机械臂辅助浮选尾矿分层灰分检测实验研究

doi:10. 11799/ ce202510025

摘要/Abstract

摘要：

针对传统浮选尾矿灰分检测多依赖表层矿浆颜色、相关性不高且易受环境干扰等问题，提出并实现了一种基于机械臂辅助分层的尾矿灰分检测新方法。首先，利用物理力学原理分析煤与矸石在静置及流动倾倒过程中的沉降速度差异，为尾矿中煤矸分层提供理论支撑。随后，基于DH参数法建立六自由度机械臂模型，并借助MATLAB Robotics Toolbox对其运动学和工作空间进行仿真，验证了所选机械臂在选煤厂空间环境下可达且运行平稳。为了实现快速高效的在线监测，进一步采用TOPP-RA算法对机械臂的时间最优轨迹进行规划，在满足速度与加速度约束的同时，显著缩短操作周期。实验过程中，机械臂通过合理的倾斜角度与振动动作，使尾矿中煤与矸石产生明显分层；随后利用图像处理技术提取分层后煤、矸石的面积占比，将其与实际化验得到的灰分值进行对比分析。结果表明，在多煤种、多工况条件下，煤矸占比与尾矿灰分的皮尔逊相关系数可达0.42，显著优于传统基于表层矿浆颜色的检测方式。

关键词:

智能分选 , 尾矿灰分预测 , 机械臂建模 , 机械臂辅助尾矿分层 , 轨迹优化

Abstract:

In order to solve the problems that the traditional flotation tailings ash detection mostly depends on the color of the surface slurry, the correlation is not high and it is susceptible to environmental interference, a new method for tailings ash detection based on robotic arm-assisted stratification is proposed and implemented. Firstly, the difference in settlement velocity between coal and gangue in the process of static and flow dumping was analyzed by using the principles of physical mechanics, so as to provide theoretical support for the stratification of coal gangue in tailings. Subsequently, a six-degree-of-freedom manipulator model was established based on the DH parameter method, and its kinematics and working space were simulated with the help of MATLAB Robotics Toolbox, which verified that the selected manipulator could reach and run smoothly in the space environment of the coal preparation plant. In order to achieve fast and efficient online monitoring, this paper further uses the TOPP-RA algorithm to plan the time-optimal trajectory of the robotic arm, which significantly shortens the operation cycle while satisfying the speed and acceleration constraints. During the experiment, the manipulator arm made the coal and gangue in the tailings obviously stratified through reasonable tilt angle and vibration action. Then, the image processing technology was used to extract the area proportion of coal and gangue after stratification, and compared it with the ash value obtained by the actual laboratory analysis. The results show that the Pearson correlation coefficient between coal gangue proportion and tailings ash can reach 0.42 under the conditions of multiple coal types and working conditions, which is significantly better than the traditional detection method based on the color of surface slurry. This study provides a feasible automation solution for the prediction of flotation tailings ash, and lays an important foundation for the intelligent construction and refined production control of subsequent coal preparation plants.

中图分类号:

TD94

刘舒通, 王然风, 付翔, 魏凯, 柴宇青, 窦治衡, 任涵弛. 机械臂辅助浮选尾矿分层灰分检测实验研究[J]. 煤炭工程, 2025, 57(10): 202-210.

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