School of Information Science and Engineering, Northeastern University, Shenyang 110819, China.
School of Information Science and Engineering, Northeastern University, Shenyang 110819, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2023 Feb 15;287(Pt 2):122042. doi: 10.1016/j.saa.2022.122042. Epub 2022 Oct 25.
Proximate analysis of coal is of profound significance for understanding coal quality and promoting rational utilization of coal resources. Traditional coal proximate analysis mainly uses chemical analysis methods, which have the disadvantages of slow speed and high cost. This paper proposed an approach combining reflectance spectroscopy with deep learning (DL) for rapid proximate analysis of coal. First, 80 sets of coal spectral data are enhanced by data augmentation, outlier detection, and dimensional transformation to improve the number and quality of samples. Then, an analytical model combining dilated convolution, multi-level residual connection, and a two-hidden-layer extreme learning machine (TELM), named DR_TELM, was proposed. The model extracted effective features from coal spectral data by a convolutional neural network (CNN) and utilized TELM as a regressor to achieve feature identification and content prediction. The experimental results showed that DR_TELM achieved coefficients of determination (R) of 0.981, 0.989, 0.990, 0.985, 0.989 and root mean square errors (RMSE) of 0.533, 1.833, 1.111, 1.808, 0.723 for the content prediction of moisture, ash, volatile matter, fixed carbon and higher heating value (HHV), respectively. And while ensuring high accuracy, the test time is only 0.034 s. It is fully demonstrated that DR_TELM can rapidly and accurately analyze coal.
煤的工业分析对于了解煤质和促进煤炭资源的合理利用具有重要意义。传统的煤工业分析主要采用化学分析方法，存在速度慢、成本高的缺点。本文提出了一种结合反射光谱和深度学习（DL）的快速煤工业分析方法。首先，通过数据增强、异常值检测和维度变换对 80 组煤光谱数据进行增强，以提高样本数量和质量。然后，提出了一种结合扩张卷积、多层次残差连接和双隐层极限学习机（TELM）的分析模型，称为 DR_TELM。该模型通过卷积神经网络（CNN）从煤光谱数据中提取有效特征，并利用 TELM 作为回归器实现特征识别和内容预测。实验结果表明，DR_TELM 对水分、灰分、挥发分、固定碳和高位发热量（HHV）的含量预测的决定系数（R）分别为 0.981、0.989、0.990、0.985、0.989，均方根误差（RMSE）分别为 0.533、1.833、1.111、1.808、0.723。同时，在保证高精度的情况下，测试时间仅为 0.034s。充分证明了 DR_TELM 可以快速准确地分析煤。
