Dynamic Response of Vertical Tank Impacted by Blast Fragments in Chemical Industrial Parks

PresentationThe adjacent vessels may be impacted and/or destroyed by blast fragments in chemical industrial parks or plants, which could lead to the domino effects. Based on the analysis of common parameters of blast fragments including the shape, quantity, mass, and impact velocity, the numerical model of vertical storage tanks impacted by blast fragments was developed with LS- DYNA. Considering deformation of the fragment itself, the law of the dynamic response of vertical tank was described quantitatively. The resultsshowed that there were 3 collisions during the impact process, the maximum plastic deformation occurred at the impact center, the plastic strain was mainly distributed in the range from the impact center to the tank bottom, and there were 4 plastic hinge lines in the deformation region. There was linear relationship between the residual displacement of impact center and the impact velocity of the fragment, and the tank wall had entered plastic deformation stage. With the horizontal impact angle in the range from 15° to 30°, the plastic deformation energy of the tank increased with the horizontal impact angle evidently; with the horizontal impact angle in the range from 30° to 35°, the impact mode of the fragment was changed from penetrating the tank wall to sliding along the tank wall; with the horizontal impact angle in the range from 35° to 60°, the deformation energy of the tank decreased linearly with horizontal impact angle, and the influence of vertical impact angle on the deformation energy of the tank was greatly reduced

Risk assessment of fire accidents in chemical and hydrocarbon processing industry

Fire disasters are among the most dangerous accidents in the chemical and hydrocarbon processing industry. Fires have been the source of major accidents such as the Piper Alpha disaster (1976), the BP Texas City disaster (2005), the Buncefield oil depot fire (2005), Puerto Rico’s fire accident (2009), and the Jaipur fire accident (2009). The catastrophic impact of fire accidents necessitates a detailed understanding of the mechanisms of their occurrence and evolution in a complex engineering system. Detailed understanding will help develop fire prevention and control strategies. This thesis aims to provide a detailed understanding of fire risk in the hydrocarbon production and processing industry. In order to realize this objective, the work presented in the thesis includes three parts: i) Developing a procedure to study potential fire accident scenarios in an offshore facility with different ignition source locations. This procedure helps to design safety measures. The effectiveness of safety measures is verified using a computational fluid dynamics (CFD) code. This work emphasizes that an FLNG layout must be considered with the utmost care since it is the most effective measure in limiting a potential LNG release and subsequent dispersion effect, and directly influences the fire dynamics and thus limits the potential damage. ii) An integrated probabilistic model for fire accident analysis considering the time-dependent nature of the fire is developed. The developed model captures the dynamics of fire evolution using three distinct techniques Bayesian networks, Petri Nets, and a CFD model. The Bayesian network captures the logical dependence of fire causation factors. The Petri Net captures the time-dependent evolution of a fire scenario. The CFD model captures the dimension and impact of the fire accident scenario. The results in this work show that a time-dependent probability analysis model is necessary for fire accidents. iii) Whether fire alone can cause a domino effect is demystified in the last work. A solid-flame model is used in a CFD framework to calculate the escalation vector for a domino effect; escalation probability is assessed using a probit model. The results demonstrate that a pool fire alone sometimes may not cause a domino effect in the current industry. It is other factors, such as explosion and hydrocarbon leakage, work together with a pool fire to escalate into a domino event, for example, the results shown in the case study of the Jaipur fire accident

Dynamic risk assessment of process facilities using advanced probabilistic approaches

A process accident can escalate into a chain of accidents, given the degree of congestion and complex arrangement of process equipment and pipelines. To prevent a chain of accidents, (called the domino effect), detailed assessments of risk and appropriate safety measures are required. The present study investigates available techniques and develops an integrated method to analyze evolving process accident scenarios, including the domino effect. The work presented here comprises two main contributions: a) a predictive model for process accident analysis using imprecise and incomplete information, and b) a predictive model to assess the risk profile of domino effect occurrence. A brief description of each is presented below. In recent years the Bayesian network (BN) has been used to model accident causation and its evolution. Though widely used, conventional BN suffers from two major uncertainties, data and model uncertainties. The former deals with the used of evidence theory while the latter uses canonical probabilistic models. High interdependencies of chemical infrastructure makes it prone to the domino effect. This demands an advanced approach to monitor and manage the risk posed by the domino effect is much needed. Given the dynamic nature of the domino effect, the monitoring and modelling methods need to be continuous time-dependent. A Generalized Stochastic Petrinet (GSPN) framework was chosen to model the domino effect. It enables modelling of an accident propagation pattern as the domino effect. It also enables probability analysis to estimate risk profile, which is of vital importance to design effective safety measures

