Gas chromatography–mass spectrometry (GC–MS) is a powerful tool in process safety, widely applied to identify and monitor hazardous chemicals in industrial environments. Its ability to separate complex mixtures and unambiguously characterize components makes it essential for detecting leaks, monitoring volatile organic compound emissions, analyzing contaminants, and ensuring regulatory compliance. This paper highlights GC–MS applications in fire risk assessment, chemical hazard evaluation, and transportation safety. Key studies include compositional analysis of flammable gases emitted during battery thermal runaway, following UL 9540A to inform fire and explosion protection standards (NFPA 855, IFC 608, UL 9540). GC–MS has also been used to quantify flammable gases from expandable polymeric beads and molding compounds, supporting safe handling and transport decisions under UN Test U.1 for UN 2211 and UN 3314 classifications. In fire investigations, GC–MS identifies ignitable liquid residues using ASTM E1618, enhancing understanding of fire origin, fuel load, and incendiary characteristics. Additional applications include verifying refrigerant blends for safety classification (ASHRAE Standard 34) and coupling GC–MS with calorimetric tools such as ARC, VSP2, and RC‐1. This integration provides complementary chemical, physical, and kinetic data, enabling deeper insight into reaction mechanisms, dynamic hazard mitigation, and accident prevention during scale‐up processes.
Afrasyab kheirdast, Seyed Ali Jozi, Sahar Rezaian
et al.
Background and objective In providing fire management strategies using international and integrated methods, no study has been conducted in district 19 of Tehran, Iran, so far. Therefore, this study aims to propose strategies for fire management and reducing the vulnerability of worn-out buildings in this district using the A’WOT analysis and Freeman’s stakeholder matrix.
Method This is a strategic and applied study. Participants included 20 crisis management experts and managers of the fire departments. The information was collected using interviews and brainstorming. The AʼWOT analysis, a hybrid analytic hierarchy process (AHP)-SWOT method, was used. After determining the internal factors (strength and weakness) and external factors (threat and opportunity), strategies were identified and ranked in a hierarchical manner. Freeman’s stakeholder matrix was used to present the strategic fire management plans. The sensitivity analysis was done using Expert choice software, version 11.
Results The final score obtained from the internal factor evaluation (IFE) and external factor evaluation (EFE) matrices showed that the strategies were offensive and competitive, respectively. Based on the evaluation of quantitative strategic planning, IFE, and EFE matrices, the strategic plans were finally ranked as follows: “the use of local expert forces familiar with the region” with a score of 5.22, “using movable conex boxes to command operations” with a score of 5.08 and “building new stations with advanced firefighting equipment” with a score of 4.96. Based on Freeman’s stakeholder matrix, the offensive strategies “building new fire stations with advanced firefighting equipment,” with a score of 107 and “visiting worn-out buildings and holding a maneuver to increase the personnel operational capacity” with a score of 98 were placed in the first and second ranks, respectively.
Conclusion By examining the weaknesses, strengths, opportunities, and threats in district 19 of Tehran, we presented strategies to reduce the fire vulnerability of worn-out buildings. The AʼWOT analysis is a proper scientific and operational method for fire management in worn-out buildings in this district. Freeman’s stakeholder matrix can be a suitable model for ranking fire management strategies. Based on this matrix, offensive strategies to manage the fires in the study area can be predicted and implemented.
Risk in industry. Risk management, Industrial safety. Industrial accident prevention
In industrial settings, the accurate detection of anomalies is essential for maintaining product quality and ensuring operational safety. Traditional industrial anomaly detection (IAD) models often struggle with flexibility and adaptability, especially in dynamic production environments where new defect types and operational changes frequently arise. Recent advancements in Multimodal Large Language Models (MLLMs) hold promise for overcoming these limitations by combining visual and textual information processing capabilities. MLLMs excel in general visual understanding due to their training on large, diverse datasets, but they lack domain-specific knowledge, such as industry-specific defect tolerance levels, which limits their effectiveness in IAD tasks. To address these challenges, we propose Echo, a novel multi-expert framework designed to enhance MLLM performance for IAD. Echo integrates four expert modules: Reference Extractor which provides a contextual baseline by retrieving similar normal images, Knowledge Guide which supplies domain-specific insights, Reasoning Expert which enables structured, stepwise reasoning for complex queries, and Decision Maker which synthesizes information from all modules to deliver precise, context-aware responses. Evaluated on the MMAD benchmark, Echo demonstrates significant improvements in adaptability, precision, and robustness, moving closer to meeting the demands of real-world industrial anomaly detection.
