MO.2.A || Life Cycle Approaches in the Raw Materials Sector II

Muller, Stephanie; Beylot, Antoine; Lai, Frédéric; Villeneuve, Jacques; Moore, Kathryn; Sanchidrián, José A.; Kinnunen, Päivi

When looking at life cycle inventory (LCI) databases or specific scientific literature regarding the potential environmental impacts of mining through life cycle assessments (LCA), the reliability of the data used to model the LCI of the mining system can be questioned. Data used might suffer from quality issues such as incompleteness and limitations in geographical or technological representativeness. In order to overcome these issues, expertise from the mining sector can be coupled with LCA expertise. Firstly process simulation can be used to model the exchange of flows at the unit process level, building on experimental data. The coupling between process simulation and LCA was performed in the H2020 project IMPaCT to access representative data regarding an innovative mineral processing technology implemented at a pilot scale. A second type of coupling concerns reactive transport modeling and LCA to estimate the long-term emissions resulting from tailings management. This coupling was performed in the frame of the H2020 ITERAMS project and showed that current generic datasets available in LCI databases might overestimate the potential long-term tailings emissions. Coupling between LCA and rock mass fragmentation expertise or with leaching measurements might also be a solution to generate reliable data regarding the rock blasting phase in mining. Indeed, either the nitrates potential leaching due to the use of explosives or the effect of blasting on the efficiency of the comminution process and so on the energy use, are issues poorly – and even not -considered at present in the LCA, as underlined in the conclusions of the H2020 project SLIM. These different examples show the importance of building bridges between different expertises in the raw material sector towards more reliable LCA models. Generalization is the next step further, with coupling applied on specific case studies also used to enhance the quality of mining datasets available in LCI databases.