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Life Cycle Assessment (LCA) is a widely-used methodology for estimating the potential environmental impacts of technological systems in nature. Relative maturity of this method has been achieved thanks to recent standardization efforts, although there are still several challenges, particularly in the minerals industry where LCA still needs to be strengthened and extended in use. An exhaustive review of recent LCA studies on mining and mineral processing operations was conducted to identify how these have dealt with various methodological challenges. A total of twenty-nine studies were found, most of which focusing on developed regions and in three commodities: coal (28%), aggregates (21%), and copper ores (14%). The source of data for background processes has been the Ecoinvent database in 45% of the studies analyzed, whereas the SimaPro software has been the preferred LCA tool in 66% of such studies. Although important advances have been made, significant issues remain. For instance, inconsistencies were found in the definition of the functional unit and system boundaries (temporal and technological). Further, the adoption of allocation criteria and the use of normalization and weighting in impact assessment have not been considered in most cases. While climate change was the main environmental hotspot (more than 90% of the studies addressed this impact category), the impacts associated with water use and waste management have been overlooked in most studies. Furthermore, mining and processing operations have been described as generic, averaged values or as simple functional relationships between system inputs and outputs using parameters without physical significance ("black box models") in several production chains, making it difficult to identify opportunities for environmental sustainability enhancement within the sector. The work then discusses the opportunity of improvement in LCA in the minerals industry through the incorporation of process simulation from a bottom-up perspective. Evidence from other industries and recent advances in the computational development of such tools for the mineral sector are reviewed, which suggests that there is an opportunity for reducing the epistemic uncertainty in future LCA studies of mining and processing operations through the combination of both approaches. (C) 2019 Elsevier Ltd. All rights reserved.