摘要
磨矿粒度和循环负荷是磨矿过程产品质量与生产效率的关键运行指标,相对于底层控制偏差,回路设定值对其影响要严重的多.然而,磨矿过程受矿石成分与性质、设备状态等变化因素影响,运行工况动态时变,难以建立模型,因此难以通过传统的模型方法优化回路设定值.本文将增强学习与案例推理相结合,提出一种数据驱动的磨矿过程设定值优化方法.首先根据当前运行工况,采用基于Prey-Predator优化的案例推理方法,决策出可行的基于Elman神经网络的Q函数网络模型;然后利用实际运行数据,在增强学习的框架下,根据Q函数网络模型优化回路设定值.在基于METSIM的磨矿流程模拟系统上进行实验研究,结果表明所提方法可根据工况变化在线优化回路设定值,实现磨矿运行指标的优化控制.
In grinding processes,particle size and circulating load are two key operation indexes for product quality and production efficiency.With respect to the economic performance,the basic loop controller performance is most probably not as important as the right selection of the loop set-points.The industrial grinding processes,however,are affected by the factors such as composition and properties of ore,the equipment status and so on.When the large fluctuation of the factors occurs,the operation will be time-varying,thereby making the process modeling very difficult.Therefore,it is hard to employ the traditional model-based methods to optimize the loop set-points.In this paper,a data-driven optimalsetting control method is proposed by using case-based reasoning(CBR)and reinforcement learning(RL)technologies.The method first employs a Prey-Predator optimization-based CBR method to determine a feasible Elman neural networkbased Q function model in accordance with current operation condition.Then,under the RL framework,the Q function model is adopted to optimize the loop set-points according to the operation data.Experiments studies are carried out in a METSIM-based grinding simulation system.Results show that the proposed method can realize the optimization control of the grinding operation indexes by optimizing the loop set-points online according to the varied operation conditions.
作者
代伟
王献伟
路兴龙
柴天佑
DAI Wei;WANG Xian-wei;LU Xing-long;CHAI Tian-you(School of Information and Control Engineering,China University of Mining and Technology,Xuzhou Jiangsu 221116,China;State Key Laboratory of Synthetical Automation for Process Industries,Northeastern University,Shenyang Liaoning 110819,China)
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2019年第1期53-64,共12页
Control Theory & Applications
基金
国家自然科学基金项目(61603393
61741318)
江苏省自然科学基金项目(BK20160275)
中国博士后科学基金项目(2015M581885
2018T110571)
东北大学流程工业综合自动化国家重点实验室开放课题(PAL–N201706)资助~~
关键词
案例推理
增强学习
神经网络
设定值优化
磨矿过程
case-based reasoning
reinforcement learning
neural network
set-point optimization
grinding process
