Open Access. Powered by Scholars. Published by Universities.®

Engineering Commons

Open Access. Powered by Scholars. Published by Universities.®

Sustainability

Coal Geology & Exploration

2024

Machine learning

Articles 1 - 6 of 6

Full-Text Articles in Engineering

A Method For Predicting The Tar Yield Of Tar-Rich Coals Based On The Bp Neural Network Using Multiple Indicators Of Coal Petrography And Coal Quality, Qiao Junwei, Wang Changjian, Zhao Hongchao, Shi Qingmin, Zhang Yu, Fan Qi, Wang Duo, Yuan Dandan Jul 2024

A Method For Predicting The Tar Yield Of Tar-Rich Coals Based On The Bp Neural Network Using Multiple Indicators Of Coal Petrography And Coal Quality, Qiao Junwei, Wang Changjian, Zhao Hongchao, Shi Qingmin, Zhang Yu, Fan Qi, Wang Duo, Yuan Dandan

Coal Geology & Exploration

Objective Tar yield, the most important coal quality parameter for coal utilization through low-temperature pyrolysis, determines the clean utilization of tar-rich coals. However, various constraints result in limited test data on tar yield in the geological exploration stage of coals, substantially restricting the fine-scale assessment and efficient utilization of tar-rich coals. Methods To achieve more scientific and accurate fine-scale tar-rich coal assessments, this study examined 1073 sets of lithotype and coal quality data obtained previously from a Jurassic coalfield in northern Shaanxi. From these data, 141 sets with 20 lithotype and coal quality parameters regarding macerals, proximate analysis, ultimate analysis, …


Application Of Microseismic Monitoring System For Coal Mines To The Prevention And Control Of Water Disasters On Working Face Roofs, Fan Xin, Cheng Jianyuan, Li Sheng, Duan Jianhua, Fan Tao, Li Bofan, Wang Yanbo Jun 2024

Application Of Microseismic Monitoring System For Coal Mines To The Prevention And Control Of Water Disasters On Working Face Roofs, Fan Xin, Cheng Jianyuan, Li Sheng, Duan Jianhua, Fan Tao, Li Bofan, Wang Yanbo

Coal Geology & Exploration

[Objective] Increasing coal mining depth in coal mines has caused increasingly prominent risks of water disasters on working face roofs, which restrict the green, safe, and efficient coal mining in China’s coal mines. Therefore, there is an urgent need for new technologies for the monitoring and early warning of these water disasters. [Methods] This study explored the application of the joint well-ground microseismic monitoring system to the prevention and control of water disasters on working face roofs, achieving transparent, intelligent monitoring and early warning of water disaster risks. In this system, microseismic sensor arrays are arranged on the surface above …


Intelligent Identification Method Of Drilling Fluid Rheological Parameters Based On Machine Learning, Liu Changye, Yang Xianyu, Cai Jihua, Wang Ren, Wang Jianlong, Dai Fanfei, Guo Wanyang, Jiang Guoshe, Feng Yang May 2024

Intelligent Identification Method Of Drilling Fluid Rheological Parameters Based On Machine Learning, Liu Changye, Yang Xianyu, Cai Jihua, Wang Ren, Wang Jianlong, Dai Fanfei, Guo Wanyang, Jiang Guoshe, Feng Yang

Coal Geology & Exploration

The rheology of drilling fluid, which characterizes its flow and deformation, is vital for transporting and suspending rock cuttings as well as for enhancing the drilling rate. Precise control of drilling fluid rheological parameters is essential to ensure borehole cleanliness and efficient drilling. This paper proposes an intelligent identification method for drilling fluid rheological parameters based on Convolutional Neural Networks (CNNs). The method employs magnetic stirring to generate stable images of drilling fluid flow, uses various data augmentation methods to increase the number of images and create a database, thereby enhancing the model’s robustness and generalization capabilities. The AlexNet CNN …


Some Reflections On The Application Of Machine Learning To Research Into The Theoretical System Of Mine Water Prevention And Control, Yao Hui, Yin Huichao, Liang Manyu, Yin Shangxian, Hou Enke, Lian Huiqing, Xia Xiangxue, Zhang Jinfu, Wu Chuanshi May 2024

Some Reflections On The Application Of Machine Learning To Research Into The Theoretical System Of Mine Water Prevention And Control, Yao Hui, Yin Huichao, Liang Manyu, Yin Shangxian, Hou Enke, Lian Huiqing, Xia Xiangxue, Zhang Jinfu, Wu Chuanshi

Coal Geology & Exploration

The theoretical system of mine water prevention and control encompasses three fundamental aspects: disaster-causing mechanisms, risk evaluation, and disaster prediction. This theoretical system, having undergone rapid development over the past 20 years, aims to gain insights into the behavior characteristics of mine water and predict its evolutionary trend, thus serving the prevention and control of water disasters in mining areas. Applying machine learning, a powerful tool for data analysis and mining in the era of big data, to research into the theoretical system has garnered considerable attention. This study focuses on the specific applications of machine learning to the three …


An Intelligent Water Source Discrimination Method For Water Inrushes From Coal Seam Roofs In The Inner Mongolia-Shaanxi Border Region, Wang Hao, Sun Junqing, Zeng Yifan, Shang Hongbo, Wang Tiantian, Qiao Wei Apr 2024

An Intelligent Water Source Discrimination Method For Water Inrushes From Coal Seam Roofs In The Inner Mongolia-Shaanxi Border Region, Wang Hao, Sun Junqing, Zeng Yifan, Shang Hongbo, Wang Tiantian, Qiao Wei

Coal Geology & Exploration

Water hazard on the coal seam proof induced by high-intensity coal mining are increasingly prominent in the Inner Mongolia-Shaanxi border region. The effective, accurate water-source discrimination of the water inrushes is the key to water hazard prevention. This study investigated three typical mines in the Inner Mongolia-Shaanxi border region. To this end, principal component analysis (PCA) was employed to extract principal components from 80 groups of groundwater samples. Then, with inorganic indicators K++Na+, Ca2+, Mg2+, Cl, SO4 2−, HCO3 and TDS and organic indicators UV254 …


Early Warning And Prediction Of Kicks And Lost Circulation Accident During Rescue Drilling Of Mine, Chen Weiming, Wang Jiawen, Fan Dong, Hao Shijun, Zhao Jiangpeng, Qiu Yu Mar 2024

Early Warning And Prediction Of Kicks And Lost Circulation Accident During Rescue Drilling Of Mine, Chen Weiming, Wang Jiawen, Fan Dong, Hao Shijun, Zhao Jiangpeng, Qiu Yu

Coal Geology & Exploration

In order to solve the problems such as the difficulty in early warning and prediction of kicks and lost circulation accidents during emergency rescue drilling of mine, a machine learning-based early for warning and prediction model of drilling process was established. Firstly, the accident characterization parameters of the drilling parameters in the early stage of kicks and lost circulation accidents were analyzed. Secondly, the accident characterization parameters were cleaned and processed. On this basis, XGBoost and early warning model was used to carry out the early diagnosis and identification of kicks and lost circulation accidents. Then, the PSO-LSTM accident development …