Spontaneous combustion of sulfide ore is one of the most common disasters in the process of ore mining, storage, and transportation, which can lead to a series of safety and environmental problems, thus affecting sustainable development in society. In this paper, four imidazolium-based ionic liquids: [BMIM][I], [BMIM][BF₄], [EMIM][BF₄], and [BMIM][NO₃], were selected for inhibition experiments with sulfide ores to reveal the inhibition performance of ionic liquids against spontaneous combustion. The results show that the main products from the reaction were Fe₂O₃ and SO₂, produced during the process of oxidation and spontaneous combustion and that the reaction moves towards a higher temperature under the action of ionic liquid, indicating that ionic liquids have a significant inhibition effect on the spontaneous combustion of sulfide ore. At the same temperature, the apparent activation energies of the samples treated with ionic liquids were all greater than those of the control samples, indicating that imidazolium-based ionic liquids can effectively reduce the spontaneous combustion tendency of sulfide ores. In addition, compared with other ionic liquids, [BMIM][NO₃] had a more pronounced inhibition effect, with the activation energies of both ore samples maximally increased by 8.4% and 10.2% after [BMIM][NO₃] treatment. This is due to the ability of [BMIM][NO₃] to better isolate the samples from oxygen in the air and reduce the effective collisions between active molecules, thus inhibiting and retarding the spontaneous combustion of sulfide ores.

Fault tree analysis (FTA) is one of the important analysis methods of safety system engineering commonly utilized in various industries to evaluate and improve the reliability and safety of complex systems. To grasp the current situation and development trend of FTA research and to further point out FTA’s future development directions, 1469 FTA-related articles from the literature were retrieved from the SCIE and SSCI databases. Informetric methods, including co-authorship analysis, co-citation analysis and co-occurrence analysis, were adopted for analyzing the cooperation relationship, research knowledge base, research hotspots and frontier in the FTA research field. The results show that China has the highest number of publications, and the Loughborough University of England has the highest number of publications of relevant institutions. Dynamic fault tree analysis, fuzzy fault tree analysis and FTA based on binary decision diagrams are recognized as the knowledge bases in FTA studies. “Reliability Engineering and System Safety”, “Safety Science” and “Fuzzy Sets and Systems” are the core journals in this field. Fuzzy fault tree analysis, dynamic fault tree analysis based on Bayesian networks and FTA combined with management factors may be both the main research hotspots and the frontiers. Then, by deriving the above results, this study can help scholars better master the current research status and frontiers of FTA to improve system reliability and safety.

Risk assessment is of great significance in industrial production and sustainable development. Great potential is attributed to machine learning in industrial risk assessment as a promising technology in the fields of computer science and the internet. To better understand the role of machine learning in this field and to investigate the current research status, we selected 3116 papers from the SCIE and SSCI databases of the WOS retrieval platform between 1991 and 2022 as our data sample. The VOSviewer, Bibliometrix R, and CiteSpace software were used to perform co-occurrence analysis, clustering analysis, and dual-map overlay analysis of keywords. The results indicate that the development trend of machine learning in industrial risk assessment can be divided into three stages: initial exploration, stable development, and high-speed development. Machine learning algorithm design, applications in biomedicine, risk monitoring in construction and machinery, and environmental protection are the knowledge base of this study. There are three research hotspots in the application of machine learning to industrial risk assessment: the study of machine learning algorithms, the risk assessment of machine learning in the Industry 4.0 system, and the application of machine learning in autonomous driving. At present, the basic theories and structural systems related to this research have been established, and there are numerous research directions and extensive frontier branches. “Random Forest”, “Industry 4.0”, “supply chain risk assessment”, and “Internet of Things” are at the forefront of the research.

Accident prevention is of great significance in avoiding or reducing all kinds of casualties and economic losses, and is one of the main challenges for social sustainable development. Hence, it has been an active research field for many decades around the world. To master the research status of accident prevention, and explore the knowledge base and hot trends, 1294 papers from the WOS retrieval platform SCIE and SSCI databases from 1990 to 2021 were selected as data samples. Co-occurrence analysis, co-citation analysis, co-authorship analysis, and keyword analysis were performed on the literature on accident prevention research with bibliometric analysis methods. The study showed that the United States ranked first in the number of publications of any country/region and Georgia Inst Technol ranked first in the number of institutional publications. System analysis and accident model establishment, analysis of construction accidents, road accident prevention, and safety culture and safety climate are the knowledge base in the accident prevention studies and the core journals in this field are Safety Science, Accident Analysis and Prevention, Pediatrics, and Reliability Engineering & System Safety. There are four major research hotspots in accident prevention studies: routine accident prevention, model-based research, systems analysis and accident prediction, and occupational safety and public health research. At present, the basic theory and structural system of accident prevention research have been basically established, with many research directions and a wide range of frontier branches. Safety management, public safety, Bayesian networks, and simulation are the research frontiers of accident prevention.

Fire is a typical disaster in the processing industry. Ionic liquids, as a type of green flame retardant, play an important role in process safety. In order to grasp the current research status, hotspots, and frontiers in the field of ionic liquids in flame retardancy, the bibliometric mapping method is applied to study the relevant literature in Web of Science datasets from 2000–2022 in this paper. The results show that the research on ionic liquids in flame retardancy is multidisciplinary and involves some disciplines such as energy science, material science, and environmental protection. Journal of Power Sources, Polymer Degradation and Stability, ACS Applied Materials and Interfaces, and Chemical Engineering Journal are the core journals in the field. The results of keyword co-occurrence indicate that the hotspots of research can be divided into five components: the improvement and application of pure ionic liquids electrolytes, the research of gel polymer electrolytes, applying ionic liquids to enhance the polymer materials’ flame retardancy properties, utilizing ionic liquids and inorganic materials to synergize flame retardant polymers, and using ionic liquids flame retardant to improve material’s multiple properties. The burst terms and time zone diagram’s results point out the combination of computational quantum chemistry to study the flame retardancy mechanism of ionic liquids, the study of fluorinated electrolytes, ionic liquids for smoke suppression, phosphorus-containing ionic liquids for flame retardant, and machine learning-assisted design of ILs flame retardants are the research frontiers and future research trends.