scholarly journals Stochastic modeling and analysis of vapor cloud explosions domino effects in chemical plants

2019 ◽  
Vol 25 (1) ◽  
Author(s):  
Diego Sierra ◽  
Leonardo Montecchi ◽  
Ivan Mura
Keyword(s):  
Stochastic Modeling ◽  
Operating Conditions ◽  
Chemical Plants ◽  
Domino Effect ◽  
Modeling And Analysis ◽  
Modeling Methodology ◽  
Vapor Cloud ◽  
Maintenance Activities ◽  
Support Decision Making

Abstract Because of the substances they process and the conditions of operation, chemical plants are systems prone to the occurrence of undesirable and potentially dangerous events. Major accidents may occur when a triggering event produces a cascading accident that propagates to other units, a scenario known as domino effect. Assessing the probability of experiencing a domino effect and estimating the magnitude of its consequences is a complex task, as it depends on the nature of the substances being processed, the operating conditions, the failure proneness of equipment units, the execution of preventive maintenance activities, and of course the plant layout. In this work, we propose a stochastic modeling methodology to perform a probabilistic analysis of the likelihood of domino effects caused by propagating vapor cloud explosions. Our methodology combines mathematical models of the physical characteristics of the explosion, with stochastic state-based models representing the actual propagation among equipment units and the effect of maintenance activities. Altogether, the models allow predicting the likelihood of major events occurrence and the associated costs. A case study is analyzed, where various layouts of atmospheric gasoline tanks are assessed in terms of the predicted consequences of domino effects occurrence. The results of the analyses show that our approach can provide precious insights to support decision-making for safety and cost management.

scholarly journals How to Determine the Dangerous Potential of Accidents to Domino Effect Detonation in a Hydrocarbon Processing Area?

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Julio Ariel Dueñas Santana ◽  
Yanelys Cuba Arana ◽  
Mary Carla Barrera González ◽  
Jesús Luis Orozco
Keyword(s):  
Domino Effect ◽  
Effect Analysis ◽  
Industrial Facilities ◽  
Cascade Effect ◽  
Vapor Cloud ◽  
Fire And Explosion ◽  
Hydrocarbon Storage ◽  
Explosion Accidents ◽  
New Procedures

The crude oil industry has been developed in recent decades due to the uses of this product, as well as its derivatives. One of the worst consequences phenomena that can occur in the process industry is the called domino effect. The domino effect or cascade effect occurs when an initiating event, such as a pool of fire or a vapor cloud explosion, causes a new number of accidents. Moreover, due to the importance of avoiding this phenomenon, the European Commission considers the domino effect analysis as mandatory for industrial facilities. There are methodologies in the specialized literature focused on quantifying the existing risks in the storage and processing of hydrocarbons. However, there is a tendency to develop new procedures that increase the risk perception of these accidents. In addition, it is necessary to develop a method that allows visualizing clearly and concisely the dangerous potential of fire and explosion accidents for the occurrence of the domino effect. Precisely, this research aims to predict the dangerous potential of fire and explosion accidents for the occurrence of the domino effect. For this purpose, a methodology consisting of three fundamental stages is developed. Finally, hydrocarbon storage and processing area is selected to apply the proposed methodology. Overall, the development of graphs that summarize information and show the dangerous potential regarding the escalation of fire and explosion accidents is vital in risk analysis. For the case study, the effectiveness of the same was demonstrated, since after its realization it was possible to increase the risk awareness of workers, technicians, and managers of the area taken as a case study.

Stochastic Modeling of Damage Physics for Mechanical Component Prognostics Using Condition Indicators

2009 ◽  
Author(s):  
David He ◽  
Ruoyu Li ◽  
Eric Bechhoefer
Keyword(s):  
Stochastic Modeling ◽  
Health Monitoring ◽  
Condition Indicators ◽  
Current Health ◽  
Modeling Methodology ◽  
Mechanical Component ◽  
Monitoring Applications ◽  
Deterioration Process ◽  
Physical Laws

The health of a mechanical component deteriorates over time and its service life is randomly distributed and can be modeled by a stochastic deterioration process. For most of the mechanical components, the deterioration process follows a certain physical laws and their mean life to failure can be determined approximately by these laws. However, it is not easy to apply these laws for mechanical component prognostics in current health monitoring applications. In this paper, a stochastic modeling methodology for mechanical component prognostics with condition indicators used in current health monitoring applications is presented. The effectiveness of the methodology is demonstrated with a real shaft fatigue life prediction case study.

