As processes involved in mineral processing operations increase their complexity, automation and control become critical to ensure an economically viable and environmentally sustainable operation. In the context of modern mineral processing, paste thickening stands out as a relatively new method for producing high density slurries that has proven challenging for standard control algorithms. In this setting, the use of machine-learning-based models within a predictive control strategy arises as an appealing alternative. This work presents a Random Forest Model Predictive Control scheme for paste thickening based on a purely data-driven approach for modeling and evolutionary strategies for solving the associated optimization problem. Results show that the proposed strategy outperforms conventional predictive control both qualitatively and quantitatively.

随着涉及矿物加工操作的过程的复杂性增加，自动化和控制对于确保经济可行和环境可持续的运营至关重要。在现代矿物加工的背景下，糊料增稠是生产高密度浆料的一种相对较新的方法，已证明对标准控制算法具有挑战性。在这种情况下，在预测控制策略中使用基于机器学习的模型作为一种有吸引力的替代方法出现了。这项工作提出了一种基于纯数据驱动的建模和演化策略的糊状增稠的随机森林模型预测控制方案，以解决相关的优化问题。