Coal is widely used in industrial applications such as carbonization and coke production for steel making, combustion, or gasification to generate electricity, and liquefaction to generate petrochemical feedstock. The utility of a coal is dictated by its properties, commonly referred to as its rank, type, and grade. Coal type or maceral composition and coal rank determination by vitrinite (collotelinite) reflectance are traditionally conducted manually by trained petrographers using reflected light optical microscopy and bulk chemical tests. This study developed an automatic method based on machine learning for rank determination on petrographic images of coal that might improve the efficiency of this process and potentially eliminate operator subjectivity. Firstly, a Mask R-CNN-based architecture deep learning approach was developed to identify and segment the collotelinite maceral, which is fundamental for rank analysis, as rank can be assessed by the reflectance of this maceral. Secondly, an image processing method was developed to analyze the collotelinite segmented images and determine coal rank by associating the grey values with the reflectance. For the segmentation, five samples were used to train the network, 174 images were used for training, and 86 were used in the test set, with over 80% success rates. Four of those five samples were used alongside another eight to determine the rank. The samples ranged from 0.97% to 1.8% reflectance. The root mean square error calculated between the proposed method and the reference values of reflectance was 0.0978%.

煤炭广泛用于工业应用，例如用于炼钢的碳化和焦炭生产、燃烧或气化以发电，以及液化以产生石化原料。煤的效用取决于其特性，通常称为其等级、类型和等级。传统上，通过镜质体 (collotelinite) 反射率确定煤的类型或矿物成分和煤阶是由训练有素的岩相学家使用反射光光学显微镜和大宗化学测试手动进行的。本研究开发了一种基于机器学习的自动方法，用于确定煤岩相图像的等级，这可能会提高该过程的效率并可能消除操作员的主观性。首先，开发了一种基于 Mask R-CNN 架构的深度学习方法来识别和分割 collotelinite maceral，这是等级分析的基础，因为等级可以通过该 maceral 的反射率来评估。其次，开发了一种图像处理方法，通过将灰度值与反射率相关联，对煤灰石分割图像进行分析，确定煤阶。分割方面，5个样本用于训练网络，174张图像用于训练，86张用于测试集，成功率超过80%。这五个样本中的四个与另外八个一起用于确定排名。样品的反射率范围为 0.97% 至 1.8%。所提方法与反射率参考值计算的均方根误差为0.0978%。这是排名分析的基础，因为排名可以通过这个maceral的反射率来评估。其次，开发了一种图像处理方法，通过将灰度值与反射率相关联，对煤灰石分割图像进行分析，确定煤阶。分割方面，5个样本用于训练网络，174张图像用于训练，86张用于测试集，成功率超过80%。这五个样本中的四个与另外八个一起用于确定排名。样品的反射率范围为 0.97% 至 1.8%。所提方法与反射率参考值计算的均方根误差为0.0978%。这是排名分析的基础，因为排名可以通过这个maceral的反射率来评估。其次，开发了一种图像处理方法，通过将灰度值与反射率相关联，对煤灰石分割图像进行分析，确定煤阶。分割方面，5个样本用于训练网络，174张图像用于训练，86张用于测试集，成功率超过80%。这五个样本中的四个与另外八个一起用于确定排名。样品的反射率范围为 0.97% 至 1.8%。所提方法与反射率参考值计算的均方根误差为0.0978%。开发了一种图像处理方法，通过将灰度值与反射率相关联，分析煤层石分割图像并确定煤阶。分割方面，5个样本用于训练网络，174张图像用于训练，86张用于测试集，成功率超过80%。这五个样本中的四个与另外八个一起用于确定排名。样品的反射率范围为 0.97% 至 1.8%。所提方法与反射率参考值计算的均方根误差为0.0978%。开发了一种图像处理方法，通过将灰度值与反射率相关联，分析煤层石分割图像并确定煤阶。分割方面，5个样本用于训练网络，174张图像用于训练，86张用于测试集，成功率超过80%。这五个样本中的四个与另外八个一起用于确定排名。样品的反射率范围为 0.97% 至 1.8%。所提方法与反射率参考值计算的均方根误差为0.0978%。这五个样本中的四个与另外八个一起用于确定排名。样品的反射率范围为 0.97% 至 1.8%。所提方法与反射率参考值计算的均方根误差为0.0978%。这五个样本中的四个与另外八个一起用于确定排名。样品的反射率范围为 0.97% 至 1.8%。所提方法与反射率参考值计算的均方根误差为0.0978%。