Marcelo Ramos Martins

Application of Bayesian Belief networks to the human reliability analysis of an oil tanker operation focusing on collision accidents

During the last three decades, several techniques have been developed for the quantitative study of human reliability. In the 1980s, techniques were developed to model systems by means of binary trees, which did not allow for the representation of the context in which human actions occur. Thus, these techniques cannot model the representation of individuals, their interrelationships, and the dynamics of a system. These issues make the improvement of methods for Human Reliability Analysis (HRA) a pressing need. To eliminate or at least attenuate these limitations, some authors have proposed modeling systems using Bayesian Belief Networks (BBNs). The application of these tools is expected to address many of the deficiencies in current approaches to modeling human actions with binary trees.

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.

Implantação e consolidação de escritório de gerenciamento de projetos: um estudo de caso

The growth in telecommunications sector, mainly on mobile telephony, has been very significant since the end of 90´s due to the public policies adopted in Brazil. Among growth strategies, adopted for the four biggest mobile telephony companies in Brazil, there is the use of the joint ventures with other companies, that long ago worked regionally, in order to get the national operation status. As a result of joint venture process, there is the challenge of Technology Information Systems consolidation. Aiming to face these challenges, companies have been using project management practices implemented through the Project Management Offices (PMO). This paper presents the difficulties during the Project Management Office implementing and consolidation processes, in the context of mobile telephony companies’ joint ventures.

Outsourcing Operations in Project Management Offices: The Reality of Brazilian Companies

The purpose of this study is to explore the mechanisms that influence decisions regarding outsourcing competencies in the operation of project management offices (PMOs). The exploratory research described here involves the use of a web-based survey for enterprises in Brazil. In 78 of the surveys valid cases, the PMO is operated using the organizations internal resources. A possible conclusion is that the PMO is unlikely to positively relate to the culture of external services used by the organization and to use outsourcing to operate the PMO.

Multi-Objective Optimization Design of Tanker Ships via a Genetic Algorithm

The cost of a new ship design heavily depends on the principal dimensions of the ship; however, dimensions minimization often conflicts with the minimum oil outflow (in the event of an accidental spill). This study demonstrates one rational methodology for selecting the optimal dimensions and coefficients of form of tankers via the use of a genetic algorithm. Therein, a multi-objective optimization problem was formulated by using two objective attributes in the evaluation of each design, specifically, total cost and mean oil outflow. In addition, a procedure that can be used to balance the designs in terms of weight and useful space is proposed. A genetic algorithm was implemented to search for optimal design parameters and to identify the nondominated Pareto frontier. At the end of this study, three real ships are used as case studies.

Quantitative risk analysis and comparison for onshore and offshore lng terminals: the port of suape - brazil case

In recent decades, natural gas has been gaining importance in world energy scene and established itself as an important source of energy. One of the biggest obstacles to increase the usage of natural gas is its transportation, mostly done in its liquid form, LNG – Liquefied Natural Gas, and storage. It involves the liquefaction of natural gas, transport by ship, its storage and subsequent regasification, in order to get natural gas in its original form and send it to the final destination through natural gas pipeline system. Nowadays, most terminals for receiving, storing and regasificating LNG, as well as sending-out natural gas are built onshore. These terminals, however, are normally built close to populated areas, where consuming centers can be found, creating safety risks to the population nearby. Apart from possible damages caused by its cryogenic temperatures, LNG spills are associated with hazards such as pool fires and ignition of drifting vapor clouds. Alternatively to onshore terminals, there are currently several offshore terminals projects in the world and some are already running. Today, Brazil owns two FSRU (Floating Storage and Regasification Unit) type offshore terminals, one in Guanabara Bay, Rio de Janeiro and the other in Pecem, Ceara, both contracted to PETROBRAS. The identification of the operation risks sources of LNG terminals onshore and offshore and its quantification through mathematical models can identify the most suitable terminal type for a particular location. In order to identify and compare the risks suggested by onshore and offshore LNG terminals, we have taken the example of the Suape Port and its Industrial Complex, located in Pernambuco, Brazil, which is a promising location for the installation of a LNG terminal. The present work has focused on calculating the distance to the LNG vapor cloud with the lower flammability limits (LFL), as well as thermal radiation emitted by pool fire, in case of a LNG spill from an onshore and from an offshore terminal. The calculation was made for both day and night periods, and for three types of events: operational accident, non-operational accident and worst case event, corresponding to a hole size of 0,75m, 1,5m e 5m, respectively. Even though the accidents that happen at an onshore terminal generate smaller vulnerability distances, according to the results it would not be desirable for the Suape Port, due to the location high density of industries and people working. Therefore, an offshore terminal would be more desirable, since it presents less risk to the surrounding populations, as well as for workers in this location.Copyright

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

The Application of the Bayesian Networks in the Human Reliability Analysis

Analysis, assessment, and risk management are vital in the oil and gas sector — due to the potential severity of accidents with respect to human life, to the environment, and patrimony. As most of the accidents in this area are driven by human factors, the purpose of this work is to present an efficient methodology and technique for the analysis of these factors in this industry. The applied quantitative technique is based on Bayesian Networks. Finally, this article presents a study of an oil tanker operation, focusing the quantification of the human factor contribution in collision scenarios. Besides, are presented some considerations about management and organizational factors that can influence the human performance and, therefore, the collision risk.Copyright

Application of Bayesian Networks in the Analysis of Human Contribution in Collision Accidents

Analysis, assessment, and risk management are vital in the oil and gas sector due to the potential severity of accidents with respect to human life, to the environment, and patrimony. As most of the accidents in this area are driven by human factors, the purpose of this work is to present an efficient methodology and technique for the analysis of these factors in this industry. The applied quantitative technique is based on Bayesian Networks. Finally, this article presents a study of an oil tanker operation, focusing on the quantification of the human factor contribution in collision scenarios.Copyright