Rouzbeh Abbassi

The role of human error in risk analysis: Application to pre- and post-maintenance procedures of process facilities

Human factors play an important role in the safe operation of a facility. Human factors include the systematic application of information about human characteristics and behavior to increase the safety of a process system. A significant proportion of human errors occur during the maintenance phase. However, the quantification of human error probabilities in the maintenance phase has not been given the amount of attention it deserves. This paper focuses on a human factors analysis in pre-and post- pump maintenance operations. The procedures for removing process equipment from service (pre-maintenance) and returning the equipment to service (post-maintenance) are considered for possible failure scenarios. For each scenario, human error probability is calculated for each activity using the Success Likelihood Index Method (SLIM). Consequences are also assessed in this methodology. The risk assessment is conducted for each component and the overall risk is estimated by adding individual risks. The present study is aimed at highlighting the importance of considering human error in quantitative risk analyses. The developed methodology has been applied to a case study of an offshore process facility.

Corrosion induced failure analysis of subsea pipelines

Pipeline corrosion is one of the main causes of subsea pipeline failure. It is necessary to monitor and analyze pipeline condition to effectively predict likely failure. This paper presents an approach to analyze the observed abnormal events to assess the condition of subsea pipelines. First, it focuses on establishing a systematic corrosion failure model by Bow-Tie (BT) analysis, and subsequently the BT model is mapped into a Bayesian Network (BN) model. The BN model facilitates the modelling of interdependency of identified corrosion causes, as well as the updating of failure probabilities depending on the arrival of new information. Furthermore, an Object-Oriented Bayesian Network (OOBN) has been developed to better structure the network and to provide an efficient updating algorithm. Based on this OOBN model, probability updating and probability adaptation are performed at regular intervals to estimate the failure probabilities due to corrosion and potential consequences. This results in an interval-based condition assessment of subsea pipeline subjected to corrosion. The estimated failure probabilities would help prioritize action to prevent and control failures. Practical application of the developed model is demonstrated using a case study.

Dynamic risk-based maintenance for offshore processing facility

Processing facilities in a marine environment may not remain safe and available if they are not well maintained. Dynamic risk‐based maintenance (RBM) methodology is a tool for maintenance planning and decision making, used to enhance the safety and availability of the equipment. It also assists in identifying and prioritizing the maintenance of equipment based on the level of risk. This article discusses an advanced methodology for the design of an optimum maintenance program integrating a dynamic risk‐based approach with a maintenance optimization technique. In this study, Bayesian Network (BN) is employed to develop a new dynamic RBM methodology that is capable of using accident precursor information in order to revise the risk profile. The use of this methodology is based on its failure prediction capability which optimizes the cost of maintenance. The developed methodology is applied to a case study involving a failure of a separator system in the offshore oil and gas production platform considering marine environments. The result shows it is essential that the valve system in the separator needs to be planned for maintenance once every 25 days; however, the cooler system can be planned for repairs once only biennially. A sensitivity analysis is also conducted to study the criticality of the operating system.

Vulnerability analysis of process plants subject to domino effects

In the context of domino effects, vulnerability analysis of chemical and process plants aims to identify and protect installations which are relatively more susceptible to damage and thus contribute more to the initiation or propagation of domino effects. In the present study, we have developed a methodology based on graph theory for domino vulnerability analysis of hazardous installations within process plants, where owning to the large number of installations or complex interdependencies, the application of sophisticated reasoning approaches such as Bayesian network is limited. We have taken advantage of a hypothetical chemical storage plant to develop the methodology and validated the results using a dynamic Bayesian network approach. The efficacy and out-performance of the developed methodology have been demonstrated via a real-life complex case study.

Effects of Cold Environments on Human Reliability Assessment in Offshore Oil and Gas Facilities

Objective: This paper proposes a new methodology that focuses on the effects of cold and harsh environments on the reliability of human performance. Background: As maritime operations move into Arctic and Antarctic environments, decision makers must be able to recognize how cold weather affects human performance and subsequently adjusts management and operational tools and strategies. Method: In the present work, a revised version of the Human Error Assessment and Reduction Technique (HEART) methodology has been developed to assess the effects of cold on the likelihood of human error in offshore oil and gas facilities. This methodology has been applied to post-maintenance tasks of offshore oil and gas facility pumps to investigate how management, operational, and equipment issues must be considered in risk analysis and prediction of human error in cold environments. Results: This paper provides a proof of concept indicating that the risk associated with operations in cold environments is greater than the risk associated with the same operations performed in temperate climates. It also develops guidelines regarding how this risk can be assessed. The results illustrate that in post-maintenance procedures of a pump, the risk value related to the effect of cold and harsh environments on operator cognitive performance is twice as high as the risk value when performed in normal conditions. Conclusion: The present work demonstrates significant differences between human error probabilities (HEPs) and associated risks in normal conditions as opposed to cold and harsh environments. This study also highlights that the cognitive performance of the human operator is the most important factor affected by the cold and harsh conditions. Application: The methodology developed in this paper can be used for reevaluating the HEPs for particular scenarios that occur in harsh environments since these HEPs may not be comparable to similar scenarios in normal conditions.

