Estimation of coal vitrinite reflectance based on random forest and dendritic network
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摘要: 镜质组平均最大反射率是表征煤化程度的重要指标，在确定煤级、鉴别混煤和指导炼焦配煤中起关键作用。传统反射率测定方法费时耗力，且测量结果的主观性较强，致使实验室间鉴定结果的可比性差。针对该问题，提出一种基于随机森林（RF）和树突网络（DDNet）的煤镜质组反射率估计方法，主要包括煤岩显微图像分割、镜质组识别和镜质组平均最大反射率预测3个部分。利用手肘法和K−Means算法对显微图像聚类，以实现不同显微组分区域的分割；采用人工少数类过采样法（SMOTE）对少数类样本过采样，以改善煤岩中镜质组与非镜质组区域样本的不均衡问题；利用基于DDNet的回归算法实现镜质组平均最大反射率的估计，构建回归模型时从镜质组区域中选择多个41×41像素的方形窗口并提取其灰度特征，以提高算法的鲁棒性，其决定系数达到0.990。实验结果表明：采用手肘法自动确定K−Means算法的参数K，具有良好的自适应能力，能够自动区分不同类别数的显微组分；SMOTE方法可有效避免模型因过度学习样本先验信息而导致对多数类识别好、少数类识别差的问题，提高分类准确度，其中基于RF的识别模型准确率达到97.0%；建立了7种回归估计模型，其中DDNet回归模型性能最佳，决定系数达到0.990，预测结果与实际值高度契合，验证了所提方法的可行性。Abstract: The mean maximum vitrinite reflectance is an important indicator of the degree of coalification, and plays a key role in determining coal grade, identifying mixed coal, and guiding coking coal blending. The traditional reflectance measurement methods are time-consuming and labor-intensive. The subjectivity of measurement results is strong, resulting in poor comparability of identification results between laboratories. To address this issue, a method for estimating coal vitrinite reflectance based on random forests(RF) and dendritic networks(DDNet) is proposed. It mainly includes three parts: coal rock microscopic image segmentation, vitrinite recognition, and mean maximum vitrinite reflectance prediction. The elbow method and K-Means algorithm are used to achieve segmentation of different maceral regions of the clustering microscopic images. The artificial minority oversampling method (SMOTE) is used to oversample minority samples to improve the imbalance between vitrinite and nonvitrinite regional samples in coal and rock. The DDNet-based regression algorithm is used to estimate the mean maximum vitrinite reflectance. When building a regression model, multiple 41×41 pixel square windows are selected from the vitrinite regions to extract their grey scale features. It improves the robustness of the algorithm, with a determination coefficient of 0.990. The experimental results show that using elbow method to automatically determine the parameter K of the K-Means algorithm, which has good adaptive capability. It can automatically distinguish different types of microscopic components. The SMOTE method can effectively avoid the problem of over-learning sample prior information, which leads to good recognition of the majority class and poor recognition of the minority class. It improves classification accuracy. Among them, the recognition model based on RF has an accuracy rate of 97.0%. Seven regression estimation models have been established, among which the DDNet regression model has the best performance, with a determination coefficient of 0.990. The predicted results are highly consistent with the actual values, verifying the feasibility of the proposed method.
