Machine vision and machine learning have been researched widely in froth flotation and the technology continues to benefit from advances in computer technology. The feature engineering leads to a predicted resistance in the machine learning pipelines, especially in coal flotation. This study sought to examine the performance of feature engineering of coal flotation froth image on ash content prediction with an industrial dataset. In order to evaluate the practical use in industry, the morphoscopic (3 features), statistical (gray levels histogram (5 features), gray level co-occurrence matrix (24 features), statistical modeling (48 features)) and color spaces (18 features) are used to prepare feature engineering. Correlation matrix are used to investigate the relationship between features and ash content. The support vector regression is used to predict ash content. The evaluation of the model performance shows that the principal component analysis can effectively improve the accuracy. When the feature dimension is reduced to 14 by the principal component analysis, the optimal RMSE is 0.6331, the R² value is 0.78. The feature engineering of coal flotation froth image in this paper can make a good prediction of the coal flotation concentrate ash content. Furthermore, the results can be used as the theoretical basis for the intelligent construction of flotation.

机器视觉和机器学习在泡沫浮选方面得到了广泛的研究，并且该技术继续受益于计算机技术的进步。特征工程导致机器学习管道中的预测阻力，特别是在煤浮选中。本研究试图用工业数据集检验煤浮选泡沫图像特征工程对灰分预测的性能。为了评估在工业中的实际应用，形态学（3个特征）、统计（灰度直方图（5个特征）、灰度共生矩阵（24个特征）、统计建模（48个特征））和色彩空间（18个特征） features) 用于准备特征工程。相关矩阵用于研究特征与灰分含量之间的关系。支持向量回归用于预测灰分含量。模型性能评估表明，主成分分析可以有效提高精度。当特征维数通过主成分分析降到14时，最优RMSE为0.6331，R²值为 0.78。本文的煤浮选泡沫图像特征工程可以很好地预测煤的浮选精矿灰分含量。此外，研究结果可为浮选智能化建设提供理论依据。