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Lv Y, Wang S, Yang E, Ge S. A near-infrared spectroscopy dataset of coal and coal-measure rock under diverse conditions. Sci Data 2024; 11:628. [PMID: 38877010 PMCID: PMC11178847 DOI: 10.1038/s41597-024-03422-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 05/23/2024] [Indexed: 06/16/2024] Open
Abstract
The identification technology for coal and coal-measure rock is required across multiple stages of coal exploration, mining, separation, and tailings management. However, the construction of identification models necessitates substantial data support. To this end, we have established a near-infrared spectral dataset for coal and coal-measure rock, which includes the reflectance spectra of 24 different types of coal and coal-measure rock. For each type of sample, 11 sub-samples of different granularities were created, and reflectance spectra were collected from sub-samples at five different detection azimuths, 18 different detection zeniths, and under eight different light source zenith conditions. The quality and usability of the dataset were verified using quantitative regression and classification machine learning algorithms. Primarily, this dataset is used to train artificial intelligence-based models for identifying coal and coal-measure rock. Still, it can also be utilized for regression studies using the industrial analysis results contained within the dataset.
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Affiliation(s)
- Yuanbo Lv
- School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou, 221116, China
- Jiangsu Province and Education Ministry Co-sponsored Collaborative Innovation Center of Intelligent Mining Equipment, China University of Mining and Technology, Xuzhou, 221116, China
- State Key Laboratory of Intelligent Mining Equipment Technology, China University of Mining and Technology, Xuzhou, 221116, China
| | - Shibo Wang
- School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou, 221116, China.
- Jiangsu Province and Education Ministry Co-sponsored Collaborative Innovation Center of Intelligent Mining Equipment, China University of Mining and Technology, Xuzhou, 221116, China.
- State Key Laboratory of Intelligent Mining Equipment Technology, China University of Mining and Technology, Xuzhou, 221116, China.
| | - En Yang
- School of Intelligent Manufacturing, Jiangsu Vocational Institute of Architectural Technology, Xuzhou, 221116, China
| | - Shirong Ge
- China University of Mining and Technology-Beijing, Beijing, 100083, China
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Li B, Xiao D, Xie H, Huang J, Yan Z. Coal Classification Based on Reflection Spectroscopy and the IAT-TELM Algorithm. ACS OMEGA 2023; 8:35232-35241. [PMID: 37780011 PMCID: PMC10536090 DOI: 10.1021/acsomega.3c04999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 08/25/2023] [Indexed: 10/03/2023]
Abstract
As a principal energy globally, coal's quality and variety critically influence the effectiveness of industrial processes. Different coal types cater to specific industrial requirements due to their unique attributes. Traditional methods for coal classification, typically relying on manual examination and chemical assays, lack efficiency and fail to offer consistent accuracy. Addressing these challenges, this work introduces an algorithm based on the reflectance spectrum of coal and machine learning. This method approach facilitates the rapid and accurate classification of coal types through the analysis of coal spectral data. First, the reflection spectra of three types of coal, namely, bituminous coal, anthracite, and lignite, were collected and preprocessed. Second, a model utilizing two hidden layer extreme learning machine (TELM) and affine transformation function is introduced, which is called affine transformation function TELM (AT-TELM). AT-TELM introduces an affine transformation function on the basis of TELM, so that the hidden layer output satisfies the maximum entropy principle and improves the recognition performance of the model. Third, we improve AT-TELM by optimizing the weight matrix and bias of AT-TELM to address the issue of highly skewed distribution caused by randomly assigned weights and biases. The method is named the improved affine transformation function (IAT-TELM). The experimental findings demonstrate that IAT-TELM achieves a remarkable coal classification accuracy of 97.8%, offering a cost-effective, rapid, and precise method for coal classification.
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Affiliation(s)
- Boyan Li
- School
of Information Science and Engineering, Northeastern University, Shenyang 110819, China
- Liaoning
Key Laboratory of Intelligent Diagnosis and Safety for Metallurgical
Industry, Northeastern University, Shenyang 110819, China
| | - Dong Xiao
- School
of Information Science and Engineering, Northeastern University, Shenyang 110819, China
- Liaoning
Key Laboratory of Intelligent Diagnosis and Safety for Metallurgical
Industry, Northeastern University, Shenyang 110819, China
| | - Hongfei Xie
- School
of Information Science and Engineering, Northeastern University, Shenyang 110819, China
- Liaoning
Key Laboratory of Intelligent Diagnosis and Safety for Metallurgical
Industry, Northeastern University, Shenyang 110819, China
| | - Jie Huang
- School
of Information Science and Engineering, Northeastern University, Shenyang 110819, China
- Liaoning
Key Laboratory of Intelligent Diagnosis and Safety for Metallurgical
Industry, Northeastern University, Shenyang 110819, China
| | - Zelin Yan
- School
of Information Science and Engineering, Northeastern University, Shenyang 110819, China
- Liaoning
Key Laboratory of Intelligent Diagnosis and Safety for Metallurgical
Industry, Northeastern University, Shenyang 110819, China
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Zhang W, Kasun LC, Wang QJ, Zheng Y, Lin Z. A Review of Machine Learning for Near-Infrared Spectroscopy. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22249764. [PMID: 36560133 PMCID: PMC9784128 DOI: 10.3390/s22249764] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 12/04/2022] [Accepted: 12/05/2022] [Indexed: 06/01/2023]
Abstract
The analysis of infrared spectroscopy of substances is a non-invasive measurement technique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes, instruments, signal preprocessing methods, etc. Firstly, four different measurement modes available in NIR are reviewed, different types of NIR instruments are compared, and a summary of NIR data analysis methods is provided. Secondly, the public NIR spectroscopy datasets are briefly discussed, with links provided. Thirdly, the widely used data preprocessing and feature selection algorithms that have been reported for NIR spectroscopy are presented. Then, the majority of the traditional machine learning methods and deep network architectures that are commonly employed are covered. Finally, we conclude that developing the integration of a variety of machine learning algorithms in an efficient and lightweight manner is a significant future research direction.
