Adriana Miralles Schleder

A Methodology for Risk Analysis Based on Hybrid Bayesian Networks: Application to the Regasification System of Liquefied Natural Gas Onboard a Floating Storage and Regasification Unit

This article presents an iterative six-step risk analysis methodology based on hybrid Bayesian networks (BNs). In typical risk analysis, systems are usually modeled as discrete and Boolean variables with constant failure rates via fault trees. Nevertheless, in many cases, it is not possible to perform an efficient analysis using only discrete and Boolean variables. The approach put forward by the proposed methodology makes use of BNs and incorporates recent developments that facilitate the use of continuous variables whose values may have any probability distributions. Thus, this approach makes the methodology particularly useful in cases where the available data for quantification of hazardous events probabilities are scarce or nonexistent, there is dependence among events, or when nonbinary events are involved. The methodology is applied to the risk analysis of a regasification system of liquefied natural gas (LNG) on board an FSRU (floating, storage, and regasification unit). LNG is becoming an important energy source option and the worlds capacity to produce LNG is surging. Large reserves of natural gas exist worldwide, particularly in areas where the resources exceed the demand. Thus, this natural gas is liquefied for shipping and the storage and regasification process usually occurs at onshore plants. However, a new option for LNG storage and regasification has been proposed: the FSRU. As very few FSRUs have been put into operation, relevant failure data on FSRU systems are scarce. The results show the usefulness of the proposed methodology for cases where the risk analysis must be performed under considerable uncertainty.

The effect of the computational grid size on the prediction of a flammable cloud dispersion

The consequence analysis is used to define the extent and nature of effects caused by undesired events being of great help when quantifying the damage caused by such events. For the case of leaking of flammable and/or toxic materials, effects are analyzed for explosions, fires and toxicity. Specific models are used to analyze the spills or jets of gas or liquids, gas dispersions, explosions and fires. The central step in the analysis of consequences in such cases is to determine the concentration of the vapor cloud of hazardous substances released into the atmosphere, in space and time. With the computational advances, CFD tools are being used to simulate short and medium scale gas dispersion events, especially in scenarios where there is a complex geometry. However, the accuracy of the simulation strongly depends on diverse simulation parameters, being of particular importance the grid resolution. This study investigates the effects of the computational grid size on the prediction of a cloud dispersion considering both the accuracy and the computational cost. Experimental data is compared with the predicted values obtained by means of CFD simulation, exploring and discussing the influence of the grid size on cloud concentration the predicted values. This study contributes to optimize CFD simulation settings concerning grid definition when applied to analyses of consequences in environments with complex geometry.

Bayesian Networks on Risk Analysis of a Regasification System on an Offshore Unit

Nowadays, LNG Import Terminals (where the storage and regasification process is conducted) are mostly onshore; the construction of these terminals is costly and many adaptations are necessary to abide by environmental and safety laws. Moreover, an accident in one of these plants might produce considerable impact in neighboring areas and population; this risk may be even worse due to the possibility of a terrorist attack. Under this perspective, a discussion is conducted about a vessel known as FSRU (Floating Storage and Regasification Unit), which is a storage and regasification offshore unit, that can work miles away from de coast and, owing to this, can be viewed as an option for LNG storage and regasification facilities. The goal is to develop a method for using Bayesian Networks in the Risk Analysis of Regasification System of the FSRU, which will convert Fault Trees (FT) into Bayesian Networks (BN) providing more accurate data. Using BN is possible to represent uncertain knowledge and local conditional dependencies. In addition, FT models the failure modes as independent and binary events while BN may model a larger number of states. It is worth noting that BN does not require the determination of cut sets; however, given a failure, it is capable of providing the probability of each possible cut set. This method will provide information to define, in a future study, a maintenance plan based on the Reliability Centered Maintenance. The results intend to clarify the applicability of BN on risk assessment and might improve the risk analysis of a Regasification System FSRU.Copyright

Reliability Analysis of the Regasification System on Board of a FSRU Using Bayesian Networks

© 2012 Ramos Martins and Miralles Schleder, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Reliability Analysis of the Regasification System on Board of a FSRU Using Bayesian Networks

Multicriteria Optimization for System Configuration Using Monte Carlo Simulation and RAM Analysis

Currently, engineers should to deal with conflicting objectives, especially concerning safety and economics constraints. It is necessary to take into account performance indicators like reliability and availability coupled with economic criteria such as the costs of acquisition, maintenance and plant downtime. This paper aims at bringing up a rational process of selecting the optimal configuration of the system in the preliminary design phase considering these conflicting indicators. Here, it is proposed a coupled approach using Monte Carlo Simulation and Genetic Algorithms to define the system configuration and the time interval between preventive maintenances in order to maximize the availability and the expected profit with the system operation considering possible constraints about the number of maintenance teams. The approach proposed in this paper has shown to be promising for solving complex system design related to realistic scenarios in which conflicting performance and economic objectives must be taken into account. INTRODUCTION The complexity of the current engineering systems compel engineers to deal with conflicting objectives, specially concerning to safety and economics constrains. In order to optimize the design, one must take into account performance indicators like reliability and availability coupled with economic criteria such as the costs of acquisition, maintenance and plant downtime. The main goal of this paper is to present a rational process to select the optimal configuration of the system in the preliminary design phase considering these competing indicators. For this nontrivial multi-objective optimization problem, there is no single solution that simultaneously optimizes each objective; there is a set of optimal solutions (possibly an infinite number) that can be represented in a Pareto front [1]. In this context, this paper proposes a coupled approach using Monte Carlo Simulation (MCS) and Genetic Algorithms (GA) to define the system configuration and the time interval between preventive maintenances in order to maximize the availability and the expected profit with the system operation considering possible constraints about the number of maintenance teams. The Monte Carlo simulation is a widely used method in solving real engineering problems in several fields. Lately, the use of this method to determine the availability of complex systems and the monetary value of transactions and maintenance of production plants has increased. The complexity of current engineering systems coupled with the need of realistic considerations in shaping their availabilities and reliabilities result in very complex methods. Similarly, analyzes involving repairable systems and multiple additional events are very difficult to solve analytically.