Dynamic Water Management for Maximization of Copper Production

Low grade ores mineral processing plants require large amounts of energy and water to operate in a sustainable and profitable state. These ores present large variations in their mineralogy, metal content and hardness. These low-grade ore plants are processing mostly rock in the first part of the process, followed by the traditional mineral processing and water recovery systems. Currently, mineral processing plants operate in silos. The lack of available integrated data from mining, grinding, classification, flotation, thickening and tailings processing preclude collaboration between the diverse operating teams. The process information with the right degree of detail is usually missing to understand the integrated process plant system. Today, tight coordination between the mining product, grinding, classification, flotation and water recovery processing is a must.
Methodology: A novel strategy using a Digital Twin was designed to increase the necessary water recovery to maximize the copper production rate in a low-grade ore industrial plant. A dynamic water management system was implemented enabling to understand the water requirements for grinding, classification and flotation processes. The rheological properties of the suspensions are predicted to avoid overgrinding. The grinding particle size distributions products are closely monitored for both for flotation and flocculation of the tails produced. This over-grinding creates finer particles that are difficult to recover by conventional flotation cells and present difficult flocculation results. The typical P80 measures does not account for the tail of the size distribution. A detailed analysis conducted by the plant show a bimodal distribution which causes flocculation problems and water losses in the tailing ponds.
Results and Conclusions: This Digital Twin Mineral Processing strategy enables for a synchronized processing to maximize mill throughput, copper production rate and water recovery. The implementation results are: a 40% increase in water recovery and the total copper production rate of 32% (Bascur, et, al, 2022). These savings are very significant based on a zero-capital investment requirement and a minimum configuration time of a Digital Twin Plant Model (Bascur, 2020). The OSB Digital Twin is based on the implementation of a mineral processing model built using the PI System data infrastructure and OSB Dynamic Grinding, Flotation and Thickening simulators (Concha and Bascur, 2023). The process operational modes are calculated based on the plant business plan and current data to transform the timeseries raw data into information to build the necessary machine learning models. These models are used to understand the integrated behavior of the plant and avoid violating costly process constraints in the grinding, classification, flotation and thickening processes. The recent advances in process identification using a process historian is available using coaching mentors who can guide businesses in the new art of creating predictive models for digital plant optimization. Remote support and coaching have become established methods to obtain high benefits in remote mining operations all over the world.
References:
1. Bascur OA (2020) Digital Transformation in the Process Industries: A Road Map. Boca Raton, FL: CRC Press. • osisoft bascur book tr... .
2. Concha F, Bascur OA (2023) The Science of Mineral Processing: A Fundamental and Practical Approach, To be published by Taylor and Francis, Boca Raton, FL.
3. Bascur, O.A., Retamal, F., and Espinosa, S., 2022, Treating Low Grades Ores using a Digital in Mining and Mineral Processing Plants, Proceedings of the Copper 2022 International Conference, Nov. 13-17, IIMChile, Volume 7: Process Optimization through Innovation, Technology, Automation and Design, Santiago, Chile