Towards a technology framework for evaluating and optimizing the metal flow process for coper production

Abstract

The copper usage around the world is increasing as it is an important commodity for the electrical and electronics industries. Although, production of copper in the world is increasing, it is declining in South Africa. Between the mining face and the final product in plant, a large quantity of copper is lost in various forms. The copper ore usually contains around 2% copper along with other elements like gold, zinc, sulphur etc., depending upon the ore mineralogy. To produce pure copper, a large quantity of waste is mined with it, which is around 98% of the quantity mined. The mining and processing of copper ore is spread over a large range of activities from production to processing. The accurate measurement of mass and material transfer is significant as each process is dependent on the previous one. In the mining industry, metal accounting is carried out as a general practice to cater for production and reporting requirements. The issue with this practice is that it is not done to the required (high) level of confidence. However, there are digital tools available in the market that supports either mine or plant metal accounting. There is a need for an overall integrated system that will help both the mine and plant in the metal accounting process. In this research, a hypothetical case study of a copper mine is used to study the ore flow process from mine to the final product in the plant. The AMIRA code for metal accounting is also discussed and the protocols for mass measurement, sampling, sample analysis, data collection, data analysis and reporting are outlined. A digital framework called WMI-OFA for ore flow analysis is proposed in accordance with the AMIRA code of guidelines that will be able to monitor and analyse the overall ore flow process. The WMI-OFA framework will be able to bridge the gap of information flow from mine to the final product in plant. It will also assist in reduction of ore losses during production with the real-time identification of bias in measurements.