Measuring and optimizing flotation metal recovery in the digital era, O.A. Bascur

Ores are becoming extremely variable with mineralogy and hardness disturbing the grinding and flotation circuits. The current grinding and flotation sensors provide large amounts of data for process optimisation. Adding the right context and operational events augments operational knowledge for proactive actions to improve the performance of the grinding and flotation circuits. A data-driven strategy is needed to enable operations, maintenance, and business personnel to quickly and easily take corrective action when abnormal conditions occur. A digital plant template transforms data into operational insights in real-time. It uncovers the hidden, idle and downtime losses. By measuring and managing these unproductive times, people find new ways of avoiding them, improving the profitability of the plant. The information created by the real time analytics enables the calculation of recovery in real time and develops predictive analytics models to find the best operating condition based on the type of ore currently mined. The creation of new workflows and collaboration between mining and concentrator plant and the enterprise, including services providers, are enabled. A grade recovery model is used to identify the best operating conditions in real-time. An optimal Gaudin size distribution shape is used to find the grinding operating conditions that optimise the metal recovery in the rougher sections of the mineral processing plant. Sensors in the flotation circuits enable estimation of metal recovery and determine the optimal froth depth and aeration profiles, using an industrial air holdup flotation model. The implementation of a recovery/grind strategy with industrial examples in non-ferrous metal concentrator is presented in the paper. Keywords: Digital transformation, soft sensors, predictive analytics, grinding flotation optimisation, flotation bubbles, particle size, particle size distribution shape, froth aeration profiles, flotation air hold up model, recovery predictive analytics