非煤粉尘抑爆领域文献计量分析

消防科学与技术 ›› 2022, Vol. 41 ›› Issue (10): 1464-1467.

非煤粉尘抑爆领域文献计量分析

陆继锋1,2，李方2,3，刘吉庆3

（1. 山东科技大学文法学院，山东青岛 266590；2. 青西新区民安应急与安全管理研究院，山东青岛 266590；3. 山东科技大学安全与应急管理学院，山东青岛 266590）

出版日期:2022-10-15 发布日期:2022-10-15
通讯作者: 国家自然科学基金（52074174）；青岛西海岸新区2020 年科技计划科技惠民专项（2020-31）
作者简介:作者简介：陆继锋（1980- ），男，山东济宁人，青岛西海岸新区民安应急与安全研究院院长，博士，主要从事应急与安全管理等方面的研究，山东省青岛市黄岛区前湾港路579号，266590。

Bibliometric analysis in the field of non-coal dust explosion suppression

Lu Jifeng1,2，Li Fang2,3，Liu Jiqing3

（1. School of Humanity and Law, Shandong University of Science and Technology, Shandong Qingdao 266590, China； 2. Qingdao West Coast New District Minan Emergency and Safety Management Research Institute, Shandong Qingdao 266590, China； 3. School of Safety and Emergency Management, Shandong University of Science and Technology, Shandong Qingdao 266590, China）

Online:2022-10-15 Published:2022-10-15

摘要/Abstract

摘要： 摘要：为系统掌握非煤粉尘抑爆领域研究进展，以中国知网（CNKI）为数据来源，采用文献计量法对108篇非煤粉尘抑爆领域文献的数字特征进行分析，利用VOSviewer 软件绘制知识图谱，以探究非煤粉尘抑爆领域研究演进、热点与前沿。结果表明：非煤粉尘抑爆领域文献数量总体呈增长趋势，其研究进程可分为3个阶段；选取108篇非煤粉尘爆炸抑爆领域相关论文，其研究方向主要集中在安全科学与灾害防治、矿业工程、有机化工等学科；研究热点主要涉及可燃性粉尘爆炸、抑爆剂、抑爆系统和爆炸特性以及抑制机理等主题；从宏观措施研究转变为微观机理研究是研究变化趋势。

关键词: 关键词：非煤粉尘抑爆；文献计量；知识图谱；VOSviewer软件

Abstract: Abstract: To investigate the feasibility of machine learning in predicting the minimum ignition temperature of pulverized coal clouds, based on the minimum ignition temperature (MITc) of coal dust clouds and the influence factors obtained from previous tests using Godbert-Greenwal furnace, analyzed the correlation of the influence factors. The prediction effectiveness of the three machine learning models in terms of both the minimum ignition temperature of coal dust clouds and the incidence of ignition was evaluated and analysed using the AUC/ROC, Kappa coefficient, sensitivity, specificity, MAE and RMSE metrics. The results showed that the RSM model has the worst prediction effect; the RF model has the best accuracy and stability in predicting the MITc and ignition probability of pulverized coal clouds, and with the Bagging model, the AUC values are greater than 0.85 in predicting the ignition incidence, but the effect of predicting MITc is poor. The results provide a new research idea on prediction of coal dust cloud ignition sensitivity.

Key words: Key words: coal dust cloud; ignition sensitivity; random forest; Bagging model

陆继锋1,2，李方2,3，刘吉庆3. 非煤粉尘抑爆领域文献计量分析[J]. 消防科学与技术, 2022, 41(10): 1464-1467.

Lu Jifeng1,2，Li Fang2,3，Liu Jiqing3. Bibliometric analysis in the field of non-coal dust explosion suppression[J]. Fire Science and Technology, 2022, 41(10): 1464-1467.