Texas LPG fire: Domino effects triggered by natural hazards

© 2018 Institution of Chemical Engineers On February 2007, a massive fire in a propane de-asphalting unit in an oil refinery in Texas, USA happened due to liquid propane release from a cracked pipe in a control station injuring four people, damaging extensive equipment, causing significant business interruption, and resulting in more than $50 million losses. The accident was triggered by a natural hazard: freezing of piping at a control station caused an inlet pipe elbow to crack, which in turn, led to the release of high-pressure liquid propane which was rapidly ignited. In addition, there were two near-miss events due to potential domino effects. In fact, the accident could reasonably have resulted in much more severe consequences due to the exposure of large butane storage spheres and chlorine containers, increasing the possibility of a catastrophic domino effect. This paper develops a Natech (natural hazard triggering technological disasters) risk assessment methodology that relies upon Bayesian network capabilities and takes into account the potential Natech domino effects. The methodology is implemented in the intended refinery and mathematically graphically represents the dynamic cause–effect relations between units involved in the scenario, and handles uncertainties among the interactions. In addition, the methodology can provide a risk value for the entire scenario that can be used further for risk-based decision making

The explosion at institute: Modeling and analyzing the situation awareness factor

In 2008 a runaway chemical reaction caused an explosion at a methomyl unit in West Virginia, USA, killing two employees, injuring eight people, evacuating more than 40,000 residents adjacent to the facility, disrupting traffic on a nearby highway and causing significant business loss and interruption. Although the accident was formally investigated, the role of the situation awareness (SA) factor, i.e., a correct understanding of the situation, and appropriate models to maintain SA, remain unexplained. This paper extracts details of abnormal situations within the methomyl unit and models them into a situational network using dynamic Bayesian networks. A fuzzy logic system is used to resemble the operator's thinking when confronted with these abnormal situations. The combined situational network and fuzzy logic system make it possible for the operator to assess such situations dynamically to achieve accurate SA. The findings show that the proposed structure provides a useful graphical model that facilitates the inclusion of prior background knowledge and the updating of this knowledge when new information is available from monitoring systems

A situation risk awareness approach for process systems safety

Promoting situation awareness is an important design objective for a wide variety of domains, especially for process systems where the information flow is quite high and poor decisions may lead to serious consequences. In today's process systems, operators are often moved to a control room far away from the physical environment, and increasing amounts of information are passed to them via automated systems, they therefore need a greater level of support to control and maintain the facilities in safe conditions. This paper proposes a situation risk awareness approach for process systems safety where the effect of ever-increasing situational complexity on human decision-makers is a concern. To develop the approach, two important aspects - addressing hazards that arise from hardware failure and reducing human error through decision-making - have been considered. The proposed situation risk awareness approach includes two major elements: an evidence preparation component and a situation assessment component. The evidence preparation component provides the soft evidence, using a fuzzy partitioning method, that is used in the subsequent situation assessment component. The situation assessment component includes a situational network based on dynamic Bayesian networks to model the abnormal situations, and a fuzzy risk estimation method to generate the assessment result. A case from US Chemical Safety Board investigation reports has been used to illustrate the application of the proposed approach. © 2013 Elsevier Ltd

Bayesian networks for spatio-temporal integrated catchment assessment

Includes abstract.Includes bibliographical references (leaves 181-203).In this thesis, a methodology for integrated catchment water resources assessment using Bayesian Networks was developed. A custom made software application that combines Bayesian Networks with GIS was used to facilitate data pre-processing and spatial modelling. Dynamic Bayesian Networks were implemented in the software for time-series modelling

A Hierarchical Model-Based Reasoning Approach for Fault Diagnosis in Multi-Platform Space Systems

Health monitoring and fault diagnosis in traditional single spacecraft missions are mostly accomplished by human operators on ground through around-the-clock monitoring and trend analysis on huge amount of telemetry data. Future multiplatform space missions, commonly known as the formation flight missions, will utilize multiple inexpensive spacecraft in formation by distributing the functionalities of a single platform among the miniature inexpensive platforms. Current spacecraft diagnosis practices do not scale up well for multiple space platforms due to an increasing need to make the long-duration missions cost-effective by limiting the size of the operations team which will be large if traditional diagnosis is employed. An ideal solution to this problem is to incorporate an autonomous fault detection, isolation, and recovery (FDIR) mechanism. However, the effectiveness of spacecraft autonomy is yet to be demonstrated and due to the existence of perceived risks, it is often desired that the expert human operators be involved in the spacecraft operations and diagnosis processes i.e., the autonomous spacecraft actions be understandable by the human operators on ground so that intervention may be made, if necessary. To address the above problems and requirements, in this research a systematic and transparent fault diagnosis methodology for ground-based operations of multi-platform space systems is developed. First, novel hierarchical fault diagnosis concepts and framework are developed. Within this framework, a multi-platform space system is decomposed hierarchically into multiple levels. The decomposition is driven by the need for supporting the development of the components/subsystems of the overall system by a number of design teams and performing integration at the end. A multi-platform system is considered to be a set of interacting components where components at different levels correspond to formation, system, sub-system, etc. depending on the location of the node in the hierarchy. Two directed graph based fault diagnosis models are developed namely, fuzzy rule based hierarchical fault diagnosis model (HFDM), and Bayesian networks (BN)-based component dependency model (CDM). In HFDM, fault diagnosis of different components in the formation flight is investigated. Fuzzy rules are developed for fault diagnosis at different levels in the hierarchy by taking into account the uncertainties in the fault manifestations in a given component. In this model, the component interactions are quantified without taking the uncertainties in the component health state dependencies into account. Next, a component dependency model (CDM) based on Bayesian networks (BN) models is developed in order to take the uncertainties in component dependencies into account. A novel methodology for identifying CDM parameters is proposed. Fault evidences are introduced to the CDM when the fault modes of a component are observed via fuzzy rule activations. Advantages and limitations associated with the proposed HFDM and the CDM are also discussed. Finally, the verification and validation (V&V) of the hierarchical diagnosis models are investigated via a sensitivity analysis approach. It should be noted that the proposed methodology and the fault diagnosis strategies and algorithms that are developed in this research are generic in a sense that they can be applied to any hierarchically decomposable complex systems. However, the system and domain specific knowledge they require, especially for modeling component dependencies, are mostly available in the aerospace industry where extensive system design and integration-related analysis are common due to high system building cost and failure risks involved