The selection and design of process options to deliver outcomes consistent with sustainable development requires simultaneous consideration of multiple objectives, within a climate of uncertainty, including the environmental and social aspects of technology management in addition to the technical and economic aspects of such. An issue of particular relevance in primary metal production is the deportment of minor and trace components within orebodies, products and wastes and the implications of such deportment for eco-efficiency, in particular, the long term environmental impact of solid waste management practices. The identification of opportunities to enhance recovery of valuable byproducts and simultaneously improve the environmental acceptability of final waste output requires a clear understanding of the distribution of the relevant ore components, which results from the choice of processing route and unit operation efficiency. This is challenging, given that process streams generated during primary metal production are generally poorly characterised and the behaviour and deportment of trace to minor and trace components is not well understood. This paper demonstrates how current data gaps and inconsistencies can be systematically addressed through the meaningful reconciliation of empirical plant data with a fundamental understanding of the mechanisms and parameters influencing element behaviour and deportment during the formation and subsequent beneficiation of mineral ore deposits. On this basis, quantitative distribution data and a comprehensive and comprehensible inventory of unit process inputs and outputs as a function of ore compositions and processing technology options can be generated, which is consistent with early design stage information requirements, both in terms of detail and accuracy, i.e. is ‘first order’ in nature. This predictive approach to element distribution during primary metal production provides decision makers with key information in the early design stages of a project (in terms of developing processes within the context of sustainability), while simultaneously guiding further data collection and environmental impact prediction studies.
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