锑浮选过程加药量自适应迭代学习控制

针对现有的加药量控制方法需要浮选过程动态模型或是鲁棒性不足的问题, 提出一种基于自适应动态规划 (ADP) 的浮选过程加药量自适应迭代学习控制方法. 首先, 将药剂量控制问题转化为两级优化问题 (问题 1 和问题 2). 其中, 基于前馈控制原理求解问题 1 得出前馈补偿分量以抑制外界扰动. 然后, 采用基于值迭代的 ADP 算法, 求解问题 2 以得到最优反馈增益, 从而设计一个数据驱动的最优加药量控制策略使最终的生产指标 (精矿品位和尾矿品位) 跟踪给定值, 且药剂量消耗最少. 最后, 通过工业生产数据进行仿真验证, 证明所提方法的收敛性和稳定性.

Adaptive iterative learning reagents control for antimony flotation process

In order to solve the problem that the existing control methods require dynamic model of flotation process or lack of robustness, an adaptive iterative learning reagents control scheme for flotation processes is proposed based on adaptive dynamic programming (ADP) technique. First, the flotation reagents control problem is formulated as a two-stage optimization problem (problem 1 and problem 2). Specifically, the feedforward compensation component can be obtained by solving the problem 1 based on feedforward control principle, which can be used in disturbance rejection. After that, a value-iteration based ADP algorithm is applied to deal with the problem 2 in order to derive the optimal feedback gain matrix. Thus, a data-driven optimal reagents control strategy is designed to force the flotation indexes(concentrate grade and tailing grade) to track the desired values, and keep the reagents consumption to a minimum. In the end, the convergence and stability of the proposed data-driven method are proved by the simulation with industrial data.