scholarly journals Application of an Enhanced Version of Recursive Operability Analysis for Combustible Dusts Risk Assessment

2020 ◽  
Vol 17 (9) ◽  
pp. 3078
Author(s):  
Marco Barozzi ◽  
Sabrina Copelli ◽  
Martina Silvia Scotton ◽  
Vincenzo Torretta
Keyword(s):  
Risk Assessment ◽  
Corn Starch ◽  
Logical Structure ◽  
Critical Issue ◽  
Sequence Diagrams ◽  
Chemical Safety ◽  
Fault Trees ◽  
Sugar Refinery ◽  
Dust Explosions ◽  
Application Fields

Organic dust explosions were and are still today a critical issue in the food, pharmaceutical, and fine chemical industry. Materials such as flour, corn starch, sugar and APIs represent a cause of severe accidents. In this framework, we investigated a modified version of Recursive Operability Analysis−Incidental Sequence Diagrams (ROA–ISD), called ROA Plus−ISD, specifically tailored to describe industrial processes involving organic combustible dusts. Compared to more classical techniques such as Hazard and Operability (HazOp), ROA−ISD allows for a direct generation of fault trees, providing a useful tool to connect Qualitative with Quantitative Risk Analysis (QRA). ROA Plus−ISD is very similar to ROA−Cause Consequence Diagrams (CCD), which has already proven to be an effective tool to perform both risk assessment on existing plants and reconstructing already occurred accidents, given its logical structure and width of the application fields. In this work, we modified specific parts of the standard ROA−CCD method: (1) the Failure Mode and Operability Analysis (FMEA) database has been structured in order to retrieve the well-known explosion pentagon (for dusts) and all the instruments, devices, apparatuses and controllers typical of industries which process organic dusts; (2) a new comprehensive list of process variables has been compiled. In this way, it is possible to tailor the information required for the generation of the fault trees concerning top events involving mainly dust explosions and fires. This method has been implemented in order to reconstruct the dynamics of the February 2008 Imperial Sugar refinery plant accident (Port Wentworth, GA, USA). Results demonstrated the applicability of the enhanced method by highlighting the criticalities of the process already showed by a previously detailed reconstruction performed by the Chemical Safety Board.

scholarly journals On the Use of the Hybrid Causal Logic Methodology in Ship Collision Risk Assessment

2020 ◽  
Vol 8 (7) ◽  
pp. 485 ◽  
Author(s):  
Tengfei Wang ◽  
Qing Wu ◽  
Mihai A. Diaconeasa ◽  
Xinping Yan ◽  
Ali Mosleh
Keyword(s):  
Risk Assessment ◽  
Bayesian Networks ◽  
Human Behavior ◽  
Human Error ◽  
Risk Identification ◽  
System Level ◽  
Sequence Diagrams ◽  
Fault Trees ◽  
Ship Collision ◽  
Causal Logic

A ship collision accident is one of the most dangerous and common types of maritime accidents. Traditional probabilistic risk assessment (PRA) of ship collision accidents is a methodology that can be adopted to ensure maritime safety. Nevertheless, a need for better approaches to model human behavior, such as risk identification, communication, and decision-making, has been identified. Such advanced PRA methods require a more explicit way of taking human factors into consideration than the traditional risk assessment methods. Hybrid causal logic (HCL) is an advanced PRA method due to its unique three-level framework that includes event sequence diagrams, fault trees, and Bayesian networks, which makes it suitable for modeling human behavior that is important to ship collision accidents. This paper discusses the applicability of the HCL methodology for the ship collision accident. Firstly, the event sequences of typical ship collision accidents are summarized based on the study of 50 accident investigation reports. Then, fault trees for mechanical failure events and the Bayesian networks for human error events are constructed to analyze the events in a structured way at a more detailed level. Finally, the three main end-state types of ship collision avoidance scenario have been quantified. The result of the probability of a ship collision accident is verified by estimating the annual frequency of collision accidents in the Singapore Strait. Comparing with the historical data, the estimation results are quite near to the real case. By taking advantage of the HCL methodology, the modeling of ship collision scenarios can be carried out at a deep logical level. At the same time, it is possible to combine a detailed analysis of various primary events with a comprehensive analysis at the system level.

scholarly journals Generalized Read-Across prediction using genra-py

2021 ◽  
Author(s):  
Imran Shah ◽  
Tia Tate ◽  
Grace Patlewicz
Keyword(s):  
Risk Assessment ◽  
Human Health Risk ◽  
Chemical Properties ◽  
Predictive Performance ◽  
Hazard Identification ◽  
Data Driven ◽  
Chemical Safety ◽  
Physico Chemical ◽  
Data Driven Approach ◽  
Assessment Problems

Abstract Motivation Generalized Read-Across (GenRA) is a data-driven approach to estimate physico-chemical, biological or eco-toxicological properties of chemicals by inference from analogues. GenRA attempts to mimic a human expert’s manual read-across reasoning for filling data gaps about new chemicals from known chemicals with an interpretable and automated approach based on nearest-neighbors. A key objective of GenRA is to systematically explore different choices of input data selection and neighborhood definition to objectively evaluate predictive performance of automated read-across estimates of chemical properties. Results We have implemented genra-py as a python package that can be freely used for chemical safety analysis and risk assessment applications. Automated read-across prediction in genra-py conforms to the scikit-learn machine learning library's estimator design pattern, making it easy to use and integrate in computational pipelines. We demonstrate the data-driven application of genra-py to address two key human health risk assessment problems namely: hazard identification and point of departure estimation. Availability and implementation The package is available from github.com/i-shah/genra-py.

