Effective firefighting and evacuation are integral parts of emergency response plans in process plants, which play a key role in protecting human lives and assets in the event of major fires. Given sufficient firefighting resources, firefighters would suppress all the burning vessels and cool off all the exposed vessels in order to contain the fire and prevent a fire-induced domino effect. However, when the number of critical units—whether on fire or exposed to fire—exceeds the firefighting resources, firefighters should decide how to optimally allocate the resources so as to best satisfy the safety goals. To facilitate such decisions, the present work aims to develop a methodology for effective firefighting under insufficient resources. The methodology seeks out two safety goals via optimal firefighting strategies: (1) providing for the safety of evacuees, and (2) reducing the risk of domino effects. Although both safety goals are attempted to be satisfied at the same time, a higher priority is assigned to the first goal as long as the evacuation is underway. When the evacuation is complete, all the resources are focused on the second goal. The study shows that a multi-objective optimization approach to identifying firefighting plans outdoes single-objective optimization approaches in that several safety goals could be met at once. Although only two safety goals are considered in the present study, the methodology is flexible enough to accommodate several goals such as safety of offsite people and assets.@en

Modeling potential domino scenarios in process plants includes the prediction of the most probable sequence of events and the calculation of respective probabilities, so-called escalation probabilities, so that appropriate prevention and mitigation safety measures can be devised. Domino effect modeling, however, is very challenging mainly due to uncertainties involved in estimation of escalation probabilities (parameter uncertainty) and prediction of the sequence of events during a domino effect (model uncertainty). In the present study, a methodology based on dynamic Bayesian network is developed for identification of the most likely sequence of events in domino scenarios while accounting for model uncertainty. Verifying the accuracy of the methodology based on a comparison with previous studies, the methodology is applied to model single-primary-event and multiple-primary-event domino scenarios in process plants.

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Process safety has been practiced as a field of research and safety management in the oil and chemical industries since the 1960s. Over this period there have been many tragic incidents, which have resulted in fatalities as well as asset, environmental, and reputational damage. While standards have improved since then and much work has been done, particularly in inherently safer design and management systems, catastrophic incidents are still happening and will continue to do so until we tackle them head on. It appears as if we are not learning lessons from the past, because the causes of failures for current incidents are the same as past incidents, albeit in different environments. We must learn from these incidents. As an industry, our inability to learn from past incidents and demonstrate that process safety is improving has led to a project called “Process Safety in the 21st Century and Beyond”. The aim of this project is to envision better process safety by outlining efforts that each stakeholder can take. This project was led by a steering committee convened to bring in academic, industrial, regulatory, and societal perspectives from around the world and across stakeholders. Prof. Dr.-Ing. Jürgen Schmidt was part of the steering committee which identified the challenges in process safety and published their results in an informative brochure.@en

Process accidents in the early 1980s have drawn process safety into the main stream. In the 1990s, risk-based approaches were developed to bring safety into design as well as economic considerations. In the 2000s, the inherent safety approach began to be practiced on a limited basis, and process safety evolution made significant progress. As process systems continue to become more complex and operations move into remote and harsh environments, traditional risk analysis may no longer be sufficient. Dynamic risk assessment is the basis for the next generation of risk and management approaches that help to enable safer complex process systems operating in extreme environments. This article investigates the main contributions in the area of dynamic risk assessment. Then, an overall framework for dynamic risk management of process facilities is proposed.

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Accidental releases of hazardous chemicals from process facilities can cause catastrophic consequences. The Bhopal disaster resulting from a combination of inherently unsafe designs and poorly managed operations is a well-known case. Effective risk modeling approaches that provide early warnings are helpful to prevent and control such rare but catastrophic events. Probability estimation of these events is a constant challenge due to the scarcity of directly relevant data. Therefore, precursor-based methods that adopt the Bayesian theorem to update prior judgments on event probabilities using empirical data have been proposed. The updated probabilities are then integrated with consequences of varying severity to produce the risk profile. This paper proposes an operational risk assessment framework, in which a precursor-based Bayesian network approach is used for probability estimation, and loss functions are applied for consequence assessment. The estimated risk profile can be updated continuously given real-time operational data. As process facilities operate, this method integrates a failure-updating mechanism with potential consequences to generate a real-time operational risk profile. The real time risk profile is valuable in activating accident prevention and control strategies. The approach is applied to the Bhopal accident to demonstrate its applicability and effectiveness.