QRA with respect to domino effects and property damage

In 1996 the European Union adopted the Seveso II Directive. The Directive stated actions to be taken in the process industry in order to prevent and limit the impact of serious chemical accidents. In the Directive it is clearly stated that domino effects shall be considered, but the level of detail required is not specified. Due to that fact and the high degree of complexity linked to domino effects, these aspects are mostly dealt with in a qualitative manner. Such approach leads to subjective assessments and is highly dependent on simplified assumptions, leading to results that may be questionable. Thus, it would be beneficial to develop a method that incorporates the risk of domino effects in a quantitative risk analysis (QRA), which has been the aim of this thesis. The method was developed based on a literature review of existing research. Focus was on integrating domino effects as a natural part of a QRA without compromising the timeframe associated to a QRA. The developed method has been applied in a case study of an oil refinery in order to evaluate how well it is applicable in practise. During the case study, the method has proven to enable the risk of property damage with regard to domino effects to be quantitatively analysed. The results from the case study, evidence the importance of taking domino effects into consideration in QRAs, as the risk may be underestimated if not

An Integrated Approach for Reliability Evaluation of Electric Power Systems Considering Natural Gas Network Reliability

With the rapid increase of demand for electric power and the growing complexity of the electric system, the reliable operation of electric systems is facing new challenges. Meanwhile, natural gas has been widely used in transportation, electricity generation, and heating. In addition, gas-fired turbines play a growing vital role in the generation of electricity. However, all the facilities in a natural gas network are subject to failures. The operation of gas-fired turbines will be affected by the status of natural gas network, and the insufficient supply of natural gas may cause the output of gas turbine units to reduce to zero. This power decrease may further influence the operation of power systems. Therefore, it is quite urgent to quantify the influence of natural gas networks on the power system reliability. A deep understanding of the operation of natural gas network is needed to quantify the impact that natural gas networks will bring to the power system reliability. The main facilities in a natural gas network are natural gas pipelines, compressor stations and natural gas sources. Additionally, the mathematical failure models have been developed for these facilities to build a reliability analysis framework for the gas network. The mass flow of natural gas at different failure conditions is analyzed by the maximum flow algorithm. Case studies are conducted on a modified Europe Belgium natural gas network to analyze the influences of different failures on the maximum flow of natural gas. The main problem discussed in this thesis is related to how the natural gas network operation status influences the reliability of power system. The coupling unit is the gas-fired turbine between and electric and gas infrastructures, while the simplified gas-fired turbine model used in this work shows a linear relation among the power generation and the mass flow of natural gas. In this thesis, reliability evaluation is performed based on the hierarchical level II which contains the generation system and the transmission system. The optimal power flow analysis has been conducted for the reliability evaluation. Based on the results of power flow, the status of load shedding can be obtained in a power system. Then, system reliability states can be determined. Failure statuses of both the natural gas network and electric system are simulated by Monte Carlo Simulation. Case studies are conducted on the RTS-79 system and the modified Europe Belgium natural gas network by using MATLAB and IBM CPLEX. The results indicate that the reliability of system decreases

Comprehensive quantitative dynamic accident modelling framework for chemical plants

This thesis introduces a comprehensive accident modelling approach that considers hazards associated with process plants including those that originate from the process itself; human factors including management and organizational errors; natural events related hazards; and intentional and security hazards in a risk assessment framework. The model is based on a series of plant protection systems, which are release, dispersion, ignition, toxicity, escalation, and damage control and emergency management prevention barriers. These six prevention barriers are arranged according to a typical sequence of accident propagation path. Based on successes and failures of these barriers, a spectrum of consequences is generated. Each consequence carries a unique probability of occurrence determined using event tree analysis. To facilitate this computation, the probability of failure for each prevention barrier is computed using fault tree analysis. In carrying out these computations, reliability data from established database are utilized. On occasion where reliability data is lacking, expert judgment is used, and evidence theory is applied to aggregate these experts’ opinion, which might be conflicting. This modelling framework also provides two important features; (i) the capability to dynamically update failure probabilities of prevention barriers based on precursor data, and (ii) providing prediction of future events. The first task is achieved effectively using Bayesian theory; while in the second task, Bayesian-grey model emerged as the most promising strategy with overall mean absolute percentage error of 18.07% based on three case studies, compared to 31.4% for the Poisson model, 22.37% for the first-order grey model, and 22.4% for the second-order grey model. The results obtained illustrated the potentials of the proposed modelling strategy in anticipating failures, identifying the location of failures and predicting future events. These insights are important in planning targeted plant maintenance and management of change, in addition to facilitating the implementation of standard operating procedures in a process plant

OpenSRANE, a Flexible and Extensible Platform for Quantitative Risk Assessment of NaTech Events

