Abstract
The utilization of anaerobic systems for biogas production integrates various aspects such as renewable energy generation, waste management, waste treatment, and biofertilizer production. This study introduces a model that focuses on the economic optimization of a biomass supply network for biogas production in urban areas. The selected feedstocks considered in the model are biowaste and residues sourced from restaurants, shops, and the food and beverage industry. This study introduces two significant advancements. Firstly, it employs an enhanced GIS-based approach that integrates greenhouse gas (GHG) requirements by incorporating a maximal allowed transport distance. This integration aims to achieve minimal GHG savings from biogas usage. These GHG-based requirements align with the specifications outlined in Directive 2018/2001, which promotes the use of renewable energy sources and stipulates a minimum 80% reduction in greenhouse gas emissions from biogas plants operating from 2026, in addition to meeting environmental sustainability criteria. Secondly, the study introduces a novel approach that combines GIS map** of biomass potential with a P-graph framework for optimizing the biomass supply network. This integration facilitates comprehensive and efficient optimization of the network for biogas production. The model is developed and solved using P-Graph Studio, while feedstock availability and transportation distances are determined using the QGIS tool. The approach is tested under two scenarios: one with an annual production of 36,000 GJ and another with an annual production of 72,000 GJ. The p-graph approach enables the identification of the optimal economic solution for both scenarios. As the most of the biogas potential is concentrated in a single brewery, the specific cost of the biomass supply network, including feedstock and transport, remains comparable for both scenarios, with values of 12.44 EUR/GJ and 12.61 EUR/GJ for the second case.
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References
Adonyi R, Heckl I, Olti F (2013) Scheduling of bus maintenance by the P-graph methodology. Optim Eng 14(4):565–574. https://doi.org/10.1007/s11081-013-9240-8
Bartos A, Bertok B (2020) Production line balancing by P-graphs. Optim Eng 21(2):567–584. https://doi.org/10.1007/s11081-019-09462-1
Chakraborty A et al (2022) Develo** a spatial information system of biomass potential from crop residues over India: a decision support for planning and establishment of biofuel/biomass power plant. Renew Sustain Energy Rev 165(February 2021):112575. https://doi.org/10.1016/j.rser.2022.112575
da Romero CWS, Miyazaki MR, Berni MD, Figueiredo GKDA, Lamparelli RAC (2023) A spatial approach for integrating GIS and fuzzy logic in multicriteria problem solving to support the definition of ideal areas for biorefinery deployment. J Clean Prod 390(October 2021):135886. https://doi.org/10.1016/j.jclepro.2023.135886
Egieya JM, Čuček L, Zirngast K, Isafiade AJ, Pahor B, Kravanja Z (2018) Synthesis of biogas supply networks using various biomass and manure types. Comput Chem Eng. https://doi.org/10.1016/j.compchemeng.2018.06.022
EU (2018) Directive (EU) 2018/2001 of the European parliament and of the council on the promotion of the use of energy from renewable sources. Off J Euro Union 328:82–209
European Biomass Association (ΕΒΑ) (2021) EBA statistical Report 2021
Friedler F, Tarjan K, Huang YW, Fan LT (1993) Graph-theoretic approach to process synthesis: polynomial algorithm for maximal structure generation. Comput Chem Eng 17(9):929–942. https://doi.org/10.1016/0098-1354(93)80074-W
Friedler F, Fan LT Homepage for Process Network Synthsis (PNS) and the Process Graph or P-graph Framework. P-graph. https://p-graph.org/. (Accessed Jan 2023)
How BS, Hooi B, Loong H, Friedler F (2015) Synthesis of multiple biomass corridor via decomposition approach : a P-graph application. J Clean Prod. https://doi.org/10.1016/j.jclepro.2015.12.021