Effects of particle size on flame structures through corn starch dust explosions

2017 ◽  
Vol 50 ◽  
pp. 7-14 ◽  
Author(s):  
Hongming Zhang ◽  
Xianfeng Chen ◽  
Ying Zhang ◽  
Yi Niu ◽  
Bihe Yuan ◽  
...  
Keyword(s):  
Particle Size ◽  
Corn Starch ◽  
Dust Explosions ◽  
Flame Structures

Chemical Safety and Risk Assessment

2011 ◽  
pp. 19-35
Author(s):  
Kai M. Savolainen ◽  
Pentti Kalliokoski
Keyword(s):  
Risk Assessment ◽  
Chemical Safety

scholarly journals A risk and security assessment of VANET availability using attack tree concept

2020 ◽  
Vol 10 (6) ◽  
pp. 6039
Author(s):  
Meriem Houmer ◽  
Moulay Lahcen Hasnaoui
Keyword(s):  
Risk Assessment ◽  
Ad Hoc ◽  
Critical Issue ◽  
Security And Privacy ◽  
Security Assessment ◽  
Total Risk ◽  
Tree Model ◽  
Protection Measures ◽  
Attack Tree ◽  
Multi Attribute Utility Theory

The challenging nature of insecure wireless channels and the open-access environment make the protection of vehicular ad hoc network (VANET) a particularly critical issue. Researchers and interested authorities have therefore paid more attention to this issue. Therefore, robust approaches to protect this network's security and privacy against adversaries and attacks need to be improved, trying to achieve an adequate level, to secure the confidential information of drivers and passengers. Accordingly, to improve the security of VANET, it is necessary to carry out a risk assessment, in order to evaluate the risk that faces this network. This paper focuses on the security threats in vehicular network especially on the availability of this network. We propose a novel risk assessment approach to evaluate the risk of the attack that the attacker can lead against the availability of VANET. We adopt a tree structure called attack tree to model the attacker's potential attack strategies. Based on this attack tree model, we can estimate the degree that a certain threat can lead to the VANET and identify possible attack sequences that an attacker may launch against the availability of this network. Then we utilize the multi-attribute utility theory to calculate the system’s total risk value also the probabilities of each attack sequence. The analysis results can provide support for decision-makers to make corresponding protection measures against the attack on the availability of this network.

scholarly journals Combining semi-quantitative risk assessment, composite indicator and fuzzy logic for evaluation of hazardous chemical accidents

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Huyen Thi Thu Do ◽  
Tram Thi Bich Ly ◽  
Tho Tien Do
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Safety Management ◽  
Triangular Fuzzy Number ◽  
Composite Indicator ◽  
Quantitative Risk Assessment ◽  
Chemical Safety ◽  
Chemical Accidents ◽  
Hazardous Chemical ◽  
Industrial Zones

Abstract In this study, a combination of semi-quantitative risk assessment, composite indicator and fuzzy logic has been developed to identify industrial establishments and processes that represent potential environmental accidents associated with hazardous chemicals. The proposed method takes into consideration the root causes of risk probability of hazardous chemical accidents (HCAs), such as unsafe onsite storing and usage, inadequate operation training, poor safety management and analysis, equipment failure, and factors affected by the potential consequences of the accidents, including human health, water resources, and building and construction. These issues have been aggregated in a system of criteria and sub-criteria, demonstrated by a list of non-overlapping and exhaustive categorical terms. Semi-quantitative risk assessment is then applied to develop a framework for screening industrial establishments that exhibit potential HCAs. Fuzzy set theory with triangular fuzzy number deals with the uncertainty associated with the data input and reduces the influence of subjectivity and vagueness to the final results. The proposed method was tested among 77 industrial establishments located within the industrial zones of Ho Chi Minh City, Vietnam. Eighteen establishments were categorized as high HCA risk, 36 establishments were categorized as medium HCA risk, and 23 ones were of low HCA risk. The results are compatible with the practical chemical safety situation of the establishments and are consistent with expert evaluation.

A Framework for Development of Risk-Informed Autonomous Adaptive Cyber Controllers

2019 ◽  
Vol 19 (4) ◽  
Author(s):  
Arun Veeramany ◽  
William J. Hutton ◽  
Siddharth Sridhar ◽  
Sri Nikhil Gupta Gourisetti ◽  
Garill A. Coles ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Control System ◽  
Business Processes ◽  
Assessment Methodology ◽  
Attack Graphs ◽  
Adaptive Controllers ◽  
Fault Trees ◽  
Wide Applicability ◽  
Event Trees ◽  
Risk Assessment Methodology

This article details a framework and methodology to risk-inform the decisions of an unsupervised cyber controller. A risk assessment methodology within this framework uses a combination of fault trees, event trees, and attack graphs to trace and map cyber elements with business processes. The methodology attempts to prevent and mitigate cyberattacks by using adaptive controllers that proactively reconfigure a network based on actionable risk estimates. The estimates are based on vulnerabilities and potential business consequences. A generic enterprise-control system is used to demonstrate the wide applicability of the methodology. In addition, data needs, implementation, and potential pitfalls are discussed.

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