Wi-Fi is currently considered one of the most promising solutions for interconnecting mobile equipment (e.g., autonomous mobile robots and active exoskeletons) in industrial environments. However, relability requirements imposed by the industrial context, such as ensuring bounded transmission latency, are a major challenge for over-the-air communication. One of the aspects of Wi-Fi technology that greatly affects the probability of a packet reaching its destination is the selection of the appropriate transmission rate. Rate adaptation algorithms are in charge of this operation, but their design and implementation are not regulated by the IEEE 802.11 standard. One of the most popular solutions, available as open source, is Minstrel, which is the default choice for the Linux Kernel. In this paper, Minstrel performance is evaluated for both static and mobility scenarios. Our analysis focuses on metrics of interest for industrial contexts, i.e., latency and packet loss ratio, and serves as a preliminary evaluation for the future development of enhanced rate adaptation algorithms based on centralized digital twins.
Deep learning-based machine listening is broadening the scope of industrial acoustic analysis for applications like anomaly detection and predictive maintenance, thereby improving manufacturing efficiency and reliability. Nevertheless, its reliance on large, task-specific annotated datasets for every new task limits widespread implementation on shop floors. While emerging sound foundation models aim to alleviate data dependency, they are too large and computationally expensive, requiring cloud infrastructure or high-end hardware that is impractical for on-site, real-time deployment. We address this gap with LISTEN (Lightweight Industrial Sound-representable Transformer for Edge Notification), a kilobyte-sized industrial sound foundation model. Using knowledge distillation, LISTEN runs in real-time on low-cost edge devices. On benchmark downstream tasks, it performs nearly identically to its much larger parent model, even when fine-tuned with minimal datasets and training resource. Beyond the model itself, we demonstrate its real-world utility by integrating LISTEN into a complete machine monitoring framework on an edge device with an Industrial Internet of Things (IIoT) sensor and system, validating its performance and generalization capabilities on a live manufacturing shop floor.
Within the framework of the legislation of the Russian Federation aiming to protect the life and health of employees, studies in the field of occupational risk assessment have become more relevant. Occupational risk assessment and management are one of the basic measures to prevent accidents and occupational illnesses, which facilitates the preservation of employees' health and positively affects the performance and efficiency of enterprises at large. The study aims to determine the causes of industrial accidents and assess occupational risks using probabilistic graphical models based on the example of industrial sectors of the economy. Today, a risk-oriented approach is the main mechanism of safety management that not only helps to identify potential threats but also to develop strategies to minimize them, thus ensuring a higher level of protection for employees. The following results have been obtained: the data on the injury rate in various regions of the Russian Federation have been collected and processed; a methodological approach based on probabilistic graphical models allowing for the visualization of complex interconnections between various factors affecting labor safety has been developed. The implementation of the proposed approach will enable not only to minimize the number of industrial accidents but also to develop a safe working environment. The concept of the risk-oriented approach allows for the transition from the response actions to an accident to the prevention based on preventive measures.
The increasing demand for intelligent safety systems in industrial environments, particularly within the steel manufacturing sector, has led to the development of advanced solutions aimed at minimizing workplace hazards. This paper presents a microcontroller-based hand safety system designed to reduce the risk of hand-related injuries near industrial cutting machines. The proposed system integrates an ultrasonic sensor to detect proximity and an MLX90614 thermal sensor to confirm the presence of a human hand based on thermal signatures. Upon detecting an intrusion within a critical range, the system activates a buzzer and displays a real-time warning message on an LCD screen while simultaneously deactivating the machine using a relay switch. This layered detection mechanism significantly reduces false positives and enhances response accuracy. Powered by an Arduino microcontroller, the system ensures real-time decision-making and rapid intervention, offering improved safety without human dependency. Testing in a simulated industrial environment demonstrated high reliability, quick response, and robust performance under varying operational conditions. The system represents a low- cost, scalable solution for real-time accident prevention in hazardous workspaces and offers a foundation for future enhancements using IoT, machine learning, and predictive maintenance features for broader industrial applications.
