As an extremely important energy source, improving the efficiency and accuracy of coal classification is important for industrial production and pollution reduction. Laser-induced breakdown spectroscopy (LIBS) is a new technology for coal classification which has the ability to rapidly analyze coal compared with traditional coal analysis methods. In the practical application of LIBS, a large amount of labeling data is usually required, but it is quite difficult to obtain labeling data in industrial sites. In this paper, to address the problem of insufficient labeled data, a semi-supervised classification model (SGAN) based on adversarial neural network is proposed, which can utilize unlabeled data to improve the classification accuracy. The effects of labeled and unlabeled samples on the classification accuracy of the SGAN model are investigated, and the results show that the number of labeled and unlabeled samples are positively correlated, and the highest average classification accuracy that the model can achieve is 98.5%. In addition, the classification accuracies of SGAN and other models (e.g., CNN, RF) are also compared, and the results show that, with the same number of labeled samples in the three models, SGAN performs better after the number of unlabeled samples reaches a certain level, with an improvement of 0.7% and 2.5% compared to the CNN and RF models, respectively. This study provides new ideas for the application of semi-supervised learning in LIBS.

In recent years, with the increase in environmental awareness, people have become more and more concerned about the effectiveness with which coal burns. Laser-induced breakdown spectroscopy (LIBS) has become an important way of coal elemental analysis because of its uncomplicated sample handling, remote sensing capability, and superior sensitivity in identifying a wide range of elements, including both major and minor constituents, down to trace levels. However, the complexity of its mechanism of action, the experimental environmental factors, and the presence of matrix effects in its measurement spectrum have affected the measurement accuracy. In this paper, on the basis of introducing the experimental process and principle of LIBS, we summarize and analyze the influence of each factor on the LIBS detection medium, summarize the mainstream model analysis algorithms, and analyze the advantages and disadvantages of each model. While summarizing the LIBS in media detection in recent years, it aims to provide strong support and guidance for subsequent more in-depth exploration and research.

Based on the acid-sensitive characteristics of SBA-15 during synthesis, this study varied the acid types, pH values, and mixed acid ratios during SBA-15 preparation to enhance the performance of CaO/Cr-SBA-15 dual-functional materials (DFMs) in integrated CO₂ capture and utilization for oxidative dehydrogenation of ethane (ICCU-ODHE). It was found that the SBA-15 support synthesized in an H₂SO₄ environment exhibited a high specific surface area and abundant surface silanol groups, which facilitated the dispersion of Cr and increased the proportion of Cr⁶⁺ active sites, thereby achieving the highest ethane conversion. In contrast, the moderate surface acidity of the HCl-prepared support facilitated the selective dehydrogenation of ethane over Cr active sites, effectively inhibiting side reactions and maximizing ethylene selectivity. Further investigations into the effects of pH and mixed acids revealed that pH 1 is optimal for SBA-15 preparation. At this value, the support reached its maximum mesoporous ordering and specific surface area, allowing for optimal Cr dispersion. Consequently, the ethane conversion, ethylene selectivity, and DFM yield all reached their peak values. Any deviation from this pH led to degradation of the support structure and reduced Cr dispersion, resulting in a significant decline in catalytic performance. Among the tested materials, the CaO/Cr-SBA-15-Cl-S DFM synthesized with an HCl-H₂SO₄ mixed acid demonstrated the superior reactivity, achieving an ethylene yield of 33.95%. Long-term cycling tests indicated that the material possesses good stability, with its performance attenuation primarily attributed to coking and adsorbent sintering.