Model matematyczny łańcucha dostaw surowców do produkcji biogazu

• Autorzy: Rotkegel Adam , Ziobrowski Zenon

Warianty tytułu

EN

Mathematycal model of the feedstocks supply chain in biogas production

• Języki publikacji: PL

Abstrakty

PL

Rosnąca emisja gazów cieplarnianych oraz malejąca ilość paliw kopalnych powodują wzrost zainteresowania alternatywnymi źródłami energii. Biogaz jest niekonwencjonalnym paliwem wytwarzanym z materii organicznej w warunkach beztlenowych w procesie fermentacji metanowej. W pracy przedstawiono porównanie i analizę wykorzystywanych obecnie substratów w gospodarce biogazowej. W celu optymalizacji łańcucha dostaw surowców do produkcji biogazu opracowano model matematyczny w oparciu o schemat ideowy przepływu surowców i produktów w łańcuchu wartości produkcji biogazu. W obliczeniach wykorzystano procedury Mixed-Integer Linear Programing (MILP) programu MATLAB.

EN

The increasing energy demands together with flue gas emissions resulting from conventional energy sources accelerates the research for renewable energy and technologies such as anaerobic digestion (AD) to limit the environmental damage [1]. Anaerobic digestion process depends on a four biological steps (hydrolysis, acidogenesis, acetogenesis, and methanogenesis). involving different microbial species such as bacteria and archaea [4]. The stability of the AD process as well as the biogas yields depends on the characteristics of the available feedstocks, C/N ratio, biodegradability, nutrient content or buffering capacity. Generally this process depends on several relevant parameters: feedstock type and its composition, organic loading rate, fermentation temperature, pH, hydraulic retention time and carbon to nitrogen ratio. Methane yields and process stability can be impacted by different shortcomings such as low biodegradation, lag-phase, foam formation, over-acidification and high apparent viscosity or inhibitory elements. In this work the main feedstocks were compared and analyzed. The analysis shows that carbon-nitrogen ratio (C/N) is the most important factor to produce a biomethane. The (C/N) ratio is also important in the quantity of biogas production, even low deviances may cause pH changes to either volatile fatty acid or dangerous ammonia accumulation. It was found that systems containing less than 50% manure show different pH correlations and reduced C/N ratio. Any changes in pH may cause inhibition in biogas production as microbial performance reduces. The low C/N ratio of 15–25 is vital for good performance, whilst especially manure-heavy digestions perform better under higher pH conditions. In order to formulate a mathematical model optimizing the biogas production value chain, it is necessary to know the process parameters given above, as well as the costs of substrates and their transport, the distance between the place of obtaining substrates and the biogas plant, investment and operating costs of the biogas plant, and profits from the sale of biogas/biomethane. Additionally, in the future, the cost of purifying biogas into biomethane should be taken into account when determining the profitability of biomethane production. Subsidies and subsidies supporting the production of zero-emission fuels should also be taken into account.. The flow diagram of raw materials xi,j,k and products yk,n in the proposed model is shown in Figure 1. On its basis, a mathematical model was defined that takes into account the economic benefits of energy and biogas production, which takes into account the costs of biogas production, the costs of transporting raw materials, the possible location of plants where the biogas production is carried out. is the biogas production process. In the case of electricity generation from biogas, the proposed model can take into account the possible installation of a power plant. Operation costs (KOpx) and return on investment costs (KCap) were estimated on the basis of data included in [22]. In calculations based on the mathematical model the MixedInteger Linear Programming (MILP) procedures of MATLAB were used. MILP is a fast procedure for calculating optimal values, minima and maxima for complex mathematical models. For this purpose, the model equations were transformed to the form required by MATLAB [28].

Słowa kluczowe

PL

biogaz; biometan; odpady organiczne

EN

biogas; biomethan; feedstock

• Wydawca: Instytut Inżynierii Chemicznej Polskiej Akademii Nauk
• Czasopismo: Prace Naukowe Instytutu Inżynierii Chemicznej Polskiej Akademii Nauk
• Rocznik: 2024
• Tom: Nr 28
• Strony: 78--94
• Opis fizyczny: Bibliogr. 28 poz., rys., tab., wykr.

