This paper aims to explore the current status, research trends and hotspots related to the field of infrared detection technology through bibliometric analysis and visualization techniques based on the Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) articles published between 1990 and 2018 using the VOSviewer and Citespace software tools. Based on our analysis, we first present the spatiotemporal distribution of the literature related to infrared detection technology, including annual publications, origin country/region, main research organization, and source publications. Then, we report the main subject categories involved in infrared detection technology. Furthermore, we adopt literature cocitation, author cocitation, keyword co-occurrence and timeline visualization analyses to visually explore the research fronts and trends, and present the evolution of infrared detection technology research. The results show that China, the USA and Italy are the three most active countries in infrared detection technology research and that the Centre National de la Recherche Scientifique has the largest number of publications among related organizations. The most prominent research hotspots in the past five years are vibration thermal imaging, pulse thermal imaging, photonic crystals, skin temperature, remote sensing technology, and detection of delamination defects in concrete. The trend of future research on infrared detection technology is from qualitative to quantitative research development, engineering application research and infrared detection technology combined with other detection techniques. The proposed approach based on the scientific knowledge graph analysis can be used to establish reference information and a research basis for application and development of methods in the domain of infrared detection technology studies.

The high-temperature environment is a major factor that affects deep mining. Cooling has become a major expense, accounting for up to 25% of the total energy consumption of such mines. To address methods of cooling and the cooling cost, this paper studies the influence of the ventilation duct layout on the cooling effect. Six models were created in ICEM-CFD (3D modeling software), and the influence of cold airflow diffusion on the temperature of the mine environment was numerically simulated using ANSYS Fluent. Under the condition of the same ventilation volume, two models utilizing single pipe and double pipe scenarios were established, and six points were selected as the pipeline suspension position, forming six ventilation duct models. The cooling effect of each model was evaluated by analyzing the average temperature of the roadway section, the three-dimensional distribution of the roadway temperature and the velocity streamline of the whole roadway. The results show that the double-tube model has greater advantages than the single-tube model does, due to its superior local temperature, average temperature of the cross-section, range below 303 K, temperature uniformity and local wind speed. Among the models, model 4 (diameter of 0.5 m, 1.9 m away from the bottom of the roadway and 2.4 m away from the center of the circle) is the best pipeline layout scheme for comprehensive temperature values, roadway temperature uniformity and other factors. The average temperature is 299.3 K within 8 m from the mining face, which is 1.66 K lower than that of the single tube model. This configuration will increase the comfort of the mining environment and reduce cooling costs. These results can provide a reference for ventilation duct layouts of roadways in high temperature mines.

Elevators have become an integral part of modern buildings, and technological advances have enabled the monitoring of their operational status through sensor technology. In response to the development of the elevator industry and the need for practical elevator operation risk evaluation, this paper proposes an elevator risk evaluation method based on fuzzy theory and machine learning methods. The method begins by establishing an elevator operation risk evaluation index system. The traditional fuzzy comprehensive evaluation method is then employed to evaluate the risk levels of the 50 elevators studied. The collected index data and labels (fuzzy comprehensive evaluation results) are used as inputs to train the support vector machine (SVM) model. To optimize the SVM model, the maximum information coefficient method, enhanced by the correlation-based feature selection (MIC-CFS) method, is employed to select features for the index input to the SVM model. The improved gray wolf algorithm (IGWO) method optimizes the SVM. Finally, the model’s performance is verified using new index data. The experimental results demonstrate that introducing machine learning methods for elevator risk evaluation saves time and effort while providing good accuracy compared to the traditional expert evaluation method. The optimization of the SVM model by IGWO and feature selection by the MIC-CFS method results in a more concise SVM model that converges faster during training, exhibits better stability, and achieves higher accuracy.

This research aimed to optimize the experimental conditions of biodesulfurization of sulfide ore and to evaluate the flame-retardant effect after desulfurization under optimal conditions. Six experimental factors were determined: particle size of ore sample, ambient temperature, rotary speed of the shaking table, volume of bacteria liquid, concentration of leaching aid (Tween80), and pH value of acidizing ore sample. Desulfurization efficiency was used as the main characterization index of the desulfurization effect in optimization studies. Particle size of ore sample, rotary speed of the shaking table, and volume of bacteria liquid inoculated were selected as significant factors by a Plackett–Burman experiment. Modeling, optimization, and analysis of the interactive effects of these factors, notably between particle size and bacteria liquid, were performed using a Box–Behnken design with response surface methodology. The optimum operating conditions were found to be: particle size of 120 to 140 mesh, rotary speed of 175 rpm, and bacteria liquid of 111 mL. Under these conditions, a significant rise of 8.1% was seen in 5-day average desulfurization efficiency. The 5-day oxidation weight gain rate of desulfurized ore was 2.73%, while that of the control group was 4.78%. The results show that, after optimized desulfurization, the surface oxidizability and spontaneous combustion tendency of the ore are reduced.

Ultrasonic technology is being increasingly explored in minerals processing. In this paper, ultrasonic treatment was introduced as a novel method for microbial desulfurization of sulfide ores. A Box–Behnken experiment was performed to find the best combination of factor levels for the following experiments; consequently, the combination of factor levels at the maximum 5-day average desulfurization rate at 20 g of ore was a particle size of 120–140 mesh, a shaker speed of 175 rpm, and a dosage of 111 mL of bacterial solution. Under these conditions, a microbial desulfurization experiment of sulfide ores by ultrasonic treatment was carried out, and the effect of the particle size, the ultrasonic action time, and the ultrasonic power were investigated. Results indicated that the ultrasonic effect was not significant for ore samples with larger particle sizes, and the appropriate increase in ultrasonic action time was beneficial to the improvement of desulfurization rate, but the effect showed a decreasing trend when it exceeded 50 min, and the best desulfurization effect was achieved when the power was 300 W. This study demonstrated that the effect of microbial desulfurization can be greatly enhanced by ultrasonic treatment.

Permafrost has been thawing faster due to climate change which would release greenhouse gases, change the hydrological regimes, affect buildings above, and so on. It is necessary to study the thawing process of frozen soil. A water-heat coupling model for frozen soil thawing is established on Darcy’s law and Heat Transfer in Porous Media interfaces in Comsol Multiphysics 5.5. Three curves of total liquid water volume, minimum temperature, and total heat flux in the thawing process are obtained from a numerical simulation. The distributions of liquid water, temperature, and pressure based on time are simulated too. The liquid water distribution is consistent with the total liquid water volume curve. The temperature distribution is confirmed by the minimum temperature and total heat flux curve. The pressure distribution represents ice in the frozen soil that generates negative pressure during the melting process. The numerical simulation research in this article deepens the understanding of the internal evolution in the process of frozen soil thawing and has a certain reference value for subsequent experimental research and related applications.