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图 5 DDNet模型的预测值与真实值对比
Figure 5. Comparison of predicted value of DDNet and the ground truth
表 1 过采样、下采样处理前后结果对比
Table 1 Comparison of experimental results before and after oversampling and down-sampling
数据处理 分类算法 准确率 查准率 召回率 F1分数 处理前 CART 0.95±0.02 0.88±0.06 0.87±0.07 0.87±0.05 KNN 0.95±0.01 0.90±0.05 0.86±0.05 0.88±0.04 SVM 0.95±0.01 0.88±0.06 0.88±0.06 0.88±0.04 RF 0.96±0.01 0.92±0.05 0.85±0.07 0.88±0.04 RUS CART 0.93±0.02 0.77±0.08 0.92±0.05 0.83±0.05 KNN 0.95±0.02 0.82±0.06 0.93±0.04 0.87±0.04 SVM 0.95±0.02 0.81±0.08 0.95±0.04 0.87±0.05 RF 0.95±0.02 0.82±0.06 0.96±0.04 0.89±0.04 SMOTE结合RUS CART 0.94±0.02 0.83±0.06 0.87±0.06 0.85±0.04 KNN 0.96±0.01 0.86±0.06 0.92±0.05 0.89±0.03 SVM 0.96±0.01 0.88±0.05 0.90±0.04 0.89±0.03 RF 0.97±0.01 0.91±0.05 0.93±0.04 0.92±0.03 SMOTE CART 0.95±0.01 0.85±0.05 0.88±0.05 0.87±0.04 KNN 0.96±0.01 0.87±0.05 0.91±0.05 0.89±0.03 SVM 0.96±0.01 0.88±0.05 0.90±0.05 0.89±0.03 RF 0.97±0.01 0.92±0.04 0.93±0.04 0.92±0.03 
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表 2 回归算法测试结果对比
Table 2 Comparison of test results of regression algorithms
算法 均方误差 平均绝
对误差决定系数 SVR 0.014 0.080 0.885 AdaBoost 0.010 0.078 0.919 KNN 0.010 0.071 0.925 Gradient Boosting 0.009 0.071 0.926 RF 0.009 0.070 0.926 FNN 0.033 0.064 0.735 DDNet 0.001 0.027 0.990
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[1] 田英奇,张卫华,沈寓韬,等. 镜质组反射率指导优化配煤炼焦方案的研究[J]. 煤炭科学技术,2016,44(4):162-168. TIAN Yingqi,ZHANG Weihua,SHEN Yutao,et al. Research on optimization of coal blending coking guided by vitrinite reflectance[J]. Coal Science and Technology,2016,44(4):162-168.
[2] 俞楠,邹冲,刘诗薇,等. 利用镜质组反射率鉴定兰炭与煤粉互混样的方法解析[J]. 冶金能源,2022,41(5):13-18. YU Nan,ZOU Chong,LIU Shiwei,et al. Analysis of the method for identifying the mixed samples of pulverized coal and semi-coke by vitrinite reflectance[J]. Energy for Metallurgical Industry,2022,41(5):13-18.
[3] 宋孝忠,张群. 煤岩显微组分组图像自动识别系统与关键技术[J]. 煤炭学报,2019,44(10):3085-3097. SONG Xiaozhong,ZHANG Qun. Automatic image recognition system and key technologies of maceral group[J]. Journal of China Coal Society,2019,44(10):3085-3097.
[4] SANTOS R B M,AUGUSTO K S,IGLESIAS J C Á,et al. A deep learning system for collotelinite segmentation and coal reflectance determination[J]. International Journal of Coal Geology,2022,263:104111-104122. DOI: 10.1016/j.coal.2022.104111
[5] VAN NIEKERK D,MITCHELL G D,MATHEWS J P. Petrographic and reflectance analysis of solvent-swelled and solvent-extracted South African vitrinite-rich and inertinite-rich coals[J]. International Journal of Coal Geology,2009,81(1):45-52. DOI: 10.1016/j.coal.2009.10.021
[6] MLYNARCZUK M,GÓRSZCZYK A,ŚLIPEK B. The application of pattern recognition in the automatic classification of microscopic rock images[J]. Computers & Geosciences,2013,60:126-133.
[7] WANG Hongdong, LEI Meng, CHEN Yilin, et al. Intelligent identification of maceral components of coal based on image segmentation and classification[J]. Applied Sciences, 2019, 9（16）. DOI: 10.3390/app9163245 .
[8] LEI Meng, RAO Zhongyu, WANG Hongdong, et al. Maceral groups analysis of coal based on semantic segmentation of photomicrographs via the improved U-net[J]. Fuel, 2021, 294. DOI: 10.1016/j.fuel.2021.120475.