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Rare events often result in large impacts and are hard to predict. Risk analysis of such events is a challenging task because there are few directly relevant data to form a basis for probabilistic risk assessment. Due to the scarcity of data, the probability estimation of a rare event often uses precursor data. Precursor-based methods have been widely used in probability estimation of rare events. However, few attempts have been made to estimate consequences of rare events using their precursors. This paper proposes a holistic precursor-based risk assessment framework for rare events. The Hierarchical Bayesian Approach (HBA) using hyper-priors to represent prior parameters is applied to probability estimation in the proposed framework. Accident precursor data are utilized from an information theory perspective to seek the most informative precursor upon which the consequence of a rare event is estimated. Combining the estimated probability and consequence gives a reasonable assessment of risk. The assessed risk is updated as new information becomes available to produce a dynamic risk profile. The applicability of the methodology is tested through a case study of an offshore blowout accident. The proposed framework provides a rational way to develop the dynamic risk profile of a rare event for its prevention and control.

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Environmental decision-making in offshore oil and gas (OOG) operations can be extremely complex due to conflicting objectives or criteria, availability of vague and uncertain information, and interdependency among multiple decision-makers. Most existing studies ignore conflicting preferences and strategic interactions among decision-makers. This paper presents a game theoretical approach to solve multi-criteria conflict resolution problem under constrained and uncertain environments. Uncertainties in the quantification of imprecise data are expressed using rough numbers. A multi-criteria game is developed to model a decision problem in which three groups of decision-makers (i.e.; operators, regulators and service engineers) are involved. This game is solved using the generalized maximin solution concept. With the solution (i.e.; optimal weights of the criteria), the rough numbers can be aggregated to an expected payoff for each alternative. Finally, the weights of upper and lower limits of a rough number are employed to transform the expected payoff into a crisp score, based on which all alternatives are ranked to identify the best one. A numerical example is outlined to demonstrate the application of the proposed method to the selection of management scenarios of drilling wastes.@en

Dust explosion is one of the main threats to equipment safety and human health in industries. Complex factors leading to accidents, serious consequences, and relevant safety measures are the main interests of governmental agencies, researchers, and industrial companies. However, a generic risk analysis model for dust explosions is absent. The bow-tie model can be used to investigate the relationships among basic causes, safety barriers, and possible consequences of an accident scenario. In this paper, a framework is established for quantitative risk assessment of dust explosions based on bow-tie analysis via review and analysis of previous major dust explosions. A large inventory of relevant safety measures is presented, and the implementation and efficacy of such safety measures to reduce the risk of dust explosions is thoroughly discussed. Finally, the methodology is applied to a case study. The results show that the generic bow-tie developed in this study can be tailored to a wide variety of dust explosion accident scenarios with minimal manipulation; also, implementation of relevant safety measures can significantly reduce the risk of dust explosions@en

Activities in offshore oil and gas (OOG) that cause environmental impacts can be systematically managed through an environmental management system (EMS). Environmental performance evaluation (EPE) is an essential part of an EMS. However, previous studies on EPE indicate that existing lists of indicators little insight into how indicators are modified to more accurately assess environmental performance. In this paper, a way is proposed to identify and define specific environmental performance indicators on a case-by-case basis, which consists of five steps: (1) describing environmental requirements; (2) determining favourable outcomes corresponding to the requirements; (3) identifying required activities or issues to achieve the outcomes; (4) searching for proper measures of the activities or issues; and (5) generating a list of key indicators. Based on these steps, a quality function deployment (QFD) approach is developed to determine key indicators and evaluate environmental performance. To handle uncertainties in QFD, the decision makers' evaluations are quantified through rough numbers using the concept of rough sets. The outputs of the proposed approach are different environmental performance indices. Using these indices, decision makers can easily determine whether an improved performance has been achieved through an EMS. The proposed approach is transparent and promising for use as a unified tool for EPE. An application of the proposed approach is demonstrated through a numerical example.@en