摘要
矿物浮选过程具有强非线性、扰动大及工况切换频繁等特点,传统控制方法难以兼顾动态响应与全局优化。为此,文章提出一种融合多模型结构与自适应动态规划的优化控制方法。首先构建基于白鲸优化算法的BWO-RNN多工况建模框架,通过全局寻优提升模型对不同工况输入—输出关系的拟合能力。在控制阶段,采用增广状态空间的并行ADP跟踪控制器,利用策略迭代实现控制律在线更新。进一步引入基于累积误差的模型切换机制,在各子控制器间自适应选择最优通道,增强系统对扰动与结构变化的适应性。仿真结果表明,该方法在多工况下均能实现精矿品位的稳定跟踪,具备良好的控制性能与工程应用潜力。
[Objective]The mineral flotation process plays a pivotal role in the extraction and purification of nonferrous metal ores such as copper and silver.However,its inherent characteristics—strong nonlinearity,substantial disturbances,and frequent operating condition changes—pose major challenges to traditional control strategies,which often fail to achieve dynamic adaptability and global optimality.This research aims to develop an intelligent control framework capable of maintaining high concentrate grades and system stability under fluctuating production conditions.[Methods]To address process variability and model mismatch,a multimodel adaptive optimal control method based on adaptive dynamic programming(ADP)is proposed.First,a multicondition modeling framework is established using recurrent neural networks(RNNs),with the beluga whale optimization(BWO)algorithm employed to globally optimize RNN learning rates.This BWO-RNN modeling approach significantly enhances the generalization capability and fitting accuracy across different operating scenarios.In the control stage,a parallel ADP tracking controller within an augmented state-space framework is adopted,enabling real-time policy iteration to compute the optimal control law.Furthermore,a cumulative error–based model-switching mechanism is introduced to dynamically select the most suitable submodel and controller in response to changing process conditions,thereby ensuring robust system performance and seamless controller transitions.[Results]The proposed framework was validated using simulation data from a copper–silver flotation plant.Compared to traditional model-based control and(particle swarm optimization)PSO-optimized RNNs,the BWO-RNN model achieved higher fitness values and shorter training times.In control experiments involving transitions among three typical operating conditions,the multimodel ADP controller demonstrated superior tracking accuracy for copper and silver concentrate grades,with lower overshoot and faster response times than baseline controllers.In addition,the model-switching strategy effectively suppressed oscillations and maintained stability even under abrupt changes in operating conditions,demonstrating strong robustness.The overall control cost was reduced by 4.3%,indicating improved reagent efficiency and operational economy.[Conclusions]This study presents a novel adaptive control framework integrating a multimodel structure,BWO-RNN–based data-driven modeling,ADP-based optimal tracking control,and a dynamic model-switching mechanism.The proposed method effectively addresses the nonlinear,time-varying,and disturbance-prone characteristics of the flotation process.Simulation results confirm its capability to achieve stable and accurate concentrate grade tracking across diverse operational scenarios,offering a promising approach for industrial deployment in mineral flotation systems.Future work will focus on extending the framework to real-time online identification and practical field implementation in industrial flotation plants.
作者
王康
易俊轩
李晓理
WANG Kang;YI Junxuan;LI Xiaoli(School of Information Science and Technology,Beijing University of Technology,Beijing 100124,China)
出处
《实验技术与管理》
北大核心
2025年第10期22-28,共7页
Experimental Technology and Management
基金
北京市教育科学“十四五”规划2022年度优先关注课题“首都高校研究生教育质量提升研究”(CDEA22009)。
关键词
浮选
多模型自适应控制
优化控制
自适应动态规划
flotation
multimodel adaptive control
optimal control
adaptive dynamic programming
