Mining heavy metal ore deposits may lead to an increase in heavy metal element content in surrounding soils, which could pose irreversible harm to the ecological environment and human health. Therefore, analyzing and classifying soils from different mining areas is of great significance and can provide reference for soil management and environmental pollution control. Laser-induced breakdown spectroscopy (LIBS) has gradually become a research hotspot in soil detection due to its fast and pre-treatment-free characteristics. However, traditional LIBS technology has problems such as low sensitivity, high noise, and poor repeatability, which affect its accuracy. Therefore, this paper proposes a soil classification method based on Principal Component Analysis (PCA) of LIBS technology coupled with K-Nearest Neighbor algorithm (KNN). This method first conducts data standardization and PCA pre-processing to eliminate redundant information and improve signal-to-noise ratio. Then, autonomous sampling technology is used to design the KNN machine learning algorithm structure to generate continuous analytical networks for training and testing sets. Finally, the results show that the soil classification accuracy of the PCA-KNN machine learning model can reach 97.531%, proving that the combination of LIBS technology and PCA-KNN can achieve rapid and accurate classification of soils from different mining areas. Therefore, this method has the significance of providing new ideas and methods for soil classification in different regions.
Currently, China is still a major consumer of coal resources. Coal can be used in various fields such as industry and civil use, and can be used for power generation, heating, and building materials. There are many types of coal, each with its unique composition and properties. It has specific requirements for its use in various fields, which make the use of coal more reasonable and important for the sustainable development of the environment and resources. Therefore, the classification research of coal is of great significance. Due to the same component influence among various coals, there are certain challenges for coal classification. Therefore, a laser induced breakdown spectroscopy (LIBS) based on principal component analysis (PCA) combined with convolutional neural network (CNN) method was proposed to classify and recognize coal samples from six different regions. Through laser ablation of coal samples and collection of corresponding data, the data are dimensionalized and standardized, and then the spectral data are classified and trained through PCA-CNN optimization model. The final results indicate that the coal classification accuracy of the PCA-CNN deep learning network model can reach 98.15%. From this result class, it can be seen that laser induced breakdown spectroscopy technology combined with PCA-CNN can achieve rapid and accurate classification of coal samples from different regions, and provide a new coal quality detection data analysis and processing scheme.
To measure flame temperatures in various complex and extreme environments, we propose a two-dimensional flame temperature measurement method based on infrared radiation. In the laboratory environment, the acetylene-oxygen premixed flame was generated by a simulated flame generation device, the two-dimensional infrared image of the flame was obtained by using a cooled infrared thermal imager, and the emissivity of the acetylene-oxygen flame was obtained by combining a colorimetric pyrometer, the atmospheric transmittance of the site is measured by using an atmospheric correction factor, a flame radiation decay transmission model was established, and the temperature calibration curve of the infrared thermal imager was combined to finally obtain two-dimensional flame temperature data. Through the temperature measurement uncertainty analysis, the temperature measurement error of the system is within 5%. The experimental results validate the feasibility of the method.
High-temperature field detection has important application value in the industrial and military fields. The three-dimensional structure reconstruction of flame is of great significance to carry out the finite element division of the temperature field. However, because the flame is a self-luminous fluid, the flame pictures obtained directly by the camera lack characteristic information, and the use of many three-dimensional reconstruction technologies directly performs topographic reconstruction with low accuracy. In this study, MATLAB software was used to conduct simulation experiments to study the accuracy of the 3D structure of flame reconstructed by chemiluminescence computed tomography. A single Gaussian peak was used to simulate a symmetrical flame, and a 2D projection of the flame was obtained according to a simplified chemiluminescence tomography model. The additive algebraic reconstruction method was used in this study. (Additive Algebra Reconstruction Technique) reconstructs the three-dimensional structure of the flame. The resolutions of 256px, 512px, 1024px triple-projection images are set respectively, as well as the different number of channels. The mean error is used for quantification to evaluate the accuracy of the reconstruction results. The experimental results show that the resolution of 512px and the number of channels of 13 converge after eight iterations, and the reconstruction accuracy is better than that of the projection resolution of 256px. At the same time, the reconstruction speed of the ART algorithm at 512px is much faster than that at 1024px. At the same resolution, the reconstruction accuracy increases with more channels.
To study the temperature distribution of the methane combustion process, the methane and air premixing model was simulated using fluent fluid simulation software, and the distribution clouds of temperature and H2O molecules were given. And the combustion region at 5 cm high was selected to study the relationship between the temperature and the mass fraction of H2O. Meanwhile, according to the principle of temperature measurement by TDLAS technology, the gas temperature was simulated in SIMULINK, the absorption line of H2O molecules was selected as the temperature measurement spectral line, and the spectral absorption model was established using the HITRAN database, and the flow chart of the simulation platform construction was given, including the light source module, gas chamber module, and data detection module. Under certain conditions, the temperature simulation data were obtained by giving 15 groups of H2O mass fractions. The results showed that the temperature measured by the TDLAS system was consistent with the temperature simulated by Fluent software, and the error range was within 5%.
The development of heavy metal mining area will pollute the surrounding soil and do great harm to the ecological environment and human health. Soil classification in different mining areas is of great significance to soil management and environmental pollution control. Soil is easily affected by matrix effect due to its complex physical properties and composition. Thus, accurately classifying soils is a challenge. Laser-induced breakdown spectroscopy has developed rapidly in the past two decades. It has been widely used in the detection of various physical samples due to its characteristics of fast analysis speed and no need for sample pretreatment. However, traditional LIBS technology has disadvantages such as low sensitivity, obvious noise and poor repeatability, which affect the accuracy of quantitative analysis. In this paper, a soil classification method based on principal component analysis (PCA) based laser-induced breakdown spectroscopy (LIBS) and random forest (RF) algorithm was proposed, and the standard soil samples from six different mining areas were accurately identified and classified. The final prediction results based on this combination show that the accuracy of soil classification by PCA-RF machine learning model can reach 97.86%. From the aspect of classification accuracy, it can be found that laser-induced breakdown spectroscopy combined with PCA-RF can achieve rapid and accurate classification of soil in different mining areas, which also provides a new method for soil classification in heavy metal mining areas.
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