Twórcy

autor

Rotkegel Adam

• Instytut Inżynierii Chemicznej Polskiej Akademii Nauk, ul. Bałtycka 5, 44-100 Gliwice

autor

Ziobrowski Zenon

• Instytut Inżynierii Chemicznej Polskiej Akademii Nauk, ul. Bałtycka 5, 44-100 Gliwice

Bibliografia

• [1] C. Bumharter, D. Bolonio, I. Amez, M.J. García Martínez, M.F. Ortega, New opportunities for the European Biogas industry: A review on current installation development, production potentials and yield improvements for manure and agricultural waste mixtures, Journal of Cleaner Production 388 (2023) 135867. https://doi.org/10.1016/j.jclepro.2023.135867.
• [2] M. Kaltschmitt, H. Hartmann, H. Hofbauer, eds., Energie aus Biomasse, Springer, Berlin, Heidelberg, 2009. https://doi.org/10.1007/978-3-540-85095-3.
• [3] S. Schattner, A. Gronauer, Methangärung verschiedener Substrate – Kenntnisstand und offene Fragen, in: Energetische Nutzung von Biogas: Stand Der Technik Und Optimierungspotenzial, Fachagentur Nachwachsende Rohstoffe e. V., Weimar, 2000: pp. 28–38. https://mediathek.fnr.de/tagungsbeitrage/bioenergie/band-15-energetische-nutzung-von-biogas-stand-der-technik-und-optimierungspotenzial.html.
• [4] E. Leca, B. Zennaro, J. Hamelin, H. Carrère, C. Sambusiti, Use of additives to improve collective biogas plant performances: A comprehensive review, Biotechnology Advances 65 (2023) 108129. https://doi.org/10.1016/j.biotechadv.2023.108129.
• [5] DWA, Merkblatt DWA-M 363 Herkunft und Verwertung von Biogas, Deutsche Vereinigung für Wasserwirtschaft, Abwasser und Abfall, 2022. https://www.lehmanns.de/shop/naturwissenschaften/58762550-9783968621630-merkblatt-dwa-m-363-herkunft-und-verwertung-von-biogas (accessed November 9, 2023).
• [6] R. Braun, Biogas — Methangärung organischer Abfallstoffe, Springer, Vienna, 1982. https://doi.org/10.1007/978-3-7091-8675-6.
• [7] A. Lehtomäki, S. Huttunen, T.M. Lehtinen, J.A. Rintala, Anaerobic digestion of grass silage in batch leach bed processes for methane production, Bioresour Technol 99 (2008) 3267–3278. https://doi.org/10.1016/j.biortech.2007.04.072.
• [8] R. Steffen, O. Szolar, R. Braun, Feedstocks for anaerobic digestion, Institute of Agrobiotechnology Tulin, University of Agricultural Sciences, Vienna (1998). https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=bd8871a108e5c24a870c54317f9397e520d2c721 (accessed November 13, 2023).
• [9] M. Myint, N. Nirmalakhandan, R.E. Speece, Anaerobic fermentation of cattle manure: Modeling of hydrolysis and acidogenesis, Water Research 41 (2007) 323–332. https://doi.org/10.1016/j.watres.2006.10.026.
• [10] J.-J. Ko, Y. Shimizu, K. Ikeda, S.-K. Kim, C.-H. Park, S. Matsui, Biodegradation of high molecular weight lignin under sulfate reducing conditions: Lignin degradability and degradation by-products, Bioresource Technology 100 (2009) 1622–1627. https://doi.org/10.1016/j.biortech.2008.09.029.
• [11] D. Deublein, A. Steinhauser, Biogas from Waste and Renewable Resources, Wiley-VCH Verlag GmbH & Co.Kg, Weinheim, 2011. https://doi.org/10.1002/9783527632794.
• [12] Substraty do produkcji biogazu - odpady hodowlane (część 3/5), OZE Odnawialne Źródła Energii (n.d.). https://www.odnawialne-firmy.pl/wiadomosci/pokaz/111,substraty-do-produkcji-biogazu-odpady-hodowlane-czesc-35 (accessed November 3, 2023).
• [13] M. Tyszka, Poferment - nawóz czy odpad? - Nawożenie, www.farmer.pl (2015). https://www.farmer.pl/produkcja-roslinna/nawozy/poferment-nawoz-czy-odpad,57951.html (accessed November 3, 2023).
