数据驱动的复杂磨矿过程运行优化控制方法

摘要

针对赤铁矿磨矿过程的磨矿粒度(Grinding particle size, GPS)与控制回路输出之间的动态特性难以用数学模型描述,且磨矿粒度不能在线测量,并受矿石成分与性质频繁波动干扰,难以采用已有运行优化方法的难题,结合磨矿过程的特点,利用数据,采用神经网络,提出由回路预设定值优化、性能指标估计、优化设定值评价以及磨矿粒度软测量组成的数据驱动的磨矿过程运行优化控制方法。该方法由磨矿粒度软测量估计矿浆粒度,通过回路预设定值优化模块求得使性能指标估计值接近最优值的回路预设定值,经优化设定值评估产生回路设定值,最后通过控制回路跟踪设定值,将矿浆粒度控制在目标值范围内并尽可能的接近目标值。通过研制的运行优化与控制研究平台,采用实际运行数据进行仿真实验,表明所提方法的有效性。%For hematite grinding processes, it is not only difficult to describe the dynamics between grinding particle size (GPS) and control loop outputs by using mathematical formulas, but also impossible to measure the GPS online. Moreover, the process is sensitively influenced by the frequent fluctuation of ore compositions and properties. The above practical problems make the existing control methods unable to be applied. In this paper, a data-driven optimal operational control approach, which consists of loop pre-setting optimizer, performance index estimator, optimum setpoints critic, and GPS soft-sensor, is proposed by using neural network. In this approach, the GPS is estimated by the GPS soft-sensor first, then the loop pre-setting optimizer generates the optimum loop pre-setting values by making performance index estimate close to its optimal value. Finally, via the optimum setpoints critic the optimal setpoint is obtained. With the help of the control loops, the GPS can be maintained inside its desired range, and close to its target value as much as possible. Experiments using a developed research platform of optimal operational control are carried out with real time data to show the effectiveness of the proposed method.