# Online Coal Identification Based on One-Dimensional Convolution and Its Industrial Applications
## 1 Introduction  
Recently, thermal power generation plays an important role in electric energy supply. In order to reduce costs and improve boiler efficiency, coal blending technology is widely used in thermal power plants (TPP), using different types of coal in specific proportions for combustion [1, 2]. Coal blending technology refers to the process of burning coal in a boiler for power generation by blending several types of coal with different characteristics in a predetermined proportion. Considering that original coal structure has a significant effect on burnout, and abnormal changes in coal species can affect the low load stability and combustion capacity of boilers, improper blending or changes in coal types may result in a decrease in efficiency and, in severe cases, safety accidents [3, 4, 5]. Hence, online identification of coal is important to improve the efficiency of power plants. In recent years, online coal identification has received extensive attention from both industry and academia. Chemical analysis-based, image-based, and machine-learning-based methods are widely used for coal identification. The chemical analysis-based method is reliable and relatively accurate. However, this method requires stringent analysis techniques for elemental content, and it is easily affected by foreign chemical substances. The image-based method primarily focuses on coal's shape, texture, and color, and it has gained considerable attention in recent years. For example, in 2019, [PERSON] et al. [6], proposed a coal quality recognition method based on electric coal images, which detects coal quality indexes more accurately using color and texture features. [PERSON] [7] proposed an image analysis method for analyzing power coal quality, which accelerated the model recognition speed. Moreover, [PERSON] et al. [8] proposed a method in 2021, which combines image processing and convolutional neural network (CNN) to performbinary classification of coal and gangue. However, such methods are primarily used for the identification of pulverized coal before combustion and are easily influenced by light and imaging, which makes the identification of coal types challenging. The online coal identification is a classification recognition problem. Machine learning methods, such as support vector machine (SVM), Naive Bayes, decision tree, and CNN, have been adopted to address this issue [9; 10]. [PERSON] and [PERSON] [11] compared plain Bayesian and logistic regression methods, reflecting the advantages of plain Bayesian in small data volumes. However, as the era of big data emerged, the classification effectiveness of the Bayesian method began to diminish, leading to the development of improved methods, such as the weighted Bayesian classifier method proposed by [PERSON] et al. [12]. Later, other improved methods such as semi-parsimonious Bayes classifier and Gaussian parsimonious Bayes classifier [13] were developed to achieve better classification results. The decision tree approach was first proposed by [PERSON] [14], and later, in 1993, the shortcomings of the previous method were addressed in the literature [15], which addressed the shortcomings of previous methods, such as the ability to handle missing values. Over time, the accuracy of decision trees has gradually improved [16; 17]. [PERSON] et al. [18]. were the first to apply CNN for image classification and classify 1.2 million images into 1000 categories with a low error rate. [PERSON] et al. [19]. improved a deeper level CNN visual geometry group (VGG) in 2015, which has been widely used in the field of target detection. [PERSON] et al. [20]. introduced residual computation in neural networks in 2015, which have addressed, to a certain extent, the gradient disappearance phenomenon that can easily occur in deep neural networks. Google subsequently proposed InceptionNet [21] and MobileNet [22], which further improved the classification accuracy of CNNs. With the improvement of computational precision, the time cost and memory usage have also increased. [PERSON] et al.