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A multi-period optimisation approach for deployment and optimal design of an aerospace CFRP waste management supply
Anh Vo Dong1, Catherine Azzaro-Pantel1, Marianne Boix1
1Laboratoire de Génie Chimique, Université de Toulouse, CNRS, INPT, UPS, Toulouse, France; Université de Toulouse, Laboratoire de Génie Chimique, LGC UMR CNRS 5503 ENSIACET INPT - 4 allée Emile Monso - BP 44362, 31432 Toulouse Cedex 4, France.
Abstract:
This paper presents a modelling framework for the deployment and design of aerospace CFRP (Carbon Fibre Reinforced Polymer) waste supply chain. The problem involves a multi-period Mixed Integer Linear Programming (MILP) formulation considering ε-constraint, lexicographic techniques and Multiple Criteria Decision Making (MCDM) tools. The methodology has been applied to a case study of France. In this model, the deployment of new recycling sites (Grinding, Pyrolysis, Supercritical Water, Microwave) is established. The system is optimised by bi-criteria optimisation including an economic objective based on cost minimisation or Net Present Value (NPV) maximisation and an environmental one (minimisation of Global Warming Potential). The potential for economic acceptability of recycled carbon fibres is assessed through a levelized cost derived from the supply chain total cost and the profitability via NPV with a range of various CFRP prices. The results show that the compromise strategy for both economic and environmental objectives leads to centralised configurations at the regions close to significant waste sources. The cooperation in the recovery system is needed to minimise cost and maximise profit. The improvement of recycling technology permits to achieve the compromise solution for both economic and environmental objectives. The results also highlight that a mix of technologies will be involved in deployment phase and that the answer is not straightforward due to the complexity of the system. The methodology is yet generic enough to be replicated in other contexts considering the upgrade of process database.
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