countries, biogas production is supported by policies promoting renewable energy sources, enhancing its importance in the energy transition process. This study analyses biogas yields and their impact on achieving energy and environmental security goals. Additionally, the use of meta-regression methods and machine learning aims to improve biogas yield prediction based on a range of proces parameters.

Słowa kluczowe

EN

biogas yield; sewage sludge; biogas anaerobic fermentation; machine learning; meta regression; ensemble stacking; energy security

• Wydawca: Szkoła Główna Służby Pożarniczej
• Czasopismo: Zeszyty Naukowe SGSP / Szkoła Główna Służby Pożarniczej
• Rocznik: 2025
• Tom: Nr 93 (tom 1)
• Strony: 81--100
• Opis fizyczny: Bibliogr. 37 poz., rys., tab.

Twórcy

autor

Organiściak Patryk

• Rzeszow University of Technology, Faculty of Electrical and Computer Engineering, Department of Complex Systems, Rzeszów, Poland

• https://orcid.org/0000-0002-5277-4038

autor

Turek Anna

• Rzeszow University of Technology, Faculty of Electrical and Computer Engineering, Department of Complex Systems, Rzeszów, Poland

autor

Stęchły Arkadiusz

• Rzeszow University of Technology, Faculty of Electrical and Computer Engineering, Department of Complex Systems, Rzeszów, Poland

• https://orcid.org/0009-0006-5200-761X

autor

Rysz Joanna

• Rzeszow University of Technology, Faculty of Electrical and Computer Engineering, Department of Complex Systems, Rzeszów, Poland

• https://orcid.org/0000-0001-5104-2524

autor

Vanivska Veronika

• Rzeszow University of Technology, Faculty of Electrical and Computer Engineering, Department of Complex Systems, Rzeszów, Poland

• https://orcid.org/0009-0000-0864-3420

autor

Masłoń Adam

• Rzeszow University of Technology, Faculty of Civil and Environmental Engineering and Architecture, Department of Environmental Engineering and Chemistry, Rzeszow, Poland