ABSTRACT Propylene oxide (PO) is toxic, flammable and explosive. Accidents from the release of PO from large-scale storage tanks can have disastrous consequences associated with meteorological conditions. Therefore, this study explores the dangerous scenarios of PO leakage from a 1000 m3 spherical tank in a chemical plant located in an industrial park during various seasons using the Aerial Location of Hazardous Atmospheres software. Our results show that the maximum hazard distance of poisoning, flash fire and vapour cloud explosion caused by leakage and diffusion from the PO storage tank occur in the summer season and the affected distances were 2900, 372, and 374 m, respectively. In regard the thermal radiation resulting from a pool fire scenario, the longest threat distance was 110 m in the summer. For the boiling liquid expanding vapour explosion (BLEVE), the maximum thermal effect was 2100 m in winter. The BLEVE scenario has the largest impact area and its toxic vapour cloud is the primary accident. Adequate safety management and technical measures should be put in place for the prevention and emergency response of accidental PO release in the factory. GRAPHICAL ABSTRACT
The industrial safety of health and medical workers who struggling on the front lines of diseases and disasters to protect public health and life, is threatened by working environment hazard factors that damage health. Despite they are exposing to hazard factors and accumulating mental well-being risks frequently, the social discussions and national efforts for their health were insufficient due to complacent perceptions that “They would be safe from occupational diseases and industrial accidents because of the working environment characteristic that set as medical work”. Korean society needs to enhance the importance of industrial safety because if the mental well-being risk of health and medical workers is not healed in proper time, the deterioration of work engagement can harm personal health and patient life and threat public health and cause a national crisis. Therefore, this study analyzed occupational safety hazard factors that hinder health and medical worker's mental well-being focusing on doctor, nurse, medical technician based on Rutter's ‘Cumulated Risk Model’ that explaining the more accumulate of exposure to simultaneous risk factors, the greater ripple effect of human's internalization and externalization problem. The multiple regression analysis were performed with SPSS Statistics 29.0 for 749 health and medical workers who attended the 6th Korean Working Conditions Survey published by the Korea Occupational Safety and Health Research. As a result, the health and medical worker's mental well-being were hindered whey they exposure to occupational safety hazard factors, emotional labor and quantitative labor intensity were finally verified as occupational safety hazard factors that threatened the mental well-being of them. Based on the results, this study suggested ① services and policies to effectively counter occupational safety hazard factors that hinder the health and medical worker's mental well-being, ② prevention and solution plan about exposure problem to occupational safety hazard factors, ③ customized strategies to promote health and medical workers' mental well-being.
Safety is one of the goals of a smart city. To study storage tank explosion damage in a city’s chemical industrial parks, determine the position of control measures according to the situation, and realize the analysis of the measured utility, we proposed the area damage probability importance distribution. In this way, the prediction and prevention of risk in chemical industrial parks can be achieved intelligently. The concept of area damage probability importance distribution was given, and the utility analysis method of the control measures for storage tank explosion accidents was put forward. It is concluded that the area damage probability importance distribution represents the change degree of damage probability: that is, the damage degree of storage tank explosion in a chemical industrial park. The control measures for a storage tank explosion can be set up in varying positions, as the explosion damage is mainly caused by shock waves; the blast walls are selected as the measure set, and the calculation method for the area damage probability is modified. By comparing the calculated area damage probability distribution before and after, evaluation of the control measures’ effectiveness can be achieved. Finally, the flow chart of the algorithm is given. The example analysis shows that the calculation process and analysis results meet the design requirements of the algorithm. The effectiveness of the method, the distribution characteristics, and the significance and function of the importance distribution of damage probability are discussed. This provides an effective method for smart cities to predict and prevent the impact of an explosion at chemical industrial parks.