Determination of Human Error Probabilities for the Maintenance Operations of Marine Engines

Human error is a crucial factor in the shipping industry and not to mention numerous human errors occur during the maintenance procedures of marine engines. Determination of human error probabilities (HEPs) is important to reduce the human errors and prevent the accidents. Nevertheless, determination of HEPs in the maintenance procedures of marine engines has not been given desired attention. The aim of this study is to determine the HEPs for the maintenance procedures of the marine engines to minimize the human errors and preclude accidents from the shipping industry. The Success Likelihood Index Method is used to determine the HEPs due to the unavailability of human error data for maintenance procedures of marine engines. The results showed that among the 43 considered activities in this study, inspection and overhauls piston/piston rings have the lowest HEP meaning it has a lower consequence for accidents. On the other hand, fuel and lubricating oil filters pressure difference checking and renews filter elements activity have the highest HEP indicating it has high chances for accidents.

A network based approach to envisage potential accidents in offshore process facilities

Envisaging potential accidents in large scale offshore process facilities such as Floating Liquefied Natural Gas (FLNG) is complex and could be best characterized through evolving scenarios. In the present work, a new methodology is developed to incorporate evolving scenarios in a single model and predicts the likelihood of accident. The methodology comprises; (a) evolving scenario identification, (b) accident consequence framework development, (c) accident scenario likelihood estimation, and (d) ranking of the scenarios. Resulting events in the present work are modeled using a Bayesian network approach, which represents accident scenarios as cause‐consequences networks. The methodology developed in this article is compared with case studies of ammonia and Liquefied Natural Gas from chemical and offshore process facility, respectively. The proposed method is able to differentiate the consequence of specific events and predict probabilities for such events along with continual updating of consequence probabilities of fire and explosion scenarios taking into account. The developed methodology can be used to envisage evolving scenarios that occur in the offshore oil and gas process industry; however, with further modification it can be applied to different sections of marine industry to predict the likelihood of such accidents.

Pitting Degradation Modeling of Ocean Steel Structures Using Bayesian Network

Modelling depth of long-term pitting corrosion is of interest for engineers in predicting the structural longevity of ocean infrastructures. Conventional models demonstrate poor quality in predicting the long-term pitting corrosion depth. Recently developed phenomenological models provide a strong understanding of the pitting process however they have limited engineering applications. In this study, a novel probabilistic model is developed for predicting the long-term pitting corrosion depth of steel structures in marine environment using Bayesian Network. The proposed Bayesian Network model combines an understanding of corrosion phenomenological model and empirical model calibrated using real-world data. A case study, which exemplifies the application of methodology to predict the pit depth of structural steel in long-term marine environment, is presented. The result shows that the proposed methodology succeeds in predicting the time dependent, long-term anaerobic pitting corrosion depth of structural steel in different environmental and operational conditions.

Performance assessment of aeration and radial oxygen loss assisted cathode based integrated constructed wetland-microbial fuel cell systems

The present study explores low-cost cathode development possibility using radial oxygen loss (ROL) of Canna indica plants and intermittent aeration (IA) for wastewater treatment and electricity generation in constructed wetland-microbial fuel cell (CW-MFC) system. Two CW-MFC microcosms were developed. Amongst them, one microcosm was planted with Canna indica plants for evaluating the ROL dependent cathode reaction (CW-MFC dependent on ROL) and another microcosm was equipped with intermittent aeration for evaluating the intermittent aeration dependent cathode reaction (CW-MFC with additional IA). The CW-MFC with additional IA has achieved 78.71% and 53.23%, and CW-MFC dependent on ROL has achieved 72.17% and 46.77% COD removal from synthetic wastewater containing glucose loads of 0.7gL-1and 2.0gL-1, respectively. The maximum power density of 31.04mWm-3 and 19.60mWm-3 was achieved in CW-MFC with additional IA and CW-MFC dependent on ROL, respectively.

Risk-Based Prioritisation of Indoor Air Pollution Monitoring Using Computational Fluid Dynamics

There has been an increasing concern on indoor air quality in recent years due to the possible harmful effects to human health. Indoor air pollution as a result of using natural gas for cooking and heating is a common health threat, particularly for women and young children. Therefore, quantification of the type and emission levels of these pollutants is necessary in order to mitigate and monitor the emissions. Computational fluid dynamics (CFDs) can be used to model airflow and dispersion within buildings of complex geometry and layout. In the present paper, a CFD analysis is performed to determine the concentration of indoor air quality for a typical one-floor building in order to determine the optimal locations of monitoring sensors. According to this study, placing the monitoring sensors based on the maximum concentrations of the individual contaminant does not entirely overcome the problems, as the concentrations of different hazardous pollutants cannot be added. Moreover, high concentration with low duration of exposure is not a good candidate for placing the monitoring system. A risk-based methodology is proposed to determine the optimal location for the monitoring systems. Different risk management strategies are also considered as a part of the methodology to reduce the exposure risk of indoor contaminants.