• https://orcid.org/0000-0002-3676-0031

autor

Wadiak Bartosz

• Municipal Water and Sewage Company Sp. z o.o. in Rzeszów, Rzeszów, Poland

Bibliografia

• [1] Alayi, R., Shamel, A., Kasaeian, A., Harasii, H., and Topchlar, M.A., (2016). The role of biogas to sustainable development (aspects environmental, security and economic). Journal of Chemical and Pharmaceutical Research, 8, 4, pp. 112–118.
• [2] Bachmann, N., la Cour Jansen, J., Bochmann, G., and Montpart, N., (2015). Sustainable biogas production in municipal wastewater treatment plants. Dublin (Ireland): IEA Bioenergy.
• [3] Banks, C.J., Chesshire, M., Heaven, S., and Arnold, R., (2011). Anaerobic digestion of sourcesegregated domestic food waste: Performance assessment by mass and energy balance. Bioresource Technology, 102, 2, pp. 612–620. DOI: 10.1016/j.biortech.2010.08.005
• [4] Bartkiewicz, B., Krzosek, P., Flis, M., and Golba, K., (2016). Zagrożenia dla środowiska ze strony obiektów ochrony środowiska. Cz. 2. Charakterystyka występujących zagrożeń i możliwości ich ograniczenia. Zeszyty Naukowe SGSP, 60, 4, pp. 59–77.
• [5] Bougrier, C., Delgenes, J.-P., and Carrère, H., (2006). Combination of thermal treatments and anaerobic digestion to reduce sewage sludge quantity and improve biogas yield. Process Safety and Environmental Protection, 84, 4, pp. 280–284.
• [6] Breiman, L., (1996). Bagging predictors. Machine learning, 24, pp. 123–140.
• [7] Breiman, L., (2001). Random forests. Machine learning, 45, pp. 5–32.
• [8] Breiman, L., (2017). Classification and regression trees. Chapman and Hall/CRC. https://doi.org/10.1201/9781315139470
• [9] Chmielowiec, A., Sikorska-Czupryna, S., Klich, L., Woś, W., and Kuraś, P., (2024). Optimization of quality control processes using the NPGA genetic algorithm. Advances in Science and Technology Research Journal, 18, 7, pp. 264–276. https://doi.org/10.12913/22998624/193195
• [10] Farzin, F., Moghaddam, S.S., and Ehteshami, M., (2024). Auto-tuning data-driven model for biogas yield prediction from anaerobic digestion of sewage sludge at the south-tehran wastewater treatment plant: Feature selection and hyperparameter population-based optimization. Renewable Energy, 227, p. 120554. https://doi.org/10.1016/j.renene.2024.120554
• [11] Francés, G.E., Marín-Quemada, J.M., and González, E.S.M., (2013). RES and risk: Renewable energy’s contribution to energy security. A portfolio-based approach. Renewable and Sustainable energy Reviews, 26, pp. 549–559. https://doi.org/10.1016/j.rser.2013.06.015
• [12] Friedman, J.H., (2001). Greedy function approximation: a gradient boosting machine. Annals of Statistics, 29(5), pp. 1189–1232.
• [13] Geurts, P., Ernst, D., and Wehenkel, L., (2006). Extremely randomized trees. Machine learning, 63, pp. 3–42.
• [14] GUS, (2013). Energia ze źródeł odnawialnych w 2012 r. Warsaw: GUS.
• [15] Hamelin, L., Møller, H.B., and Jørgensen, U., (2021). Harnessing the full potential of biomethane towards tomorrow’s bioeconomy: A national case study coupling sustainable agricultural intensification, emerging biogas technologies and energy system analysis. Renewable and Sustainable Energy Reviews, 138, p. 110506. https://doi.org/10.1016/j.rser.2020.110506
• [16] Igliński, B., Buczkowski, R., and Cichosz, M., (2015). Biogas production in Poland – Current state, potential and perspectives. Renewable and Sustainable Energy Reviews, 50, pp. 686–695. https://doi.org/10.1016/j.rser.2015.05.013
• [17] Jaysingpure, V.D. and Khobragade, M.U., (2024). Optimization of Parameters Responsible for the Rate of Gas Generation Through Mixed Anaerobic Digestion. Advances in Science and Technology. Research Journal, 18, 8, pp. 256–271. DOI: 10.12913/22998624/194566
• [18] Krupa, K., (2015). Zrównoważona energetyka biogazowa w oczyszczalniach ścieków. Polityka Energetyczna, 18, 4, pp. 101–112.
• [19] Kunatsa, T. and 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, p. 126311. https://doi.org/10.1016/j.biortech.2021.126311
• [20] Kuraś, P., Organiściak, P., Kowal, B., Vanivska, V., Smalara, K., Demidowski, K., and Krawiec, P., (2024). IoT communication security: challenges and solutions. Zeszyty Naukowe SGSP, 89(2), pp. 69–90. DOI: doi.org/10.5604/01.3001.0054.3829
