College of Mining Engineering, Taiyuan University of Technology, Taiyuan, Shanxi, China.
PLoS One. 2017 Oct 17;12(10):e0186553. doi: 10.1371/journal.pone.0186553. eCollection 2017.
Flotation is an important part of coal preparation, and the flotation column is widely applied as efficient flotation equipment. This process is complex and affected by many factors, with the froth depth and reagent dosage being two of the most important and frequently manipulated variables. This paper proposes a new method of switching and optimizing control for the coal flotation process. A hybrid model is built and evaluated using industrial data. First, wavelet analysis and principal component analysis (PCA) are applied for signal pre-processing. Second, a control model for optimizing the set point of the froth depth is constructed based on fuzzy control, and a control model is designed to optimize the reagent dosages based on expert system. Finally, the least squares-support vector machine (LS-SVM) is used to identify the operating conditions of the flotation process and to select one of the two models (froth depth or reagent dosage) for subsequent operation according to the condition parameters. The hybrid model is developed and evaluated on an industrial coal flotation column and exhibits satisfactory performance.
浮选是选煤的重要环节，而浮选柱作为高效的浮选设备得到了广泛的应用。该过程较为复杂，受到许多因素的影响，其中泡沫层高度和药剂用量是两个最重要和经常被操作的变量。本文提出了一种浮选过程切换和优化控制的新方法。该方法使用工业数据建立和评估了混合模型。首先，采用小波分析和主成分分析（PCA）对信号进行预处理。其次，基于模糊控制构建了优化泡沫层高度设定点的控制模型，并设计了基于专家系统的优化药剂用量的控制模型。最后，采用最小二乘支持向量机（LS-SVM）识别浮选过程的运行条件，并根据条件参数选择两个模型（泡沫层高度或药剂用量）中的一个进行后续操作。该混合模型在工业浮选柱上进行了开发和评估，表现出了令人满意的性能。