[9] 王培珍,余晨,薛子邯,等. 基于迁移学习的煤岩壳质组显微组分识别模型[J]. 煤炭科学技术,2022,50(1):220-227. WANG Peizhen,YU Chen,XUE Zihan,et al. Transfer learning based identification model for macerals of exinite in coal[J]. Coal Science and Technology,2022,50(1):220-227.
[10] ENGLAND B M,MIKKA R A,BAGNALL E J,et al. Petrographic characterization of coal using automatic image analysis[J]. Journal of Microscopy,1979,116(3):329-336. DOI: 10.1111/j.1365-2818.1979.tb00218.x
[11] 王洪栋. 基于机器学习的煤岩显微图像分析研究[D]. 徐州: 中国矿业大学, 2019. WANG Hongdong. Research on photomicrograph analysis of coal based on machine learning[D]. Xuzhou: China University of Mining and Technology, 2019.
[12] WANG Hongdong,LEI Meng,LI Ming,et al. Intelligent estimation of vitrinite reflectance of coal from photomicrographs based on machine learning[J]. Energies,2019,12(20):1-16.
[13] ONUMANYI A J, MOLOKOMME D N, ISAAC S J, et al. AutoElbow: an automatic elbow detection method for estimating the number of clusters in a dataset[J]. Applied Sciences, 2022, 12（15）. DOI: 10.3390/app12157515.
[14] SAMMOUDA R, EL-ZAART A. An optimized approach for prostate image segmentation using K-means clustering algorithm with elbow method[J]. Computational Intelligence and Neuroscience, 2021, 2021. DOI: 10.1155/2021/4553832.
[15] BOKHARE A, BHAGAT A, BHALODIA R. Multi-layer perceptron for heart failure detection using SMOTE technique[J]. SN Computer Science, 2023, 4（2）. DOI: 10.1007/s42979-022-01596-x.
[16] ISHAQ A,SADIQ S,UMER M,et al. Improving the prediction of heart failure patients' survival using SMOTE and effective data mining techniques[J]. IEEE Access,2021,9:39707-39716. DOI: 10.1109/ACCESS.2021.3064084
[17] LIU Gang. It may be time to improve the neuron of artificial neural network[EB/OL]. [2023-01-20]. https://www.techrxiv.org/articles/preprint/It_may_be_time_to_perfect_the_neuron_of_artificial_neural_network/12477266.
[18] LIU Gang,WANG Jing. Dendrite net:a white-box module for classification,regression,and system identification[J]. IEEE Transactions on Cybernetics,2022,52(12):13774-13787. DOI: 10.1109/TCYB.2021.3124328
[19] MARIUSZ M,MARTA S. The application of artificial intelligence for the identification of the maceral groups and mineral components of coal[J]. Computers & Geosciences,2017,103:133-141.
[20] 高涛,冯松宝. 煤的显微组分特征研究综述[J]. 能源技术与管理,2021,46(4):15-16,20. GAO Tao,FENG Songbao. Literature review on property of macerals of coals[J]. Energy Technology and Management,2021,46(4):15-16,20.
[21] LI Na,HAO Huizhen,GU Qing,et al. A transfer learning method for automatic identification of sandstone microscopic images[J]. Computers & Geosciences,2017,103:111-121.
[22] WU Zhuang,JIANG Shanshan,ZHOU Xiaolei,et al. Application of image retrieval based on convolutional neural networks and Hu invariant moment algorithm in computer telecommunications[J]. Computer Communications,2020,150:729-738. DOI: 10.1016/j.comcom.2019.11.053
[23] MEENAKSHI G,GAURAV D. A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variants[J]. Neural Computing & Applications,2021,33(4):1311-1328.
[24] WANG Qi, HUANG Wei, ZHANG Xueting, et al. GLCM: global-local captioning model for remote sensing image captioning[J]. IEEE Transactions on Cybernetics （Early Access）, 2022: 1-13. DOI: 10.1109/TCYB.2022.3222606.
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