Luke Anthony Fiorini, Liberato Camilleri, Mark Gauci
The Occupational Health and Safety Authority (OHSA) was established in Malta in 2002. Since then, trends indicate that non-fatal accidents have decreased in Malta, while changes in fatal accidents are less clear. Since these trends have not been statistically investigated before, this study aims to do so. The study also aims to analyse the link between specific OHSA deterrent measures and changes in non-fatal accidents. A database compiled by the OHSA on the frequency of accident statistics in Malta and OHSA deterrent measures between 2002 and 2022 was analysed. The study demonstrated that the incidence of fatal and non-fatal accidents decreased significantly during the analysed period. The incidence of non-fatal accidents was more common in the transport and storage sector, the construction sector and the manufacturing sector. Fatal accidents were most frequent within the construction sector. Fatal accidents were common among the self-employed and foreign workers. Deterrents, especially those related to inspections and fines, were significantly associated with a decrease in fatal and non-fatal accidents. The study underscores those accidents have declined significantly since the establishment of the OHSA and demonstrates the benefits of specific deterrent measures. Continued focus is required on specific areas, including the construction sector, self-employed workers and foreign workers.
Industrial anomaly detection (IAD) plays a crucial role in the maintenance and quality control of manufacturing processes. In this paper, we propose a novel approach, Vision-Language Anomaly Detection via Contrastive Cross-Modal Training (CLAD), which leverages large vision-language models (LVLMs) to improve both anomaly detection and localization in industrial settings. CLAD aligns visual and textual features into a shared embedding space using contrastive learning, ensuring that normal instances are grouped together while anomalies are pushed apart. Through extensive experiments on two benchmark industrial datasets, MVTec-AD and VisA, we demonstrate that CLAD outperforms state-of-the-art methods in both image-level anomaly detection and pixel-level anomaly localization. Additionally, we provide ablation studies and human evaluation to validate the importance of key components in our method. Our approach not only achieves superior performance but also enhances interpretability by accurately localizing anomalies, making it a promising solution for real-world industrial applications.
Traditional industrial automation systems require specialized expertise to operate and complex reprogramming to adapt to new processes. Large language models offer the intelligence to make them more flexible and easier to use. However, LLMs' application in industrial settings is underexplored. This paper introduces a framework for integrating LLMs to achieve end-to-end control of industrial automation systems. At the core of the framework are an agent system designed for industrial tasks, a structured prompting method, and an event-driven information modeling mechanism that provides real-time data for LLM inference. The framework supplies LLMs with real-time events on different context semantic levels, allowing them to interpret the information, generate production plans, and control operations on the automation system. It also supports structured dataset creation for fine-tuning on this downstream application of LLMs. Our contribution includes a formal system design, proof-of-concept implementation, and a method for generating task-specific datasets for LLM fine-tuning and testing. This approach enables a more adaptive automation system that can respond to spontaneous events, while allowing easier operation and configuration through natural language for more intuitive human-machine interaction. We provide demo videos and detailed data on GitHub: https://github.com/YuchenXia/LLM4IAS.
This study investigates the impact of industrial agglomeration on land use intensification in the Yangtze River Delta (YRD) urban agglomeration. Utilizing spatial econometric models, we conduct an empirical analysis of the clustering phenomena in manufacturing and producer services. By employing the Location Quotient (LQ) and the Relative Diversification Index (RDI), we assess the degree of industrial specialization and diversification in the YRD. Additionally, Global Moran's I and Local Moran's I scatter plots are used to reveal the spatial distribution characteristics of land use intensification. Our findings indicate that industrial agglomeration has complex effects on land use intensification, showing positive, negative, and inverted U-shaped impacts. These synergistic effects exhibit significant regional variations across the YRD. The study provides both theoretical foundations and empirical support for the formulation of land management and industrial development policies. In conclusion, we propose policy recommendations aimed at optimizing industrial structures and enhancing land use efficiency to foster sustainable development in the YRD region.
In this paper, we use open-source tools to perform quantum resource estimation to assess the requirements for industry-relevant quantum computation. Our analysis uses the problem of industrial shift scheduling in manufacturing and the Quantum Industrial Shift Scheduling algorithm. We base our figures of merit on current technology, as well as theoretical high-fidelity scenarios for superconducting qubit platforms. We find that the execution time of gate and measurement operations determines the overall computational runtime more strongly than the system error rates. Moreover, achieving a quantum speedup would not only require low system error rates ($10^{-6}$ or better), but also measurement operations with an execution time below 10ns. This rules out the possibility of near-term quantum advantage for this use case, and suggests that significant technological or algorithmic progress will be needed before such an advantage can be achieved.
