A probabilistic visual-flowcharting-based model for consequence assessment of fire and explosion events involving leaks of flammable gases

Khalil, Y.F.

doi:10.1016/j.jlp.2017.09.016

Cited by 17 publications

(10 citation statements)

References 26 publications

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“…This simulation test is used to validate whether the consequences can be predicted and evaluated on the basis of real-time data. There are five propane tanks with a volume of 3500 m 3 , five propane tanks with a volume of 3000 m 3 , three butane tanks with a volume of 10000 m 3 , one butane tank with a volume of 6500 m 3 and two horizontal tanks with a volume of 100 m 3 at the storage area of the enterprise. The natural conditions of the city in which the enterprise operates are as follows: annual mean wind speed equals 2.4 m/s, annual mean temperature equals 21.9 C and annual mean relative humidity equals 81%.…”

Section: Case Profilesmentioning

confidence: 99%

“…Adedigba et al [2] presented a new non-sequential barrier-based process accident model to predict the probability of an accident. Khalil [3] developed a probabilistic model to quantify occupational risks of fire and explosion events initiated by leaks that ignite within enclosed spaces. Bubbico and Mazzarotta [4] presented a simplified mathematical model to assess the consequences during the evolution of the accident.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

3D Dynamic Visualization Simulation System of Pool Fire Triggered by Major Hazard Installation

Chen¹,

Hu²,

Yan³

et al. 2018

JSW

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This paper proposes a method of three-dimensional (3D) dynamic visualization simulation of pool fire triggered by major hazard installation. The method describes the 3D dynamic characteristics of pool fire by establishing the 3D model which combines with the real-time data of various parameters including environment and position information of people. To improve the computational efficiency of the model and enhance the sensing cognition of accidents, a 3D simulation system of pool fire is developed by 3D visualization technology. And an application of the system is performed to simulate the real-time process and consequences of pool fire. The application results show that the 3D simulation method of pool fire is of real-time, lifelike and visual characteristics, which not only embodies the great practical application value of 3D visualization technology in the field of safety engineering, but also provides references for safety management, disaster prediction, risk assessment, emergency response and safety planning of petrochemical enterprises.

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Section: Case Profilesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

3D Dynamic Visualization Simulation System of Pool Fire Triggered by Major Hazard Installation

Chen¹,

Hu²,

Yan³

et al. 2018

JSW

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“…), but 70 to 98% of the NG is composed of methane [1], being a major contributor to the greenhouse effect [2]. In addition to the environmental impacts, the effects of natural gas leak accidents are commonly lethal to humans and capable of destroying oil and gas processing plants [3].…”

Section: Introductionmentioning

confidence: 99%

“…Natural gas leaks, besides contributing to global climate change, have catastrophic consequences in industrial facilities, especially when such leaks are followed by an explosion [3], causing significant loss of human lives and financial losses [5]. To mitigate events involving methane leaks and explosions, the options range from area classification to monitoring and leak detection of this gas in industrial processing plants.…”

Section: Introductionmentioning

confidence: 99%

Applying Convolutional Neural Networks to Detect Natural Gas Leaks in Wellhead Images

2020

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Detecting natural gas leaks is one of the most important measures in the oil industry for preventing accidents. The literature provides different techniques for detecting natural gas leaks. However, except for previous studies by the authors on this topic, there remains a gap in the literature on leak detection of natural gas using digital images, without the need for sensors or special cameras calibrated for the spectrum of methane molecules. These previous studies used image-processing techniques associated with a novelty filter classifier to detect the presence or absence of visible cloud of hydrocarbon vapors, that is, a natural gas plume in Closed Circuit Television (CCTV) frames installed in onshore wellheads. In this paper, we present a new method for detecting natural gas leaks in oil facilities that enhances the results obtained previously, along with the Gradient-weighted Class Activation Mapping Algorithm (Grad-CAM) to identify natural gas leaks. In this new method, convolutional neural networks (CNN) are applied to classify images (CCTV frames) as belonging to classes with or without natural gas leaks in onshore wellheads. Experimental results showed that the best performance model presented an accuracy of 99.78% and false negative rate of 0.00%.

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“…These systems are adopted throughout the world, however, more strictly followed at flammable industries (oil, gas, paint, paper, etc.) while considered being little lenient at household vicinities [7,8]. The increased population with constant high pressure on the economic backbone of numerous countries has altered the previous protection protocols [9][10][11].…”

Section: Brief Introductionmentioning

confidence: 99%

Introductory Chapter: Fire Prevention Strategies

Ghosal¹,

Zafar²

2020

Fire Safety and Management Awareness

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A probabilistic visual-flowcharting-based model for consequence assessment of fire and explosion events involving leaks of flammable gases

Cited by 17 publications

References 26 publications

3D Dynamic Visualization Simulation System of Pool Fire Triggered by Major Hazard Installation

3D Dynamic Visualization Simulation System of Pool Fire Triggered by Major Hazard Installation

Applying Convolutional Neural Networks to Detect Natural Gas Leaks in Wellhead Images

Introductory Chapter: Fire Prevention Strategies

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