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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
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
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
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 …
Enhancing Landslide Susceptibility Modelling Through A Novel Non-Landslide Sampling Method And Ensemble Learning Technique, Chao Zhou, Yue Wang, Ying Cao, Ramesh P. Singh, Bayes Ahmed, Mahdi Motagh, Yang Wang, Ling Chen, Guangchao Tan, Shanshan Li
Enhancing Landslide Susceptibility Modelling Through A Novel Non-Landslide Sampling Method And Ensemble Learning Technique, Chao Zhou, Yue Wang, Ying Cao, Ramesh P. Singh, Bayes Ahmed, Mahdi Motagh, Yang Wang, Ling Chen, Guangchao Tan, Shanshan Li
Mathematics, Physics, and Computer Science Faculty Articles and Research
In recent years, several catastrophic landslide events have been observed throughout the globe, threatening to lives and infrastructures. To minimize the impact of landslides, the need of landslide susceptibility map is important. The study aims to extract high-quality non-landslide samples and improve the accuracy of landslide susceptibility modelling (LSM) outcomes by applying a coupled method of ensemble learning and Machine Learning (ML). The Zigui-Badong section of the Three Gorges Reservoir area (TGRA) in China was considered in the present study. Twelve influencing factors were selected as inputs for LSM, and the relationship between each causal factor and landslide spatial development …
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
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 …
Predicting Open-Pit Mine Production Using Machine Learning Techniques, Faustin Nartey Kumah, Alex Kwasi Saim, Millicent Nkrumah Oppong, Clement Kweku Arthur
Predicting Open-Pit Mine Production Using Machine Learning Techniques, Faustin Nartey Kumah, Alex Kwasi Saim, Millicent Nkrumah Oppong, Clement Kweku Arthur
Journal of Sustainable Mining
In mining, where production is affected by several factors, including equipment availability, it is necessary to develop reliable models to accurately predict mine production to improve operational efficiency. Hence, in this study, four (4) machine learning algorithms – namely: artificial neural network (ANN), random forest (RF), gradient boosting regression (GBR) and decision tree (DT)) – were implemented to predict mine production. Multiple Linear Regression (MLR) analysis was used as a baseline study for comparison purposes. In that regard, one hundred and twenty-six (126) datasets from an open-pit gold mine were used. The developed models were evaluated and compared using the …