Ji M, Zhang W, Xu Y, Liao Q, Jaromír Klemeš J, Wang B (2023) Optimisation of multi-period renewable energy systems with hydrogen and battery energy storage: a P-graph approach. Energy Convers Manag. https://doi.org/10.1016/j.enconman.2023.116826
Joint Research Centre (2019) “Estimating road transport costs between EU regions. Joint Research Centre, Brussels
Lam HL, Varbanov PS, Klemeš JJ (2010) Optimisation of regional energy supply chains utilising renewables: P-graph approach. Comput Chem Eng 34(5):782–792. https://doi.org/10.1016/j.compchemeng.2009.11.020
Lo SLY, Lim CH, Benjamin MFD, Lam HL, Sunarso J, How BS (2022) Addressing supply uncertainties using multi-period stochastic economic evaluation: a graph-theoretic aided element targeting approach. Clean Eng Technol 10(September):100554. https://doi.org/10.1016/j.clet.2022.100554
Lovrak A, Pukšec T, Duić N (2020) A Geographical Information System (GIS) based approach for assessing the spatial distribution and seasonal variation of biogas production potential from agricultural residues and municipal biowaste. Appl Energy 267(January):115010. https://doi.org/10.1016/j.apenergy.2020.115010
Lovrak A, Pukšec T, Grozdek M, Duić N (2022) An integrated Geographical Information System (GIS) approach for assessing seasonal variation and spatial distribution of biogas potential from industrial residues and by-products. Energy. https://doi.org/10.1016/j.energy.2021.122016
Malladi KT, Quirion-Blais O, Sowlati T (2018) Development of a decision support tool for optimizing the short-term logistics of forest-based biomass. Appl Energy 216(December 2017):662–677. https://doi.org/10.1016/j.apenergy.2018.02.027
Mitri S et al (2022) Valorization of Brewers’ spent grains: pretreatments and fermentation, a review. Fermentation. https://doi.org/10.3390/fermentation8020050
Ondruška V, How BS, Netolický M, Maša V, Yong Teng S (2022) Resource optimisation in aquaponics facility via process monitoring and graph-theoretical approach. Carbon Resour Convers 5(January):255–270. https://doi.org/10.1016/j.crcon.2022.04.003.
OpenStreetMap Foundation. Open Street Map. https://www.openstreetmap.org/. (Accessed Jan 2023)
“Overpass turbo.” Martin Raifer. Overpass turbo. https://overpass-turbo.eu/. (Accessed Jan 2023)
QGIS-A Free and Open Source Geographic Information System. http://www.qgis.org/en/site/. (Accessed Jan 2023)
Rhofita EI, Rachmat R, Meyer M, Montastruc L (2022) Map** analysis of biomass residue valorization as the future green energy generation in Indonesia. J Clean Prod 354:131667. https://doi.org/10.1016/J.JCLEPRO.2022.131667
Stile Z, Bertók B, Friedler F, Fan LT (2011) Optimal design of supply chains by P-graph framework under uncertainties. Chem Eng Trans 25:453–458. https://doi.org/10.3303/CET1125076
Tan JX et al (2021) A P-Graph approach for the synthesis of hydrogen networks with pressure and impurity constraints. Int J Hydrog Energy 46(57):29198–29215. https://doi.org/10.1016/j.ijhydene.2020.08.286
Ukoba MO, Diemuodeke EO, Briggs TA, Imran M, Owebor K, Nwachukwu CO (2023) Geographic information systems (GIS) approach for assessing the biomass energy potential and identification of appropriate biomass conversion technologies in Nigeria. Biomass Bioenergy 170(September 2022):106726. https://doi.org/10.1016/j.biombioe.2023.106726
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This work has been financially supported by the Croatian Science Foundation.
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Kodba, A., Pukšec, T. & Duić, N. P-graph approach for the optimisation of biomass supply network for biogas production in urban areas. Optim Eng 25, 13–28 (2024). https://doi.org/10.1007/s11081-023-09819-7
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DOI: https://doi.org/10.1007/s11081-023-09819-7