Construction and application of a high precision 3D simulation model for geomechanics of the complex coal seam

Zhao

Bai-sheng

2023

Sci Rep

Self Cite

The recognition of cutting state of coal-rock is the key technology to realize “unmanned” mining in coal face. In order to realized real-time perception and accurate judgment of coal-rock cutting state information, this paper combined the field test sampling, construction technology of complex coal seam, virtual prototype technology, bidirectional coupling technology, data processing theory, image fusion method, and deep learning theory to carry out multi domain deep fusion experimental research on multi-source heterogeneous data of coal and rock cutting state. The typical complex coal seam containing gangue, inclusion, and minor fault in Yangcun mine of Yanzhou mining area was taken as the engineering object. The high-precision three-dimensional simulation model of the complex coal seam that can update and replace particles was constructed. Based on the simulation results of Discrete Element Method-Multi Flexible Body Dynamics (DEM-MFBD), the one-dimensional original vibration acceleration signals of the key components of the shearer cutting part were determined, including spiral drum, rocker arm shell, and square head. After transforming one-dimensional original signal data into two-dimensional time–frequency images by Short-time Fourier Transform, morphological wavelet image fusion technology was used to realize the effective fusion of characteristic information of spiral drum, rocker arm shell, and square head under different working conditions. Based on the deep learning theory, the DCGAN-RFCNN (Deep Convolutional Generative Adversarial Networks-Random Forest Convolutional Neural Networks) coal and rock cutting state recognition network model was constructed. Combining convolution neural network with random forest recognition classifier, RFCNN coal and rock cutting state recognition classification model was constructed, and the recognition network model was trained to obtain the model recognition results. Through the comparative experimental analysis of the RFCNN network model with different recognition network models and different synthetic sample numbers in the recognition network, the effectiveness of the recognition network model was verified. The results show that: When synthetic samples are not included in each working condition in the RFCNN model, the average recognition rate is 90.641%. With the increase of the number of synthetic samples, the recognition rate of coal and rock cutting state increases. When the number of synthetic samples added to each working condition reaches 5000, the recognition effect is the best, and the average recognition rate reaches 98.344%, which verifies the superiority of enriching the data set by using the improved DCGAN network. Also, the RFCNN outperformed the other variants: it obtained higher recognition accuracy by 25.085, 21.925 and 19.337%, respectively, over SVW, CNN, and AlexNet. Also, the experimental platform of shearer cutting coal and rock was built, where the coal and rock cutting state recognition network was trained and tested based on the migration learning theory. Through the statistical test results, the accuracy of coal and rock cutting state recognition is 98.64%, which realizes the accurate recognition of coal and rock cutting state.

Section: Extraction and Analysis Of Vibration Signals In The Coal And...mentioning

confidence: 99%

Analysis and construction of the coal and rock cutting state identification system in coal mine intelligent mining

Zhao

Bai-sheng

2023

Sci Rep

Self Cite

“…It can be found that in previous studies, the emission differences of individual construction activities have been studied, but the correlation between pollution emissions and the concentration of PM in the atmospheric environment has not been fully discussed. In addition, PM emissions were found to vary with the average weight (Zhao et al 2009), number (Raile 1996), and average speed (Cowherd et al 1974) of the vehicles used on-site.…”

Section: Factors Influencing Pm Emissionsmentioning

confidence: 99%

The characteristics of PM emissions from construction sites during the earthwork and foundation stages: an empirical study evidence

Yan

Feng

2023

Environ Sci Pollut Res

The bulk of the particulate matter (PM) emissions generated during construction projects are significantly released during the earthwork and foundation stages. To reduce and control these emissions, it is necessary to have reliable data on their characteristics. However, construction PM are poorly characterized because their composition depends on several factors (e.g., weather and reduction measures) and various on-site activities whose effects may interact. To address these challenges, a long-term quantitative empirical study using advanced statistical methods was performed on a real construction project during the whole earthwork and foundation stages. The upwind-downwind method was used to collect data on PM emissions throughout the earthwork and foundation construction process, and correlation analysis, paired samples t-test, and partial least squares regression (PLS) were used to analyze TSP, PM10, and PM2.5 emissions and their relationships with various influencing factors. The results showed that both earthwork and foundation constructions generate substantial PM emissions because there were differences with statistical significances in the PM levels measured upwind and downwind of the construction site. TSP and PM10 emissions correlated moderately with humidity and wind speed. However, temperature and atmospheric pressure did not correlate significantly with any of the measured emissions. The main activities responsible for PM emissions during the earthwork and foundation construction stages were hammer piling, waste stacking, and materials transportation. Water spraying was found to effectively reduce TSP and PM10 emissions, while the use of a fog cannon more effectively reduced PM2.5 emissions. Construction PM is an important source of atmospheric pollution in cities; the findings presented herein provide cornerstone and knowledge to guide efforts for reducing its impact.

“…Finally, the optimal value of the batch size of the RFCNN network model trained by the vibration time-frequency spectrum samples in the coal and rock cutting state is determined to be 25. In order to verify the superiority of the network model in the identification of the coal and rock cutting status, experimental research was carried out in the Liaoning Provincial Key Laboratory of Large-Scale Mining Equipment 25,26,27 . Based on the original mining equipment in the laboratory, the existing experimental platform for the coal and rock cutting by shearers was reconstructed, which is mainly composed of four parts: the coal and rock cutting system, signal data acquisition and processing system, control power system and real-time monitoring system, as shown in Fig.…”

Section: Sample Typementioning

confidence: 99%

Research on Key Technology of Coal and Rock Cutting State Identification

Zhao

Bai-sheng

2022

Preprint

Self Cite

This paper combined the field test sampling, construction technology of complex coal seam, virtual prototype technology, bidirectional coupling technology, data processing theory, image fusion method, and the deep learning theory. It focuses on the key technologies of the coal and rock cutting state identification, such as "the acquisition and processing of the coal and rock cutting state data information set, sample expansion, identification and classification". The EDEM and RecurDyn were used to build the high-precision three-dimensional simulation model of complex coal seam and the bidirectional coupling model between shearer cutting parts, and the simulation group was set based on the actual cutting state of the shearer. Based on the simulation results of Discrete Element Method-Multi Flexible Body Dynamics (DEM-MFBD), the one-dimensional original vibration acceleration signals of the key components of the shearer cutting part were determined, including spiral drum, rocker arm shell, and square head. After transforming one-dimensional original signal data into the time-frequency images by STFT, morphological wavelet image fusion technology was used to realize the effective fusion of characteristic information of spiral drum, rocker arm shell, and square head under different working conditions. The basic database for coal and rock cutting state recognition was also constructed to provided original samples with rich and precise characteristic information for the expansion of recognition system data. Based on the deep learning theory, a DCGAN-RFCNN recognition network model with strong ability to perceive the cutting state information of coal and rock and rapid recognition and classification was constructed. Also, the experimental platform of shearer cutting coal and rock was built, where the coal and rock cutting state recognition network was trained and tested based on the migration learning theory. Through the statistical test results, the accuracy of coal and rock cutting state recognition is 98.64%, which realizes the accurate recognition of coal and rock cutting state.