This paper studies the operational optimal control problem for the industrial flotation process, a key component in the mineral processing concentrator line. A new model-free data-driven method is developed here for real-time solution of this problem. A novel formulation is given for the optimal selection of the process control inputs that guarantees optimal tracking of the operational indices while maintaining the inputs within specified bounds. Proper tracking of prescribed operational indices, namely concentrate grade and tail grade, is essential in the proper economic operation of the flotation process. The difficulty in establishing an accurate mathematic model is overcome, and optimal controls are learned online in real time, using a novel form of reinforcement learning we call interleaved learning for online computation of the operational optimal control solution. Simulation experiments are provided to verify the effectiveness of the proposed interleaved learning method and to show that it performs significantly better than standard policy iteration and value iteration.

中文翻译：

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基于强化学习的数据驱动浮选工业过程运行优化控制

本文研究了工业浮选过程中的操作最优控制问题，这是选矿厂选矿线的关键组成部分。此处开发了一种新的无模型数据驱动方法，用于实时解决此问题。针对过程控制输入的最佳选择，给出了一种新颖的公式，该方法可确保对操作指标进行最佳跟踪，同时将输入保持在指定范围内。正确跟踪规定的操作指标（即精矿品位和尾矿品位）对于浮选过程的正确经济运行至关重要。克服了建立精确数学模型的困难，并且可以在线实时学习最佳控制，使用一种新颖形式的强化学习，我们将交错学习称为在线最优操作控制解决方案的计算。提供了仿真实验，以验证所提出的交错学习方法的有效性，并表明它的性能明显优于标准策略迭代和值迭代。