Application of Reinforcement Learning Based on Hybrid Model in Optimal Control of Flotation Process
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NSTL
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传统的优化控制方法很难在浮选过程状态发生变化时准确、快速做出决策,导致精矿品位和尾矿品位大幅度波动、出现产品质量不稳定.此外,浮选过程难以对精矿品位进行在线检测,导致其实用性下降.针对上述问题采用混合模型对浮选过程建模,并基于示例的安全增强值评估(safety augmented value estimation from demonstrations,SAVED)的强化学习算法,控制浮选溢出气泡的尺寸分布,从而间接实现对精矿品位和尾矿品位的控制.通过仿真实验验证了所提算法的有效性.与人工经验和数据驱动模型相比,基于混合模型的SAVED算法在保证安全约束的条件下能够实现更好的控制效果.
Traditional optimization control methods are difficult to make accurate and rapid decisions when the state of the flotation process changes,resulting in significant fluctuations in the concentrate grade and tailings grade,and unstable product quality.In addition,the flotation process is difficult to detect the concentrate grade online,leading to a decrease in its practicality.In response to the above problems,a hybrid model is used to model the flotation process and a reinforcement learning algorithm based on safety augmented value estimation from demonstrations(SAVED)is used to control the size distribution of flotation overflow bubbles to indirectly control the concentrate grade and tailings grade.The effectiveness of the proposed algorithm is verified through simulation experiments.Compared with artifical experience and data-driven models,SAVED based on hybrid models is used to model the flotation process and control the size distribution of flotation overflow bubbles.The algorithms can achieve better control effects while ensuring safety constraints.
flotation processreinforcement learninghybrid modelsafety constraintsoptimal control
NETL
NSTL
万方数据
