A Numerical Study on Jet Release from Off-site and Mobile Hydrogen Refueling Station for Separation Distance

High-pressure hydrogen facilities are prone to jet release accidents. In the cases of immediate ignition, jet fire occurs, and delayed ignition can lead to explosion accidents. Therefore, its management is crucial to avoid leakage. In this study, the change in volume fraction of hydrogen and the flammable area around the hydrogen facility were calculated using a computational fluid dynamics model, for the cases of jet release accident in a hydrogen storage tank of off-site hydrogen refueling station and a mobile hydrogen refueling station. The leakage at the off-site hydrogen refueling station was through the opening at the top of the wall. The mobile hydrogen refueling station had hydrogen stagnated in the lower part of the wing body due to the wing body. Most of the hydrogen facilities were included in the hydrogen flammable zone after 10 s of the jet release. Further, after 30 s, the flammable distance was calculated to be approximately twice for of a mobile hydrogen refueling station as compared to a storage type hydrogen refueling station.

Bayesian Processing of Data on Bursts of Pressure Vessels

Two alternative Bayesian approaches are proposed for the prediction of fragmentation of pressure vessels triggered off by accidental explosions (bursts) of these containment structures. It is shown how to carry out this prediction with post-mortem data on fragment numbers counted after past explosion accidents. Results of the prediction are estimates of probabilities of individual fragment numbers. These estimates are expressed by means of Bayesian prior or posterior distributions. It is demonstrated how to elicit the prior distributions from relatively scarce post-mortem data on vessel fragmentations. Specifically, it is suggested to develop priors with two Bayesian models known as compound Poisson-gamma and multinomial-Dirichlet probability distributions. The available data is used to specify non-informative prior for Poisson parameter that is subsequently transformed into priors of individual fragment number probabilities. Alternatively, the data is applied to a specification of Dirichlet concentration parameters. The latter priors directly express epistemic uncertainty in the fragment number probabilities. Example calculations presented in the study demonstrate that the suggested non-informative prior distributions are responsive to updates with scarce data on vessel explosions. It is shown that priors specified with Poisson-gamma and multinomial-Dirichlet models differ tangibly; however, this difference decreases with increasing amount of new data. For the sake of brevity and concreteness, the study was limited to fire induced vessel bursts known as boiling liquid expanding vapour explosions (BLEVEs).

Analysis of Explosion Accidents in a Chemical Plant using STAMP, a Systematic Cause-and-effect Analysis Technique

The process safety management system for chemical plants was introduced approximately 25 years ago. With the improvement in the safety management levels for the safe operation of the chemical plants, the number of serious industrial accidents has gradually decreased; however, increased damages have been observed when accidents do occur. The cause of accidents has also increased in cases where several factors, including social and cultural factors, are complexly related, in addition to facility and human factors. The need for an overall integrated systemic approach related to society, technology, and organization, and a sequential approach for finding the direct cause of accidents, is growing while analyzing the accidents. For this reason, foreign countries have introduced and applied a method to analyze accidents in an integrated manner from a systemic point of view; however, reports of cases or research results used in Korea. In this study, the case of explosion accidents, which occurred during a trial operation at a domestic chemical plant, was analyzed using Systems-Theoretic Accident Model and Processes, a systematic accident analysis technique, to reveal the primary cause, organizational, and operational problems, so that it can be used for future investigations when other accidents occur.

Damages of Underground Facilities in Coal Mines due to Gas Explosion Shock Waves: An Overview

With coal mining depth increase, gas explosion accidents due to the high gas emission rates often occur which cause significant casualties and property damages. Among them, gas explosion shock waves not only can destroy the machines and equipment in mine roadways but also cause the failure of mine ventilation facilities resulting in secondary hazards. Thus, the mines’ serious disasters could happen. For many years, researchers have already done a great lot of works to study damages caused by the impact of shock waves of the gas explosions in underground mines. Research results provide a baseline for judgments of hazard effects by explosions. In this paper, the formation mechanism of the gas explosion shock wave is introduced firstly. Then, the damages for underground facilities, such as mechanical equipment, roadway, and life-saving devices are summarized and reviewed. Finally, a brief discussion about the methods is given, and some preliminary suggestions are also listed for improvements in the future.