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Affiliation(s)
- Wenwen Zhang
- School of Electrical and Electronic Engnineering, Nanyang Technological University, Singapore 639798, Singapore
| | | | - Qi Jie Wang
- School of Electrical and Electronic Engnineering, Nanyang Technological University, Singapore 639798, Singapore
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
| | - Yuanjin Zheng
- School of Electrical and Electronic Engnineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Zhiping Lin
- School of Electrical and Electronic Engnineering, Nanyang Technological University, Singapore 639798, Singapore
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Xiao D, Le TTG, Doan TT, Le BT. Coal identification based on a deep network and reflectance spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 270:120859. [PMID: 35033804 DOI: 10.1016/j.saa.2022.120859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 12/16/2021] [Accepted: 01/02/2022] [Indexed: 06/14/2023]
Abstract
The rapid identification of coal types in the field is an important task. This research combines spectroscopy with deep learning algorithms and proposes a method for quickly identifying coal types in the field. First, we collect field spectral data of various coals and preprocess the spectra. Then, a coal identification model that uses a convolutional neural network in combination with an extreme learning machine is proposed. The two-dimensional spectral features of coal are extracted through the convolutional neural network, and the extreme learning machine is used as a classifier to identify the features. To further improve the identification performance of the model, we use the whale optimization algorithm to optimize the parameters of the model. The experimental results show that the proposed method can quickly and accurately identify types of coal. It provides a low-cost, convenient, and effective method for the rapid identification of coal in the field.
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Affiliation(s)
- Dong Xiao
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Key Laboratory of Intelligent Diagnosis and Safety for Metallurgical Industry, Liaoning Province, Northeastern University, Shenyang 110819, China
| | - Thi Tra Giang Le
- Training Department, Institute of Science and Technology, Hoang Sam 100000, Ha Noi, Viet Nam
| | | | - Ba Tuan Le
- Control, Automation in Production and Improvement of Technology Institute (CAPITI), Hanoi, 100000, Viet Nam.
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Xing Z, Zhao S, Guo W, Guo X, Wang Y, Bai Y, Zhu S, He H. Identifying Balls Feature in a Large-Scale Laser Point Cloud of a Coal Mining Environment by a Multiscale Dynamic Graph Convolution Neural Network. ACS OMEGA 2022; 7:4892-4907. [PMID: 35187309 PMCID: PMC8851460 DOI: 10.1021/acsomega.1c05473] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 01/21/2022] [Indexed: 06/14/2023]
Abstract
In the process of coal mining, a certain amount of gas will be produced. Environmental perception is very important to realize intelligent and unmanned coal mine production and operation and to reduce the accident rate of gas explosion and other disasters. The identification of geometric features of the coal mine working face is the main part of the environmental perception of the working face. In this study, we identify geometric features in a large-scale coal mine working face point cloud (we take the ball as an example) so as to provide a method for the environmental perception of the coal mine working face. On the basis of previous research, we upgrade the dynamic graph convolution neural network (DGCNN) for directly processing point clouds from two aspects: extracting local features and global features of point clouds. First, a multiscale dynamic graph convolution neural network (MS-DGCNN) is proposed, and the combination of max-pooling and average-pooling is used as the symmetry function. Second, we use MS-DGCNN to learn the features of a variety of geometric point clouds in the point cloud data set we make and then look for the ball in the large-scale point cloud of the coal mining working face. Finally, we compare the performance of MS-DGCNN with that of other deep neural networks directly processing point clouds. This study enables MS-DGCNN to obtain more powerful feature expression ability and enhance the generalization of the model. In addition, this study provides a solid foundation for the geometric feature identification of MS-DGCNN in the environmental perception of the coal mine working face and creates a precedent for the application of MS-DGCNN in the field of energy. At the same time, this study makes a beneficial exploration for the development of a transparent coal mine working face.
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Affiliation(s)
- Zhizhong Xing
- College
of Mechanical Engineering, Xi’an
University of Science and Technology, Xi’an 710054, China
| | - Shuanfeng Zhao
- College
of Mechanical Engineering, Xi’an
University of Science and Technology, Xi’an 710054, China
| | - Wei Guo
- College
of Mechanical Engineering, Xi’an
University of Science and Technology, Xi’an 710054, China
| | - Xiaojun Guo
- School
of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou 510641, China
| | - Yuan Wang
- College
of Mechanical Engineering, Xi’an
University of Science and Technology, Xi’an 710054, China
| | - Yunrui Bai
- College
of Mechanical Engineering, Xi’an
University of Science and Technology, Xi’an 710054, China
| | - Shibo Zhu
- College
of Mechanical Engineering, Xi’an
University of Science and Technology, Xi’an 710054, China
| | - Haitao He
- College
of Mechanical Engineering, Xi’an
University of Science and Technology, Xi’an 710054, China
- Shendong
Coal Group Co., Ltd. of National Energy Group, Yulin 719315, China
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