# Online Coal Identification Based on One-Dimensional Convolution and Its Industrial Applications
## 1 Introduction
[23] addressed the issue of excessive memory consumption during training by proposing a partial channel connection based on channel attention for differentiable neural architecture search (ADARTS) in 2023, which enhanced the efficiency of the search process and optimized memory usage. In the same year, [PERSON] et al. [24] proposed a multi-objective evolutionary algorithm with a probability stack (MOEA-PS), which effectively reduces the time cost. Additionally, [PERSON] et al. [25] introduced a novel and efficient channel attention mechanism termed EPC-DARTS, which allocates weights based on channel importance and selectively employs channels with higher weights. This approach has contributed to mitigating the aforementioned issues to a certain extent. CNN, as a deep learning network, provides significant advantages compared with traditional neural networks. Given its multiple convolutional layers, it can automatically extract data features, avoiding the time-consuming and inefficient manual feature extraction process. In traditional neural networks, overfitting can easily occur because of the excessive number of parameters, which negatively affects network performance. By contrast, CNNs can mitigate overfitting to a certain extent by leveraging the weight-sharing feature. In addition, the robustness of CNNs is effective in combating noise interference. Data-driven classification recognition has been primarily applied for image processing and text-to-speech recognition; however, it has not been applied to coal species recognition. In real power plant scenarios, massive amounts of high-dimensional data are often generated, accompanied by noise interference, which makes it challenging to complete the coal identification task using traditional or manual methods. [PERSON] et al. [26] proposed a deep learning framework for coal and gangue detection based on image recognition. [PERSON] et al. [27] presented an enhanced YOLOv4 algorithm for coal and gangue recognition using deep learning, showing better performance than YOLOv3. [PERSON] et al. [28] proposed a cascade network with a detector and a discriminator, enhancing coal and gangue detection under complex conditions by designing a multi-channel feature fusion layer and optimizing the CNN in the discriminator. The aforementioned existing methodologies are designed to address coal quality recognition by constructing classification models based on coal powder images. However, to the best of our knowledge, there is currently a dearth of research focused on utilizing combustion process data, specifically derived from sensor measurements, to perform online coal species identification. To deal with the aforementioned problems, this paper proposes a data-driven algorithm for coal identification based on one-dimensional convolution, leveraging the advantages of the CNN algorithm in the field of classification and recognition. The proposed method collects process data from coal-fired boilers and feeds the pre-process data into a CNN model to construct a coal classification model. During online application, the process data are preprocessed first and then input into the CNN model, and the coal type is identified on the basis of model output. Industrial data-based verification demonstrates that the proposed method achieves high recognition accuracy, which can be easily implemented in the field. The remainder of this paper is organized as follows. Section 2 provides a description of the target boiler used in this work. Preliminaries regarding Maximum information coefficient (MIC) and CNN are provided in Section 3. Section 4 presents the proposed modeling method. Section 5 discusses experiments and results obtained using real world data. Finally, conclusions are provided in Section 6.