Assessing Pruning Knowledge towards Effective Tree Maintenance: A case study of four Local Authorities in Malaysia

2020 ◽  
Vol 5 (13) ◽  
pp. 223
Author(s):  
Norainiratna Badrulhisham ◽  
Noriah Othman
Keyword(s):  
Publishing House ◽  
Knowledge Level ◽  
Local Authorities ◽  
The Public ◽  
Risk Of Injury ◽  
International Publishing ◽  
Maintenance Activities ◽  
Tree Pruning ◽  
Open Access Article

Pruning is one of the most crucial tree maintenance activities which give an impact on the tree's health and structure. Besides, improper pruning will contribute to the risk of injury to property and the public. This study aims to assess pruning knowledge among four Local authorities in Malaysia. Results found that 69.3 percent of tree pruning workers have a Good pruning knowledge level. However, Topping, pruning types and pruning cut dimension shows the lowest mean percentage of the correct answer. The findings also show that there is a significant positive relationship between pruning knowledge and education level and frequency attending pruning courses.Keywords: Tree pruning; knowledge; sustainable practices; urban treeseISSN: 2398-4287 © 2020. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.DOI: https://doi.org/10.21834/e-bpj.v5i13.2054 

scholarly journals STANDARDISING MAINTENANCE JOBS TO IMPROVE GROUPING DECISION MAKING

2021 ◽  
Vol 1 ◽  
pp. 2701-2710
Author(s):  
Julie Krogh Agergaard ◽  
Kristoffer Vandrup Sigsgaard ◽  
Niels Henrik Mortensen ◽  
Jingrui Ge ◽  
Kasper Barslund Hansen ◽  
...  
Keyword(s):  
Decision Making ◽  
Tacit Knowledge ◽  
True Variance ◽  
Complex Decision ◽  
Maintenance Decision ◽  
Maintenance Activities

AbstractMaintenance decision making is an important part of managing the costs, effectiveness and risk of maintenance. One way to improve maintenance efficiency without affecting the risk picture is to group maintenance jobs. Literature includes many examples of algorithms for the grouping of maintenance activities. However, the data is not always available, and with increasing plant complexity comes increasingly complex decision requirements, making it difficult to leave the decision making up to algorithms.This paper suggests a framework for the standardisation of maintenance data as an aid for maintenance experts to make decisions on maintenance grouping. The standardisation improves the basis for decisions, giving an overview of true variance within the available data. The goal of the framework is to make it simpler to apply tacit knowledge and make right decisions.Applying the framework in a case study showed that groups can be identified and reconfigured and potential savings easily estimated when maintenance jobs are standardised. The case study enabled an estimated 7%-9% saved on the number of hours spent on the investigated jobs.

A Practical Approach to Expedite a Pore to Process Simulation Model for a Rich Gas Condensate Reservoir – Case Study

2021 ◽  
Author(s):  
Mohamed Ibrahim Mohamed ◽  
Ahmed Mahmoud El-Menoufi ◽  
Eman Abed Ezz El-Regal ◽  
Ahmed Mohamed Ali ◽  
Khaled Mohamed Mansour ◽  
...  
Keyword(s):  
Simulation Model ◽  
Production Optimization ◽  
Operating Conditions ◽  
Gas Condensate ◽  
Western Desert ◽  
Design Parameters ◽  
Compositional Approach ◽  
Process Plant ◽  
Black Oil