How to Determine the Dangerous Potential of Accidents to Domino Effect Detonation in a Hydrocarbon Processing Area?

The crude oil industry has been developed in recent decades due to the uses of this product, as well as its derivatives. One of the worst consequences phenomena that can occur in the process industry is the called domino effect. The domino effect or cascade effect occurs when an initiating event, such as a pool of fire or a vapor cloud explosion, causes a new number of accidents. Moreover, due to the importance of avoiding this phenomenon, the European Commission considers the domino effect analysis as mandatory for industrial facilities. There are methodologies in the specialized literature focused on quantifying the existing risks in the storage and processing of hydrocarbons. However, there is a tendency to develop new procedures that increase the risk perception of these accidents. In addition, it is necessary to develop a method that allows visualizing clearly and concisely the dangerous potential of fire and explosion accidents for the occurrence of the domino effect. Precisely, this research aims to predict the dangerous potential of fire and explosion accidents for the occurrence of the domino effect. For this purpose, a methodology consisting of three fundamental stages is developed. Finally, hydrocarbon storage and processing area is selected to apply the proposed methodology. Overall, the development of graphs that summarize information and show the dangerous potential regarding the escalation of fire and explosion accidents is vital in risk analysis. For the case study, the effectiveness of the same was demonstrated, since after its realization it was possible to increase the risk awareness of workers, technicians, and managers of the area taken as a case study.

Research on the Decompression Effects of Shaft Explosion-Proof Door at Different Lifting Heights

To study the decompression effects of shaft explosion-proof door at different lifting heights, this paper designed the gas explosion testing system. Based on the test results, this paper made a numeric analysis of the change regularities of the shock wave overpressure when the shaft explosion-proof door was lifted at different heights. Finally, this paper determined the proper lifting height of the shaft explosion-proof door and put forward the active decompression concept. The research showed that (1) the shock wave overpressure at the explosion-proof door decreased in a power exponential relationship as the lifting height increased. When the lifting height increased from 0 cm to 5 cm, the peak overpressure at the explosion-proof door decreased from 36.06 kPa to 22.47 kPa, dropping by 37.7%. When it was lifted at a height of 40 cm, the overpressure dropped to 11.20 kPa and the decompression reached 68.9%. (2) The overpressure at the ventilator decreased in a power exponential relationship as the lifting height increased. When the lifting height of the explosion-proof door increased from 0 cm to 5 cm, the decompression ratio reached the maximum 18.4%. After that, the decompression effect became worse and worse. (3) The explosion-proof door could depressurize and protect the ventilator at gas explosion but with limited effects. To protect the ventilator and the explosion-proof door to the maximum, it was suggested that the pressure sensor was set up somewhere in the mine where the gas explosion is likely to occur. In this way, the explosion was sensed in time and the explosion-proof door could be actively lifted for decompression. This paper was of great guiding significance in optimizing the design of the explosion-proof door equipment, reducing the loss of gas explosion accidents as well as carrying out the emergency rescue.

Explosions of Cylindrical Pressure Vessels Subjected to Fire: Probabilistic Prediction of a Number of Fragments

The aim of this study was to propose a procedure for a prediction of the number of fragments generated by fire induced explosions of cylindrical pressure vessels. The prediction is carried out in terms of probabilities of individual fragment numbers. The prevailing numbers of two to four fragments are considered. The fragment number probabilities are estimated by applying data on vessel fragmentations acquired in investigations of past explosion accidents. The pressure vessel explosions known as BLEVEs are considered. The Bayesian analysis is used for the estimation of the fragment number probabilities. This analysis is carried out on the basis of Poisson-gamma model. An approach to developing a gamma prior distribution for the average number of fragments per explosion accident is proposed. The assessment of the fragment number probabilities is carried out by propagating uncertainty related to the average number of fragments to uncertainty in the fragment number probabilities. The stochastic (Monte Carlo) simulation is used for this propagation. Findings of this study are viewed as a possibility to improve the assessment of risk posed by pressure vessel explosions.