查看更多>>摘要：Baker Engineering and Risk Consultants, Inc. (BakerRisk?) has performed vented deflagration testing of congested enclosures over a range of configurations, congestion levels, and fuels. This paper provides a comparison of the measured flame jetting distances to predictions made applying standard methods commonly used to calculate the associated hazard zone. These methods include the National Fire Protection Association Standard on Explosion Protection by Deflagration Venting (NFPA 68 (National Fire Protection Association, 2018, )), the British Standard's Gas Explosion Venting Protective Systems (EN 14994 (British Standard EN 14994, 2007)), and a computational fluid dynamics (CFD) analysis. Nine test series were carried out using BakerRisk's Deflagration Load Generator (DLG) test rig. The DLG is a rectangular steel enclosure measuring 48-feet wide × 24-feet deep × 12-feet tall, yielding a total volume of 13,800 ft3 (391 m3), and is enclosed by three solid steel walls, a roof, and floor. The rig vents through one of the long walls (i.e., 48-foot × 12-foot). The venting face was sealed with a 6-mil (0.15 mm) thick plastic vapor barrier for these tests to allow for the formation of the desired fuel air-mixture throughout the rig. Both slightly hyper-stoichiometric propane and lean hydrogen mixtures have been tested in the DLG. Congestion was provided by an array of vertical cylinders. A range of congestion levels and fill fractions were tested. DLG testing was performed with and without vent panels present. Flame jetting distances from the venting face of the DLG were measured using high-speed video. Flame jetting distances were predicted using the Fireball Dimensions calculation from NFPA 68 and the Flame Effects calculation from EN 14994. Blind (i.e., pre-test) simulations were also performed using the FLACS CFD code (Gexcon, 2014). The flame jetting distance in the CFD simulation was determined as the horizontal distance from the DLG vent to the location where the gas temperature dropped below a specified value; the predicted distance for the fuel concentration to drop below half the lower flammability limit (LFL) was also evaluated to assess jetting distance.
查看更多>>摘要：Thermal runaway is still an important cause of accident in chemical industry. To evaluate the risk of such events, thermal risk assessment, which is a part of process safety, must be done. This assessment determines the safe operating conditions of a process by evaluating the thermal risk of an exothermic system. Nevertheless, based on thermal risk assessment, it is not possible to know which model inputs have the most influence on the thermal risk parameters. The knowledge of the most influential model inputs on thermal risk parameters is important to establish adequate safety barriers. Global sensitivity analysis was used to evaluate the influence of model inputs and their interaction on thermal risk parameters. It was performed on the exothermic system: epoxidation of cottonseed oil by performic acid in semibatch mode under isoperibolic conditions. The maximum reaction temperature and the time to reach this maximum reaction temperature were chosen as thermal risk parameters. We have also studied the influence of model inputs on the temperature rise. In the operating conditions of this study, it appeared that two parameters have the most influence on maximum reaction temperature and the temperature rise: the initial concentration of hydrogen peroxide and the jacket temperature, and one parameter for the time to reach this temperature: the jacket temperature.
查看更多>>摘要：Gas pressure reduction stations are essential in energy distribution networks because even a minor failure of these systems causes disruptive consumer problems. This study aims to introduce and implement a new knowledge-based platform that uses the synthesized expert's opinions to improve gas pressure control facilities. Given the record of failure of gas transmission system components and the data's uncertain nature, a new fuzzy expert system is developed that takes advantage of the object-oriented programming paradigm to analyze failure modes and conditions. The artificial intelligent model is designed in C# programming language, and a user-friendly interface is developed for ease of implementation. The knowledge-based model's validity has been tested by implementing real-world case studies adapted from the Iranian gas industry. Implementing the designed expert system shows that it can minimize the probability of a breakdown and improve safety conditions.
