Integrated comminution and flotation neurocontrol using evolutionary reinforcement learning

The efficiency of mineral processing operations, i.e. concentrating raw ores for metal extraction, impacts significantly on the operating cost of the final metal products. First, valuable minerals are liberated from the ore matrix in a comminution circuit, comprised of grinding (e.g., ball mills) and classification (e.g. hydrocyclones) units. The liberated minerals are subsequently concentrated according to chemical or physical properties in flotation (i.e., by surface chemistry) or magnetic separation. The overall control objective of both circuits is to produce a concentrate that maximizes the venture profit of the concentrator, which is determined by the throughput, recovery and grade of the concentrator product. The comminution and flotation circuits both contribute to maximizing the venture profit associated with the concentrate1. The comminution circuit conditions the ore for optimal liberation and surface chemistry for improved recovery and grade in the flotation circuit. Since the comminution circuit’s liberation is not measurable on-line, the control objective for comminution attempts to maintain a particle size distribution deemed optimal for flotation. Disturbances to the comminution circuit, such as ore hardness and feed particle size variations, lead to suboptimal particle size distributions, affecting the flotation unit adversely. In addition, flotation circuits are perturbed by numerous disturbances unrelated to comminution, viz. mineralogical composition, varying solution composition and ore surface changes. Other than supplying a fixed particle size distribution, current grinding circuit control schemes do not take the current operating condition of the flotation circuit into account. Comminution circuit control actions are based purely on sensor information from the comminution circuit. Similarly, typical flotation control strategies do not consider how the current operating condition of the grinding circuit impacts the flotation unit, i.e. flotation control actions are rarely based on sensor information from the comminution circuit. The flotation circuit must have knowledge of the operating condition of the grinding circuit, adjusting its control action based on disturbances in the comminution circuit. Since the consequences of these disturbances are fed forward to the flotation circuit, the flotation control system would benefit from such a feedforward control approach. Clearly, changes to the manipulated variables of the flotation circuit have no impact on the comminution circuit operating condition. However, the comminution circuit should adjust its control strategy based on the operating condition of the flotation circuit. Thereby, the comminution control strategy may aid the flotation circuit in attaining its optimal operating conditions. Each circuit’s control strategy should therefore have knowledge of the current state of the other circuit. Better integration between the control strategies for the comminution and flotation circuits serves the overall control objective for the two circuits. Empirical and phenomenological models and on-line analysers (i.e., X-ray fluorescence) for comminution and CONRADIE, A.V.E., BASCUR, O., ALDRICH, C., and NIEUWOUDT, I. Integrated comminution and flotation neurocontrol using evolutionary reinforcement learning. Application of Computers and Operations Research in the Minerals Industries, South African Institute of Mining and Metallurgy, 2003.