基于改进型SVMR技术的煤泥浮选智能优化控制方法

Intelligent optimization control method of slime flotation based on improved SVMR technology

针对传统煤泥浮选控制方法计算速度慢、检测精度低等缺陷,提出一种基于BTM二叉树模型的支持向量机回归(SVRM)优化策略,从而对煤泥浮选过程所需的药剂添加量进行预测,然后根据预测结果,通过PLC实现药剂添加量的智能控制。仿真结果表明,与基础SVRM算法相比,基于BTM的SVMR优化算法随着叶节点的不断增加,计算速度得到了显著提升;同时预测结果与所需的实际添加结果贴近,实现了在复杂的工业浮选过程中煤泥浮选的精确控制。

Aiming at the defects of the traditional slime flotation control method, such as slow calculation speed and low detection accuracy, a Support vector machine regression(SVRM) optimization strategy based on BTM binary tree model was proposed to construct the reagent addition model required in the flotation process. Aiming at the instability and complexity of industrial flotation process, an adaptive control strategy is proposed to enhance the stability of the intelligent control system of flotation process. The simulation results show that, compared with the basic SVRM algorithm, the calculation speed of the BTM-SVMR optimization algorithm is significantly improved with the increasing of leaf nodes. The fuzzy adaptive control strategy also strengthens the stability of the intelligent flotation control system and realizes the intelligent control of slime flotation system in the complex industrial flotation process.