查看更多>>摘要：Quantitative risk analysis (QRA) is a systematic methodology to identify, analyze, and calculate risks of an operation or installation of hazardous facilities. One of the first steps of a QRA is the qualitative categorization of the frequency and severity of potential accidents. This task is performed by a group of experts and can be very time and resource-consuming for large-size plants such as oil refineries, which presents several scenarios to look into. This paper presents machine learning (ML) based models to support analysts through the initial stages of a QRA. The proposed approach uses ML classifiers to extract useful knowledge and information from past risk analyses, and thus provide qualitative estimates of consequence and frequency of the accidental scenarios. The approach is demonstrated through a case study concerning an atmospheric distillation unit of an actual oil refinery. The results indicate that the approach is a very promising tool for supporting analysts in the initial stages of QRAs. In addition to reduce time and resources, it can also aid to ensure QRA traceability and reduce variability.
查看更多>>摘要：Blast hazards represent a serious threat to industrial facilities. Past explosion incidents highlight the severe consequences of such events. A probabilistic approach can help industries and designers mitigate the consequences of blast loading by better organizing industrial plants. In this paper, we propose a physics-based probabilistic demand model and formulate the reliability problem for industrial steel tanks under blast loading. Starting from a deterministic Single-Degree-of-Freedom (SDOF) model based on Donnell shallow-shell theory, we develop a correction term that improves the model accuracy due to the simplified representation of the SDOF model. We use Bayesian inference to estimate the unknown model parameters in the correction term and model error, combining predictions from the SDOF model with experimental data and any prior information. To illustrate, we estimate the reliability of an example cylindrical steel tanks subject to blast loading considering three damage levels. The reliability analysis yields a set of fragility curves that represent the conditional probability of the bending failure of the tank given a scaled distance, as the load intensity measure. Then, as an example, we use the developed fragility functions to estimate the reliability of a chemical industrial facility considering different explosion scenarios.
查看更多>>摘要：The development of an integrated network for the management of carbon dioxide requires knowledge and optimization of all Carbon Capture Utilization and Storage (CCUS) aspects, including pipeline transport. Safety is one of the aspects that should be addressed prior CCUS facilities come in operation; the risk for people should be assessed to ensure it is below an acceptable level. In some cases, a quantitative risk assessment (QRA) is required by the approval authority. Normally the risk assessment is based on the use of statistical/historical data. However, for CO2 handling systems the operating experience is limited compared to hydrocarbon transporting systems and, for this reason, hydrocarbon pipeline statistics are normally used as a proxy. The only database that contains records on CO2 pipelines is the PHMSA since in the U.S. several CO2 pipelines have been constructed since the 1970's, essentially for Enhanced Oil Recovery operations. There is limited statistical data available compared to the hydrocarbon pipelines experience and therefore care should be taken when undertaking the frequency analysis. In this work an analysis of incidents data related to the onshore CO2 pipelines in the U.S. between 1985 and 2021 reported by the Pipeline Hazardous Material Safety Administration (PHMSA) of the U.S. Department of Transportation is presented. The aim of the study is to analyze the records contained in the PHMSA database to provide an estimate of a specific CO2 pipeline failure rate to be used in quantitative risk assessments. Concerns and limitations of the data have been also discussed.
查看更多>>摘要：One of the challenges of the multi-hazard coupling risk assessment is synergistic effects. This research performed experiments and numerical simulations, aiming to analyse the synergistic effects on the physical effects of explosions. The direct influences of atmospheric temperature and density on the propagation of explosion shock waves were explained and quantified through function fittings. Shock wave velocity will increase with the increase of atmospheric temperature and the decrease of atmospheric density. On the other hand, shock wave overpressure will decrease with the increase of atmospheric temperature, but not be significantly influenced by the density. The indirect synergistic effects on explosion physical effects were also explained through the analysis of the phenomena in the experiments and simulations. Temperature and density gradients could cause inhomogeneous velocity distribution and change the shapes of the shock wave fronts, thereby influencing the reflection and changing the shock wave overpressure. Based on the experimental and simulative results, the direct and indirect influences of the synergistic effects on consequence analysis and risk assessment were analysed in explosion-toxic-fire coupling accident scenarios. This research provides a multi-hazard idea for synergistic effect research, and can also be instructive for the risk assessment and safety management in chemical industries.
NSTL
Elsevier