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WhatsApp: +86 18037808511WEBNov 16, 2023 · BEIJING (AP) — A fire erupted in a coal mining company building in a northern Chinese city on Thursday, killing 26 people and injuring at least 38, Chinese state media reported. Rescue personnel evacuated dozens of people from the building, according to state media. The building belongs to Yongju Coal Company and is in Lvliang city in ...
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WhatsApp: +86 18037808511WEBThe lignite selected in this experiment is a type of coal that is more prone to spontaneous combustion than longflame coal and poor coal, and the CO concentration, C 2 H 6 /CH 4, and second fire coefficient R 2 can be used as the main indiors for predicting CSC, while the other gases, olefinalkane ratio and fire coefficient can be used as ...
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WhatsApp: +86 18037808511WEBMar 1, 2023 · In underground coal mines, spontaneous combustion is a common occurrence, and Graham's ratio is frequently employed as an index to determine the nature of the fire in both its early and developing stages (Ray et al., 2004, Şensöğüt, 2011).The carbon monoxide/oxygen deficiency ratio, also known as the Graham index and carbon .
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WhatsApp: +86 18037808511WEBOct 1, 2021 · Machine Learning Approach to Implement Mine Fire Predicting for Underground Coal Mines. October 2021. DOI: / Conference: 2021 2nd Global Conference for Advancement in ...
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WhatsApp: +86 18037808511WEBNov 30, 2020 · Recent studies have suggested that due to climate change, the number of wildfires across the globe have been increasing and continue to grow even more. The recent massive wildfires, which hit Australia during the 2019–2020 summer season, raised questions to what extent the risk of wildfires can be linked to various climate, .
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WhatsApp: +86 18037808511WEBFeb 14, 2022 · For coal fire prediction, CO, O2, N2, and temperature were used as input variables whilst fire intensity was considered as the output simulation of the model is carried out using the ...
WhatsApp: +86 18037808511WEBMar 1, 2023 · For example, Dogra et al. (2021) proposed a novel machine learning and Graham's ratio data integration approach to predict the likelihood of a mine fire in accordance with the environmental factors that cause coal to heat up by itself. This has been set up in order to account for the factors that cause coal to spontaneously ch fire.
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WhatsApp: +86 18037808511WEBDec 1, 2018 · Coal fire refers to burning or smouldering of coal seam, coal storage pile or coal waste pile. Even though coal fires are observed in many countries worldwide, the occurrence of major coal fires are primarily reported from India, China, USA, Australia, Brazil, Venezuela, Indonesia, South Africa and Eastern Europe (Bell et al., 2001; .
WhatsApp: +86 18037808511WEBMachine Learning Approach to Implement Mine Fire Predicting for Underground Coal Mines Abstract: Mine fire due to auto oxidation of coal leading to spontaneous combustion of coal is the leading unsolved problem worldwide. Though the exact reasons of selfheating of coal are many and unclear, the accumulated heat generated in exothermic .
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WhatsApp: +86 18037808511WEBMar 1, 2019 · Coal spontaneous combustion is one of the major disasters in underground coal exploitation, which seriously threatens the safety production of coal mines [1], [2], [3]. The resulting fire will not only cause the waste of valuable resources, equipment damage, but also destroy the ecological environment, threatening human health, and even lead to ...
WhatsApp: +86 18037808511WEBSep 30, 2022 · Coal fire is a largescale coal seam combustion disaster [1], which mostly occurs in underground coal seams or outcrops [2]. Such disasters are widely distributed all over the world, including in the United States [3], China [4], Australia [5], India [6], Indonesia [7], and other countries. ... Fire safety assessment models based on machine ...
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WhatsApp: +86 18037808511WEBAug 4, 2018 · Hence, we propose an online fire prediction system based on machine learning approach in which we used T, CO, CO 2 and O 2 as the feature matrix of input parameters for early detection of fire in underground coal mines. The machine learning approach based fire prediction in underground coal mines is presented in Algorithm 1 .
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WhatsApp: +86 18037808511WEBFeb 29, 2020 · An algorithm is proposed to be developed that aims to enable mining personnel to assess and evaluate the risk of fire in their workplace and take informed decisions based on the predictions based on Machine learning outputs. There are around 493 coal mines in India (300+ underground and around190 opencast mines) engaged in .
WhatsApp: +86 18037808511WEBDec 15, 2022 · This paper aims to use machine learning techniques and the most effective algorithm for preventing fires in mines. Two machine learning techniques, the naive Bayes classifier and support vector machines (SVMs), were employed to achieve the objective. The algorithm was developed based on the dependency of the indiing gas amount on .
WhatsApp: +86 18037808511WEBFeb 14, 2022 · Building the Dataset. Based on the literature, researchers have employed various mine's fire influencing parameters including O 2, N 2, and CO 2 in percent, CO in parts per million, and temperature in degree Celsius as input parameters to predict the early detection of mine fire in underground coal mines [4, 12].In order to .
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