Gas leaks can cause disasters at process sites, including fires and explosions, and thus, effective gas-leak detection systems are required. This study investigated the limitations of conventional detectors and introduced an innovative ultrasonic sensor-based approach for continuous monitoring. A new configuration for a stationary remote ultrasonic gas-leak monitoring system is proposed. The selected material was 1-Butene. The detection probability was assessed through a simulation based on a gas-leak scenario, detailing the selection criteria for leak sites and simulation conditions. Computational fluid-dynamics (CFD) simulations were used to evaluate the detection capability of the existing system, whereas Monte Carlo simulations were used to compare it with the proposed ultrasonic system. The CFD simulation was performed by setting the lower detection limit of the concentration-measurement-type gas detector to 600 ppm, and the leak-detection time was approximately 8.895 s. A Monte Carlo simulation accounting for wind direction showed that the existing system’s average detection probability was 3.41%. By integrating the existing and new systems, the detection probability increased by 96.58% and 3.92% compared with that using only the concentration-measurement and ultrasonic measurement methods, respectively. These rates provide valuable insights for future applications in oil refining and petrochemical industries.

Outdoor pipeline leaks are difficult to accurately measure using existing concentration measurement systems installed in petrochemical plants owing to external air currents. Besides, leak detection is only possible for a specific gas. The purpose of this study was to develop an image/ultrasonic convergence camera system that incorporates artificial intelligence (AI) to improve pipe leak detection and establish a real-time monitoring system. Our system includes an advanced ultrasonic camera coupled with a deep learning-based object-detection algorithm trained on pipe image data from petrochemical plants. The collected data improved the accuracy of detected gas leak localization through deep learning. Our detection model achieves an mAP₅₀ (Mean average precision calculated at an intersection over union (IoU) threshold of 0.50)score of 0.45 on our data and is able to detect the majority of leak points within a system. The petrochemical plant environment was simulated by visiting petrochemical plants and reviewing drawings, and an outdoor experimental demonstration site was established. Scenarios such as flange connection failure were set under medium-/low-pressure conditions, and the developed product was experimented under gas leak conditions that simulated leakage accidents. These experiments enabled the removal of potentially confounding surrounding noise sources, which led to the false detection of actual gas leaks using the AI piping detection technique.

A main cause of fires and explosions in lithium-ion batteries is the generation of combustible gases by them, and when a large number of batteries are densely packed, like in an Energy Storage System, there is a high risk of thermal runaway and fire propagation. Currently, many studies are being conducted worldwide to predict and prevent the generation of combustible gases, and thermal runaway in lithium-ion batteries, but they are still in progress. Therefore, in this study, we analyzed the gases generated before and after thermal runaway in lithium ion batteries, to prepare a basis for reducing the risk of thermal runaway. We aimed to establish the basis for prevention by early detection in the event of thermal runaway, by understanding the type and characteristics of the generated gases. For the experiment, lithium ion batteries were classified in terms of appearance (cylindrical, prismatic, pouch type), and cathode materials (NCM, NCA, LFP). The gases generated was measured against time. An FT-IR analyzer was used for gas measurement, and a separate hydrogen sensor was installed in the chamber to analyze changes in the types of gas, and measure the mass of the lithium ion battery over time. In the experiment, CO2 and CO were generated the most during thermal runaway in all lithium-ion batteries. Thereafter, CO2 increased, and CO decreased in the prismatic and pouch types, and both CO2 and CO increased in the cylindrical type. HF (a toxic gas), and H2 having a wide explosive range, were also generated, and the concentrations of these gases were inversely proportional to each other.

Leakage of flammable gases and the resulting explosions in petrochemical plants remain latent risks, capable of occurring at any moment. Therefore, to address these worst-case scenarios within a virtual reality framework, we conducted simulations aimed at predicting and effectively responding to potential damages due to gas leakage. This study presents an analysis of the hazards that can lead to leaks and potential explosions in a petrochemical plant using the BREEZE Incident Analyst program. Rapid and accurate recognition of the risk associated with gas leaks, which can cause extensive damage and explosions, is of paramount importance. This study addresses two main aspects: the prediction of the consequences of gas leaks through simulations and the implementation of appropriate detection measures. Better, more efficient risk management and mitigation strategies were implemented by predicting gas leak paths using BREEZE. Using ultrasonic detection technology, detection was demonstrated to be possible in approximately one-third the time required by conventional detectors, and it is weather-insensitive. Simultaneously, considering plant characteristics such as utility configurations, we propose an additional method to prevent leaks from going undetected. This is achieved by integrating gas detection technology that combines both existing and new technologies.