dc.contributor.author | Blanco Suárez, Víctor | |
dc.contributor.author | Hijonosa, Yolanda | |
dc.contributor.author | Zavala, Víctor M. | |
dc.date.accessioned | 2024-07-18T11:24:08Z | |
dc.date.available | 2024-07-18T11:24:08Z | |
dc.date.issued | 2024-05-22 | |
dc.identifier.citation | Blanco, V. Hinojosa Y. Zavala, V.M. ACS Sustainable Chem. Eng. 2024, 12, 8453−8466. [https://doi.org/10.1021/acssuschemeng.4c01429] | es_ES |
dc.identifier.uri | https://hdl.handle.net/10481/93228 | |
dc.description.abstract | In this paper, we propose a new mathematical
optimization approach to make decisions on the optimal design of
the complex logistic system required to produce biogas from waste. We
provide a novel and flexible decision-aid tool that allows decision
makers to optimally determine the locations of different types of plants
(pretreatment, anaerobic digestion, and biomethane liquefaction plants)
and pipelines involved in the logistic process, according to a given
budget, as well as the most efficient distribution of the products (from
waste to biomethane) along the supply chain. The method is based on a
mathematical optimization model that we further analyze and that, after
reducing the number of variables and constraints without affecting the
solutions, is able to solve real-size instances in reasonable CPU times.
The proposed methodology is designed to be versatile and adaptable to
different situations that arise in the transformation of waste to biogas. The results of our computational experiments, both in
synthetic and in a case study instance, prove the validity of our proposal in practical applications. Synthetic instances with up to 200
farms and potential locations for pretreatment plants and 100 potential locations for anaerobic digestion and biomethane
liquefaction plants were solved, exactly, within <20 min, whereas the larger instances with 500 farms were solved within <2 h. The
CPU times required to solve the real-world instance range from 2 min to 6 h, being highly affected by the given budget to install the
plants and the percent of biomethane that is required to be injected in the existing gas network. | es_ES |
dc.description.sponsorship | PID2020-114594GB-C21, funded by MICIU/AEI/10.13039/
501100011033, and Grant No. RED2022-134149-T, funded
by MICIU/AEI/10.13039/501100011033 (Thematic Network
on Location Science and Related Problems) | es_ES |
dc.description.sponsorship | FEDER
+Junta de Andalucía Project Nos. C-EXP-139-UGR23 | es_ES |
dc.description.sponsorship | AT
21_00032; VII PPIT-US (Ayudas Estancias Breves, Modalidad
III.2A); and the IMAG-Maria de Maeztu (Grant No.
CEX2020-001105-M/AEI/10.13039/501100011033) | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | ACS publications | es_ES |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Logistics, | es_ES |
dc.subject | Green Energy | es_ES |
dc.subject | Facility Location | es_ES |
dc.title | The Waste-to-Biomethane Logistic Problem: A Mathematical Optimization Approach | es_ES |
dc.type | journal article | es_ES |
dc.rights.accessRights | open access | es_ES |
dc.identifier.doi | 10.1021/acssuschemeng.4c01429 | |
dc.type.hasVersion | VoR | es_ES |