• [14] P. Ochal, Poferment z biogazowni rolniczej jako nawóz, Nawozy.Eu (n.d.). https://nawozy.eu (accessed November 3, 2023).
• [15] Substraty do produkcji biogazu - rośliny energetyczne (część 5/5), OZE Odnawialne Źródła Energii (n.d.). https://www.odnawialne-firmy.pl/wiadomosci/pokaz/120,substraty-do-produkcji-biogazu-rosliny-energetyczne-czesc-55 (accessed November 3, 2023).
• [16] Substraty do produkcji biogazu - odpady przemysłu spożywczego (część 1/5), OZE Odnawialne Źródła Energii (n.d.). https://www.odnawialne-firmy.pl/wiadomosci/pokaz/106,substraty-do-produkcji-biogazu-odpady-przemyslu-spozywczego-czesc-15 (accessed November 3, 2023).
• [17] Substraty do produkcji biogazu - osady ściekowe (część 2/5), OZE Odnawialne Źródła Energii (n.d.). https://www.odnawialne-firmy.pl/wiadomosci/pokaz/110,substraty-do-produkcji-biogazu-osady-sciekowe-czesc-25 (accessed November 3, 2023).
• [18] Substraty do produkcji biogazu - odpady komunalne (część 4/5), OZE Odnawialne Źródła Energii (n.d.). https://www.odnawialne-firmy.pl/wiadomosci/pokaz/117,substraty-do-produkcji-biogazu-odpady-komunalne-czesc-45 (accessed November 3, 2023).
• [19] V. Lukinskiy, V. Lukinskiy, Formation of Failure Models for the Evaluation of the Reliability of Supply Chains, Transport and Telecommunication Journal 16 (2015) 40–47.
• [20] P.E. Murillo-Alvarado, J.M. Ponce-Ortega, An optimization approach to increase the human development index through a biogas supply chain in a developing region, Renewable Energy 190 (2022) 347–357. https://doi.org/10.1016/j.renene.2022.02.076.
• [21] B.R. Sarker, B. Wu, K.P. Paudel, Modeling and optimization of a supply chain of renewable biomass and biogas: Processing plant location, Applied Energy 239 (2019) 343–355. https://doi.org/10.1016/j.apenergy.2019.01.216.
• [22] Y. Gital Durmaz, B. Bilgen, Multi-objective optimization of sustainable biomass supply chain network design, Applied Energy 272 (2020) 115259. https://doi.org/10.1016/j.apenergy.2020.115259.
• [23] L.A. Díaz-Trujillo, F. Nápoles-Rivera, Optimization of biogas supply chain in Mexico considering economic and environmental aspects, Renewable Energy 139 (2019) 1227–1240. https://doi.org/10.1016/j.renene.2019.03.027.
• [24] G. Leonzio, P.U. Foscolo, E. Zondervan, Optimization of CCUS Supply Chains for Some European Countries under the Uncertainty, Processes 8 (2020) 960. https://doi.org/10.3390/pr8080960.
• [25] I. Jensen, M. Münster, D. Pisinger, Optimizing the supply chain of biomass and biogas for a single plant considering mass and energy losses, European Journal of Operational Research (2017). https://doi.org/10.1016/j.ejor.2017.03.071.
• [26] C.H. Lim, H.L. Lam, Biomass supply chain optimisation via novel Biomass Element Life Cycle Analysis (BELCA), Applied Energy 161 (2016) 733–745. https://doi.org/10.1016/j.apenergy.2015.07.030.
• [27] T. Lin, L.F. Rodríguez, Y.N. Shastri, A.C. Hansen, K. Ting, GIS-enabled biomass-ethanol supply chain optimization: model development and Miscanthus application, Biofuels, Bioproducts and Biorefining 7 (2013) 314–333. https://doi.org/10.1002/bbb.1394.
• [28] Mixed-Integer Linear Programming (MILP) Algorithms - MATLAB & Simulink, (n.d.). https://www.mathworks.com/help/optim/ug/mixed-integer-linear-programming-algorithms.html (accessed February 2, 2024).