Modular model and simulation for process optimisation in advanced material recovery facilities (MRFs)

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dc.contributor.authorLiu, Milan
dc.contributor.authorGrimes, Sue
dc.contributor.authorSalonitis, Konstantinos
dc.contributor.authorLitos, Lampros
dc.date.accessioned2024-10-21T11:48:32Z
dc.date.available2024-10-21T11:48:32Z
dc.date.freetoread2024-10-21
dc.date.issued2024
dc.date.pubOnline2024-10-15
dc.description.abstractAt a time when the supply of critical materials is threatened, waste recycling and reuse is an essential solution for human development. The role of Material Recovery Facilities (MRFs) to deliver efficiently high-purity material fractions as feedstock cannot be underestimated. However, MRF sorting processes need to remain adaptive with evolving smart technologies and systems that further enhance their effectiveness. For example, a re-designed MRF with AI-based robotics can improve the performance of waste recycling, leading to significant economic and environmental benefits. This study assesses the performance of potential optimisation methods for future proofing MRFs using modular simulation methods. The authors set out to review current robotics sorting technology and pointed out the challenge of efficiency analysis with multiple variables. The study develops a new conceptual model of efficiency analysis considering the identification and sorting limitations of robots, as well as the coordination requirements between robots and conveyor belts. A computational model is designed and developed by modularity program codes to help practitioners gain insight into the MRF performance by modifying the variables (composition of input waste, separation coefficients and configurations) and analysing the resulting assessment factors (purity and recovery). In the end, this study demonstrates the performance of the optimisation methods of MRF (two target materials for one robot and recirculation loops) through simulation.
dc.description.journalNameProcedia CIRP
dc.format.extent250-255
dc.identifier.citationLiu M, Grimes S, Salonitis K, Litos L. (2024) Modular model and simulation for process optimisation in advanced material recovery facilities (MRFs). Procedia CIRP, Volume 128, 2024, pp. 250-255
dc.identifier.elementsID555075
dc.identifier.issn2212-8271
dc.identifier.urihttps://doi.org/10.1016/j.procir.2024.07.050
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23094
dc.identifier.volumeNo128
dc.languageEnglish
dc.language.isoen
dc.publisherElsevier
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S221282712400670X?via%3Dihub
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject4014 Manufacturing engineering
dc.subjectMaterial recovery facilities
dc.subjectRecycling
dc.subjectSorting
dc.subjectOptimisation
dc.subjectModular program
dc.subjectSimulation
dc.titleModular model and simulation for process optimisation in advanced material recovery facilities (MRFs)
dc.typeArticle

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