Abstract Field development planning of gas condensate fields using numerical simulation has many aspects to consider that may lead to a significant impact on production optimization. An important aspect is to account for the effects of network constraints and process plant operating conditions through an integrated asset model. This model should honor proper representation of the fluid within the reservoir, through the wells and up to the network and facility. Obaiyed is one of the biggest onshore gas field in Egypt, it is a highly heterogeneous gas condensate field located in the western desert of Egypt with more than 100 wells. Three initial condensate gas ratios are existing based on early PVT samples and production testing. The initial CGRs as follows;160, 115 and 42 STB/MMSCF. With continuous pressure depletion, the produced hydrocarbon composition stream changes, causing a deviation between the design parameters and the operating parameters of the equipment within the process plant, resulting in a decrease in the recovery of liquid condensate. Therefore, the facility engineers demand a dynamic update of a detailed composition stream to optimize the system and achieve greater economic value. The best way to obtain this compositional stream is by using a fully compositional integrated asset model. Utilizing a fully compositional model in Obaiyed is challenging, computationally expensive, and impractical, especially during the history match of the reservoir numerical model. In this paper, a case study for Obaiyed field is presented in which we used an alternative integrated asset modeling approach comprising a modified black-oil (MBO) that results in significant timesaving in the full-field reservoir simulation model. We then used a proper de-lumping scheme to convert the modified black oil tables into as many components as required by the surface network and process plant facility. The results of proposed approach are compared with a fully compositional approach for validity check. The results clearly identified the system bottlenecks. The model can be used to propose the best tie-in location of future wells in addition to providing first-pass flow assurance indications throughout the field's life and under different network configurations. The model enabled the facility engineers to keep the conditions of the surface facility within the optimized operating envelope throughout the field's lifetime.

Safety Assessment of Long Term Serviced Pressure Vessels: A Case Study of Typical Refining and Chemical Plants in China

2021 ◽  
Author(s):  
Zhiyuan Han ◽  
Guoshan Xie ◽  
Haiyi Jiang ◽  
Xiaowei Li
Keyword(s):  
Pressure Vessel ◽  
Safety Assessment ◽  
Failure Modes ◽  
Pressure Vessels ◽  
Time Dependent ◽  
Chemical Plants ◽  
Safety Specification ◽  
Detailed Assessment

Abstract The safety and risk of the long term serviced pressure vessels, especially which serviced more than 20 years, has become one of the most concerned issues in refining and chemical industry and government safety supervision in China. According to the Chinese pressure vessel safety specification TSG 21-2016 “Supervision Regulation on Safety Technology for Stationary Pressure Vessel”, if necessary, safety assessment should be performed for the pressure vessel which reaches the design service life or exceeds 20 years without a definite design life. However, the safety and risk conditions of most pressure vessels have little changes after long term serviced because their failure modes are time-independent. Thus the key problem is to identify the devices with the time-dependent failure modes and assess them based on the failure modes. This study provided a case study on 16 typical refining and chemical plants including 1870 pressure vessels serviced more than 20 years. The quantitative risk and damage mechanisms were calculated based on API 581, the time-dependent and time-independent failure modes were identified, and the typical pressure vessels were assessed based on API 579. Taking the high pressure hydrogenation plant as an example, this study gave the detailed assessment results and conclusions. The results and suggestions in this study are essential for the safety supervision and extending life of long term serviced pressure vessels in China.

Multi-element pollution in soil, ground and surface water from abandoned chromate chemical plants: a case study in Hangzhou, China

2015 ◽  
Vol 74 (4) ◽  
pp. 2861-2870 ◽  
Author(s):  
Lina Zou ◽  
Shaoxian Wang ◽  
Lei Liu ◽  
Muhammad Z. Hashmi ◽  
Xianjin Tang ◽  
...  
Keyword(s):  
Surface Water ◽  
Chemical Plants

Hierarchical Bayesian Parameter Estimation for Modeling and Analysis of User Affective Influence

2013 ◽  
Author(s):  
Feng Zhou ◽  
Jianxin (Roger) Jiao
Keyword(s):  
Decision Making ◽  
Interior Design ◽  
Computational Models ◽  
Human Perception ◽  
Model Parameters ◽  
Hierarchical Bayesian ◽  
Affective States ◽  
Modeling And Analysis ◽  
Human Decision

Traditional user experience (UX) models are mostly qualitative in terms of its measurement and structure. This paper proposes a quantitative UX model based on cumulative prospect theory. It takes a decision making perspective between two alternative design profiles. However, affective elements are well-known to have influence on human decision making, the prevailing computational models for analyzing and simulating human perception on UX are mainly cognition-based models. In order to incorporate both affective and cognitive factors in the decision making process, we manipulate the parameters involved in the cumulative prospect model to show the affective influence. Specifically, three different affective states are induced to shape the model parameters. A hierarchical Bayesian model with a technique called Markov chain Monte Carlo is used to estimate the parameters. A case study of aircraft cabin interior design is illustrated to show the proposed methodology.

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