An assessment of the likelihood of occurrence, and the damage potential of domino effect (chain of accidents) in a typical cluster of industries-Reference-Cited by-同舟云学术

An assessment of the likelihood of occurrence, and the damage potential of domino effect (chain of accidents) in a typical cluster of industries

Published:2001-07 Issue:4 Volume:14 Page:283-306
ISSN:0950-4230
Container-title:Journal of Loss Prevention in the Process Industries
language:en
Short-container-title:Journal of Loss Prevention in the Process Industries

Author:

Khan Faisal I,Abbasi S.A

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Management Science and Operations Research,Energy Engineering and Power Technology,Safety, Risk, Reliability and Quality,General Chemical Engineering,Food Science,Control and Systems Engineering

Reference13 articles.

1. Environmental pollution and its control;Abbasi,1999

2. Abbasi, S.A., & Khan, F.I. (1999). E/A and sustainability studies in Manali lndustrial complex: executive summary of final report. Centre for Pollution Control & Energy Technology, Pondicherry University, Pondicherry, pp 1–186.

3. Computer-aided environmental management;Abbasi,2000

4. Environmental impact of industries on suburban environments;Abbasi,1997

5. HOT TOPICS: everyday environmental concerns;Abbasi,1999

Cited by 136 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Probability analysis considering the temporal properties of fire-induced domino effects;Journal of Loss Prevention in the Process Industries;2024-12

2. Numerical study of failure modes of hazardous material tanks exposed to fire accidents in the process industry;Process Safety Progress;2024-09-03

3. Prediction and comparison of burning rate of n-heptane pool fire in open space based on BPNN and XGBoost;Process Safety and Environmental Protection;2024-09

4. Exploring human factors of major chemical accidents in China: Evidence from 160 accidents during 2011–2022;Journal of Loss Prevention in the Process Industries;2024-07

5. A neural network approach to predict the time-to-failure of atmospheric tanks exposed to external fire;Reliability Engineering & System Safety;2024-05