• [21] Ling, J.Y.X., Chan, Y.J., Chen, J.W., Chong, D.J.S., Tan, A.L.L., Arumugasamy, S.K., and 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, pp. 19085–19104. DOI: https://doi.org/10.1007/s11356-024-32435-6
• [22] Margineantu, D.D. and Dietterich, T.G., (1997). Pruning adaptive boosting. ICML, 97, pp. 211–218.
• [23] Marszałek, A., Dudziak, M., Frankowski, J., and Szymańczyk, M., (2023). Silkworm cocoons waste as an innovative adsorbent for rainwater purification. Zeszyty Naukowe SGSP, 88(1), pp. 135–146. DOI: 10.5604/01.3001.0054.1453
• [24] Masłoń, A., Czarnota, J., Szaja, A., Szulżyk-Cieplak, J., and Łagód, G., (2020). The enhancement of energy efficiency in a wastewater treatment plant through sustainable biogas use: Case study from Poland. Energies, 13, 22, p. 6056. DOI: https://doi.org/10.3390/en13226056
• [25] Mukasine, A., Sibomana, L., Jayavel, K., Nkurikiyeyezu, K., and Hitimana, E., (2024). Maximizing Biogas Yield Using an Optimized Stacking Ensemble Machine Learning Approach. Energies, 17, 2, p. 364. DOI: https://doi.org/10.3390/en17020364
• [26] Nevzorova, T. and Kutcherov, V., (2019). Barriers to the wider implementation of biogas as a source of energy: A state-of-the-art review. Energy Strategy Reviews, 26, p. 100414. DOI: https://doi.org/10.1016/j.esr.2019.100414
• [27] Organiściak, P., Masłoń, A.A., Kowal, B., Kuraś, P., Wadiak, B., Sikorska-Czupryna, S., and Vanivska, V., (2024). Machine Learning-Based Prediction of Biogas Production from Sludge Characteristics in Four Anaerobic Digesters: Development of the AD2Biogas Prediction Tool. Advances in Science and Technology Research Journal, 18, 8, pp. 1–15. DOI: 10.12913/22998624/192936
• [28] Pająk, M., Dzieniszewska, A., and Kyzioł-Komosińska, J., (2024). Alum sludge as a potential adsorbent for removing CD(II) and NI(II) ions from aqueous solutions – equilibrium studies. Zeszyty Naukowe SGSP, 90(2), pp. 89–105. DOI: 10.5604/01.3001.0054.7329
• [29] Panigrahi, S. and Dubey, B.K., (2019). A critical review on operating parameters and strategies to improve the biogas yield from anaerobic digestion of organic fraction of municipal solid waste. Renewable Energy, 143, pp. 779–797. DOI: https://doi.org/10.1016/j.renene.2019.05.040
• [30] Pazera, A., Slezak, R., Krzystek, L., Ledakowicz, S., Bochmann, G., Gabauer, W., Helm, S., Reitmeier, S., Marley, L., Gorga, F. et al., (2015). Biogas in Europe: food and beverage (FAB) waste potential for biogas production. Energy & Fuels, 29, 7, pp. 4011–4021. https://doi.org/10.1021/ef502812s
• [31] Quinlan, J.R., (1986). Induction of decision trees. Machine learning, 1, pp. 81–106.
• [32] Scarlat, N., Dallemand, J.-F., and Fahl, F., (2018). Biogas: Developments and perspectives in Europe. Renewable energy, 129, pp. 457–472. https://doi.org/10.1016/j.renene.2018.03.006
• [33] Sreekrishnan, T., Kohli, S., Rana, V. et al., (2004). Enhancement of biogas production from solid substrates using different techniques – a review. Bioresource Technology, 95, 1, pp. 1–10. https://doi.org/10.1016/j.biortech.2004.02.010
• [34] Vanivska, V., Kuźniar, J., Kocik, M., and Świrad, A., (2024). Digital Battleground: Analyzing Cyber Warfare between Russia and Ukraine Since 2014. Advances in Web Development Journal, 2, 1.
• [35] Wang, Y., Huntington, T., and Scown, C.D., (2021). Tree-based automated machine learning to predict biogas production for anaerobic co-digestion of organic waste. ACS Sustainable Chemistry & Engineering, 9, 38, pp. 12990–13000. https://pubs.acs.org/doi/10.1021/acssuschemeng.1c04612.
• [36] Weiland, P., (2010). Biogas production: current state and perspectives. Applied Microbiology and Biotechnology, 85, pp. 849–860. https://doi.org/10.1007/s00253-009-2246-7
• [37] Zhang, Y., Li, L., Ren, Z., Yu, Y., Li, Y., Pan, J., Lu, Y., Feng, L., Zhang, W., and Han, Y., (2022). Plant-scale biogas production prediction based on multiple hybrid machine learning technique. Bioresource Technology, 363, p. 127899. DOI: 10.1016/j.biortech.2022.127899

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).