In order to prevent accident cases and improve safety in the mining industry, a safety risk assessment and management process is needed to identify and respond to high-risk hazards in mines. This study aims to investigate the main safety risks factors influencing the typology of accidents in the Panzhihua OP-UG iron ore mine with the concept of minimizing them, reducing injuries and fatalities, and improving prevention policies. A methodology based on the analytic hierarchy process and fuzzy comprehensive evaluation (AHP-FCE) is applied to conduct a study on the assessment and evaluation of mine safety risks. Upon investigating the safety situation at the mine site, 85 risk factors were identified, of which 49 factors were considered to be non-threatening and therefore compatible with existing control measures. The remaining potential hazards, altogether 36 factors, were ultimately categorized into six major specific groups. A mine safety index system and safety risk evaluation model are established to support the evaluation process. The results show that the overall risk level of the Panzhihua OP-UG iron mine is at a medium level with a score of 86.5%. Appropriate risk management measures were recommended for each risk factor from the perspectives of theoretical analysis, safety system optimization of mine technology, disaster prevention and control of slope failure, etc. Finally, this research serves as a great industrial value and academic significance to provide technical support for the safety production of mining enterprises. Hence, the FCE method can serve as a technique to accurately evaluate the impact of iron mine risk.
Occupational health and safety K3 ensures that people are not injured or become sick due to hazards in the workplace. ensuring that preventing the risk of frequent industrial accidents and diseases that cause the loss of workers' lives and or affect employee performance. K3 is a science that focuses on ensuring safety in the workplace. Occupational safety and health (K3) is defined as the science and application of technology regarding the prevention of work accidents and occupational diseases. By providing OSH protection, it is expected that workers can work safely, healthily and productively. Research purposes. To find out health and safety K3 on employee performance in the workplace. Method. Descriptive with observational cohort. Prospective. Results. Knowing the level of occupational health and safety K3 on employee performance at work. Conclusion. Therefore, it can be concluded that in occupational health and safety risks of OSH to improve the safety of health workers, which includes various occupational risk factors in the workplace, provide refresher training and routine induction training on OSH and use personal protective equipment (PPE). To ensure safety and health in the sector prioritizing the health of workers.
In the life of companies operating with hazardous substances, the daily task is to maintain the safety on a high level and to introduce actions to increase safety performance. The companies covered by the Disaster Prevention Act have a special task, to operate a safety management system that also satisfies industrial safety aspects. To make this as efficient as possible, good practices from other safety management systems can be built into the currently used management system at the company. Perhaps the most frequently operated safety management systems for organizations producing and processing hazardous substances are the Occupational Health and Safety Management according to the ISO 45001 standard and the Process Safety Management systems. A comparison of the latter with what is required by law points to several points that may designate areas for improvement in the certified safety management system. In this way, they provide a basis for the development of a new system, that complying with the prescribed requirements, helps to maintain a high level of safety, to prevent the occurrence of accidents, and to work with safety-conscious employees and suppliers.
The article discusses the problem of determining safe work experience in conditions of increased intensity of hard work. It is especially relevant in construction industry. The need for an objective analysis of industrial accidents is an urgent task not only for the prevention of injuries, but also for improving environmental safety, interpreted as occupational risk management in order to protect the health of people of active working age. The research methodology was based on the analysis of the characteristics of the occupational diseases risk, depending on the level of existing harmful production factors, as well as the length of service. Occupational risk is directly related to the dynamics of changes in harmful and hazardous production factors during an employee's work experience. This risk gradually increases nonlinearly over the course of the employee's work experience, and in the experience sector the risk begins changing at an increasing rate. The new system for assessing working conditions introduced in 2014 in Russia, although it helps to reduce the annual increase in risk, but cannot provide completely environmentally friendly and safe working conditions and acceptable risk (R). The environmental safety of production activities is a big question and is at high risk in the construction industry, in particular due to the huge number of concealment of occupational injuries, violations of the regenerative capacity of the human body, as well as the extension of the retirement age to 65 and 70 years. Therefore, it is required to introduce a mechanism (model), providing a real system for assessing working conditions.