College of Mining Engineering, Taiyuan University of Technology

针对煤炭重介分选控制过程中的精煤灰分测量延迟问题,基于随机森林算法(RandomForest,RF)与经验模态分解(EmpiricalModeDecomposition,EMD)将工业现场实测的密度、磁性物含量、灰分数据进行降噪处理后,建立了重介分选系统数学模型;提出了灰分前置对应方法:用t时刻的输入(悬浮液密度值m、磁性物含量值n)对应t+T(T为延迟时间)时刻的输出(精煤灰分值h)进行模型训练。在对BP神经网络、随机森林算法以及基于最小二乘原理的算法进行对比寻优后,最终得出随机森林算法的建模效果最优。研究结果表明:可将随机森林估计值作为指导值用于煤炭分选工业现场,以提升重介分选效率,改善精煤煤质。

Aiming at the measurement delays of clean coal in dense medium separation control, based on the random forest algorithm (Random Forest) and empirical mode decomposition, the density, magnetic content and ash data measured in the industrial site were denoised, and the mathematical model of the heavy medium sorting system was established. In order to solve the problem of high ash delay in the re-intermediate sorting process, a pre-correspondatory method for ash separation was proposed: the input at t moment (density value m magnetic content value M) corresponds to the output (ash value h) at the moment of t+T (T is the delay time) for model training. After comparing and optimizing BP neural network, random forest algorithm and algorithm based on least squares principle, it was finally concluded that the random forest algorithm has the best modeling effect. Then, the random forest estimate can be used as a guide value for coal sorting industrial sites, which is helpful to improve the efficiency of heavy medium sorting and improve the quality of refined coal.

国家自然科学基金项目(52274157);国家自然科学基金面上项目(51974191);内蒙古自治区重点专项(2022EEDSKJXM010);山西省省筹资助回国留学人员项目(2021-059)

李 哲, 孟巧荣, 王然风, 等. 基于 EMD-RF 算法的重介精煤灰分预测研究 [J]. 煤炭工程, 2023, 55(10):174-179.