Worst-case identification of gas dispersion for gas detector mapping using dispersion modeling

Meysami, H.; Ebadi, Taghi; Zohdirad, H.; Minepur, M.

doi:10.1016/j.jlp.2013.08.019

Cited by 18 publications

(9 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PHAST is a software that quantifies consequences, independently developed by DNV (Det Norske Veritas). It is also a software specially used for risk analysis and safety calculation in petrochemical and natural gas fields 12 . In this study, characteristics of the release, dispersion, and dilution of the hazardous substances, as well as effects of explosions and fires were calculated using a computer program “PHAST version 7.2.”…”

Section: Phast Simulationmentioning

confidence: 99%

Quantitative analysis of enclosed ground flare based on PHAST analysis

Kong²,

Wei

et al. 2020

Heat Trans

View full text Add to dashboard Cite

Despite the rapid development of the petrochemical industry, it is overly characterized by complication and integration. The requirements of safety technologies and facilities in the process are increasingly high. As one of the important safety facilities, the flare is indispensable in the petrochemical industry. In recent years, the ground flare has been further applied because of its advantages in low radiation and low pollution. However, as it has been introduced and used in China for a short time, the theoretical basis for its safety application is not strong. In view of this point, after the abnormal leakage, relevant data of the enclosed ground flare are obtained, so as to identify the diffusion and thermal radiation hazards. Meanwhile, quantitative analysis is conducted. The process hazard analysis software is used in the analysis and exploration by taking the enclosed ground flare used in chemical plants as an example. By establishing different accident models, the scopes of influence of fire explosion after the abnormal leakage of the enclosed ground flare are obtained. Data for quantitative analysis are also acquired. It will be an important reference for safely designing and applying the enclosed ground flare and formulating an emergency plan for safety production.

show abstract

Section: Phast Simulationmentioning

confidence: 99%

Quantitative analysis of enclosed ground flare based on PHAST analysis

Kong²,

Wei

et al. 2020

Heat Trans

View full text Add to dashboard Cite

show abstract

“…Among the various major chemical accidents, vessel leakage or rupture is one of the main root causes of catastrophic events and can lead to triggering hazardous material dispersion, fire and explosion [1]. The Fixborough disaster in England in 1974 [2], and a toxic gas release in Seveso, Italy, in 1978 [3] are representative examples of a material release from vessels. Furthermore, the huge explosion of ammonium nitrate in Beirut's port (August in 2020) killed at least 181 people [4].…”

Section: Introductionmentioning

confidence: 99%

The Mathematical Modeling and Simulation of a New Mitigation System Including a Remote Impoundment

2021

View full text Add to dashboard Cite

After the occurrence of a hydrogen fluoride leakage accident that triggered massive losses in Gumi, South Korea in 2012, the government and companies have been interested in installing mitigation systems to minimize the loss of a leakage accident. What lacks in previous researches studying mitigation systems is an evaluation of how much a mitigation system can reduce the impact of accidents. Therefore, modeling-based simulations of mitigation systems should be urgently developed to analysis of the performance of a mitigation system. This study aims to design a mitigation system to reduce the amount of materials leaked to the outside when a leakage accident of a storage tank occurs and determine its design specifications through the use of modeling. The basic concept is that when leakage occurs, leakage material in a dike is drained to a remote impoundment installed under the ground, while the material in the storage vessel is transferred to a reserve tank by a pump at the same time. To evaluate the efficacy of the proposed system, two case studies are tested. The simulation results indicate that the proposed mitigation system can contribute to the reduction in the dispersion area for the materials as well as a large reduction in the leakage material.

show abstract

“…B. Wang et al developed a prediction and evaluation program that integrated a methane detector, neural network and methane diffusion model and was different from the real-time prediction and evaluation system of harmful methane diffusion. Their experimental verification revealed the high reliability of this system and its high correlation with the original evaluation model [10][11][12][13]. Zhang et al [14] came up with a solution that combines artificial neural networks with fault tree analysis to improve the prediction of underground coal and methane outbursts.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Methane Sensors using Generalized Regression Neural Network

2016

View full text Add to dashboard Cite

Abstract-to identify the hang, collision and drift faults of methane sensors, this paper presents a fault diagnosis method for methane sensors using multi-sensor information fusion. A methane concentration monitoring approximation model with multi-sensor information fusion is established based on generalized regression neural network (GRNN). The output of the neural network is compared with the measured value of the sensor to be diagnosed to obtain the variation curve of the residual error signal. Through the analysis of the variation tendency of the residual error signal, the fault status of a methane sensor could be determined based on a reasonable threshold. Through simulation comparison is applied between the two models of GRNN and BP neural network; verify the GRNN model is much more precise in the approximation of methane concentrations. Fault diagnosis for methane sensors using generalized regression neural network is effective and more efficient.Index Terms-generalized regression neural network (GRNN), methane sensor, fault diagnosis, multi-sensor information fusion

show abstract

Worst-case identification of gas dispersion for gas detector mapping using dispersion modeling

Cited by 18 publications

References 3 publications

Quantitative analysis of enclosed ground flare based on PHAST analysis

Quantitative analysis of enclosed ground flare based on PHAST analysis

The Mathematical Modeling and Simulation of a New Mitigation System Including a Remote Impoundment

Fault Diagnosis for Methane Sensors using Generalized Regression Neural Network

Contact Info

Product

Resources

About