一种基于鲁棒随机向量函数链接网络的磨矿粒度集成建模方法

作为磨矿过程的主要生产质量指标, 磨矿粒度是实现磨矿过程闭环优化控制的关键.将磨矿粒度控制在一定范围内能够提高选别作业的精矿品位和有用矿物的回收率, 并减少有用矿物的金属流失.由于经济和技术上的限制, 磨矿粒度的实时测量难以实现.因此, 磨矿粒度的在线估计显得尤为重要.然而, 目前我国所处理的铁矿石大多数为性质不稳定的赤铁矿, 其矿浆颗粒存在磁团聚现象, 所采集的数据存在大量异常值, 使得利用数据建立的磨矿粒度模型存在较大误差.同时, 传统前馈神经网络在磨矿粒度数据建模过程中存在收敛速度慢、易于陷入局部最小值等缺点, 且单一模型泛化性能较差, 现有的集成学习在异常值干扰下性能严重下降.因此, 本文在改进的随机向量函数链接网络(random vector functional link networks, RVFLN)的基础上, 将Bagging算法与自适应加权数据融合技术相结合, 提出一种基于鲁棒随机向量函数链接网络的集成建模方法, 用于磨矿粒度集成建模.所提方法首先通过基准回归问题进行了实验研究, 然后采用磨矿工业实际数据进行验证, 表明其有效性. 

Grinding process particle size modeling method using robust RVFLN-based ensemble learning

As a key production quality index of grinding process, particle size is of great importance to closed-loop optimization and control. This is because controlling particle within a proper range can improve the concentrate grade, enhance the recovery rate of useful minerals, and reduce the loss of metal in the sorting operation; thus, the particle size determines the overall performance of the grinding process. In fact, it is not easy to optimize or control the practical industrial process because the optimal operation largely depends on a good measurement of particle size of grinding process; however, it is difficult to realize the real-time measurement of particle size because of limitations of economy or technique. Employing soft sensor techniques is necessary to solve the problem of particle size estimation, which is particularly important for the actual grinding processes. Considering that soft sensors are applicable in many fields, the data-driven soft sensor will be a useful tool for achieving particle size estimation. However, most of the iron ores processed in China are characterized by hematite with unstable properties, and the slurry particles exhibit magnetic agglomeration, giving rise to a large number of outliers in the collected data. In this case, there are gross errors in the particle size estimation model constructed based on the data and thus unreliable measurements. Meanwhile, the traditional feedforward neural networks have the disadvantages of slow convergence speed and easily fall into local minimum during the prediction process. A single model tends to lack superiority in sound generalization, and the performance of existing ensemble learning methods will be worse under outlier interference. Therefore, in this study, based on the improved random vector functional link networks (RVFLN), the Bagging algorithm is incorporated into an adaptive weighted data fusion technique to develop an ensemble learning method for particle size estimation of grinding processes. Experimental studies were first conducted through benchmark regression issues and then validated by the samples collected from an actual grinding process, indicating the effectiveness of the proposed method.