The effects of natural hazards triggering technological disaster (NaTech) on a society, economy and the environment is a multi-disciplinary research topic. The novelty of the issue and the lack of a standard procedure for risk assessment of this category of incidents show the need for more research in this area. This article introduces OpenSRANE as an open-source, extensible, flexible and object-oriented software for calculating the quantitative risk of NaTech events in process plants. Implementing the software in the Python programming environment provides high flexibility for the modeling and evaluations desired by users. The possibility of implementing the modifications and developments to the existing software as needed by users allows them to add their desired algorithms, elements and models to it, if needed. The software is based on the Monte Carlo method, but it is possible to implement other algorithms and approaches to it. Object-oriented programming and separation of the different parts of the software can increase the readability of the program, allowing researchers in different disciplines to focus easily on studying or developing the desired part with minimal interference from other parts. The applicability of the software has been demonstrated in a case study as well as the ability of the software to calculate results such as the individual risk, scenarios that consider domino effects and physical effects

Developing a Framework for Dynamic Risk Assessment Using Bayesian Networks and Reliability Data

PresentationProcess Safety in the oil and gas industry is managed through a robust Process Safety Management (PSM) system that involves the assessment of the risks associated with a facility in all steps of its life cycle. Risk levels tend to fluctuate throughout the life cycle of many processes due to several time varying risk factors (performances of the safety barriers, equipment conditions, staff competence, incidents history, etc.). While current practices for quantitative risk assessments (e.g. Bow-tie analysis, LOPA, etc.) have brought significant improvements in the management of major hazards, they are static in nature and do not fully take into account the dynamic nature of risk and how it improves risk-based decision making In an attempt to continually enhance the risk management in process facilities, the oil and gas industry has put in very significant efforts over the last decade toward the development of process safety key performance indicators (KPI or parameters to be observed) to continuously measure or gauge the efficiency of safety management systems and reduce the risks of major incidents. This has increased the sources of information that are used to assess risks in real-time. The use of such KPIs has proved to be a major step forward in the improvement of process safety in major hazards facilities. Looking toward the future, there appears to be an opportunity to use the multiple KPIs measured at a process plant to assess the quantitative measure of risk levels at the facility on a time-variant basis. ExxonMobil Research Qatar (EMRQ) has partnered with the Mary Kay O’Connor Process Safety Center – Qatar (MKOPSC-Q) to develop a methodology that establishes a framework for a tool that monitors in real time the potential increases in risk levels as a result of pre-identified risk factors that would include the use of KPIs (leading or lagging) as observations or evidence using Bayesian Belief Networks (BN). In this context, the paper presents a case study of quantitative risk assessment of a process unit using BN. The different steps of the development of the BN are detailed, including: translation of a Bowtie into a skeletal BBN, modification of the skeletal BN to incorporate KPIs (loss of primary containment (LOPC), equipment, management and human related), and testing of the BBN with forward and backward inferences. The outcomes of the dynamic modeling of the BN with real time insertion of evidence are discussed and recommendation for the framework for a dynamic risk assessment tool are made

Vulnerability Assessment of Process Vessels in the Event of Hurricanes

Hurricanes are multi-hazard natural hazards that can cause severe damage to chemical and process plants via individual or combined impact of strong winds, torrential rainfall, floods, and hitting waves especially in coastal areas. To assess and manage the vulnerability of process plants, failure modes and respective failure probabilities both before and after implementing safety measures should be assessed. However, due to the uncertainties arising from interdependent failure modes and lack of accurate and sufficient historical data, most conventional quantitative risk assessment techniques deliver inaccurate results, which in turn lead to inaccurate risk assessment and thus ineffective or non-cost-effective risk management strategies. Bayesian network (BN) is a probabilistic technique for reasoning under uncertainty with a variety of applications is system safety, reliability engineering, and risk assessment. In this chapter, applications of BN to vulnerability assessment and management of process vessels in the event of hurricanes are demonstrated and discussed

Treatment of Uncertainties in Probabilistic Risk Assessment

Probabilistic risk assessment (PRA), sometimes called probabilistic safety analysis, quantifies the risk of undesired events in industrial facilities. However, one of the weaknesses that undermines the credibility and usefulness of this technique is the uncertainty in PRA results. Fault tree analysis (FTA) and event tree analysis (ETA) are the most important PRA techniques for evaluating system reliabilities and likelihoods of accident scenarios. Uncertainties, as incompleteness and imprecision, are present in probabilities of undesired events and failure rate data. Furthermore, both FTA and ETA traditionally assume that events are independent, assumptions that are often unrealistic and introduce uncertainties in data and modeling when using FTA and ETA. This work explores uncertainty handling approaches for analyzing the fault trees and event trees (method of moments) as a way to overcome the challenges of PRA. Applications of the developed frameworks and approaches are explored in illustrative examples, where the probability distributions of the top event of fault trees are obtained through the propagation of uncertainties of the failure probabilities of basic events. The application of the method of moments to propagate uncertainty of log-normal distributions showed good agreement with results available in the literature using different methods