This paper presents a data-driven approach to predict the pipelines’ corrosion-induced Burst failure. In this approach, different aspects of pit growth progression and spatial distribution of pits are simulated. The proposed approach takes advantage of population characteristics to model these aspects of the degradation paths for each pipe section down to the size of single joints. The insights obtained from simulations are used to project the degradation of each pipe section. Understanding corrosion behavior and field data are used to model the corrosion-related parameters such as corrosion pit dimensions, probability and time of initiation, and location. The failure is modeled using the probabilistic simulation considering degradation rate, interactions among pits, and material properties as stochastic variables. The proposed approach and included models are tested using multiple real-life inline inspection datasets. Validation of predicted properties shows prediction errors ranging from 3%–10% depending on the three remaining strength calculation approaches. This work aimed to serve as an important tool for risk-based maintenance prioritization, inspection interval assessment, and the fitness of service assessment of pipelines.

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As an important consideration in the chemical industry, chemical process safety has received notable attention in China. However, catastrophic chemical accidents still occur. To better understand why accidents continue to occur, this paper presented a diagnostic analysis of 14 major chemical accidents in China from 2012 to 2022 based on VOSviewer software. The authors analysed the correlation between the accident causation and their relationship with the safety management elements. The study observed that inferior process safety culture, intentional violation (rule-breaking) of procedure, inadequate safety training, and illegal operations were the most frequent causes of accidents. These causes highlighted the prominent gaps in PSM in China in the process safety culture, compliance with standards, the conduct of operations, process safety competency, and training & performance assurance. The results based on co-occurrence analysis indicated a strong correlation between these gaps in PSM. Enterprises should pay attention to collaborative management among them. These deficiencies in the enterprise's PSM system showed that the essential defects in China's chemical industry are a poor safety culture, inadequate accident investigation, inadequate training, and a lack of chemical safety personnel. The study recommended that the chemical industry establish superior process safety culture and competency for all personnel, monitor leading and lagging process safety indicators, apply inherent safety, and practice advanced safety management concepts. We hope that the findings can provide China's perspectives and strengths for global chemical safety.@en

The increased complexity of digitalized process systems requires advanced tools to detect and diagnose faults early to maintain safe operations. This study proposed a hybrid model that consists of Kernel Principal Component Analysis (kPCA) and DNNs that can be applied to detect and diagnose faults in various processes. The complex data is processed by kPCA to reduce its dimensionality; then, simplified data is used for two separate DNNs for training (detection and diagnosis). The relative performance of the hybrid model is compared with conventional methods. Tennessee Eastman Process was used to confirm the efficacy of the model. The results show the reduction of input dimensionality increases classification accuracy. In addition, splitting detection and diagnosis into two DNNs results in reduced training times and increased classification accuracy. The proposed hybrid model serves as an important tool to detect the fault and take early corrective actions, thus enhancing process safety.@en

Natural gas leakage can impose significant danger on a facility and its surrounding communities. Methods for early detection and diagnosis of such leakages have been developed and widely used for gas pipelines and storage tanks. Most techniques include inspection of sensor-aided mathematical models. Application of machine learning techniques to gas leakage detection has been rarely explored. In the present work, convolutional network (to model spatial likelihood of leak) is combined with bi-directional long short-term memory layer network, or BiLSTM (to model temporal dependence of leak likelihood) to perform leak detection and diagnosis. The developed model was trained and tested using sequence of concentration profiles generated using open-source simulated data. The model learned successfully to predict gas leakage and classify its size. The study also explores the flexibility of this network to perform quick detection and diagnose with the limited data. While the networks did not require parameter adjustments to achieve high prediction accuracy, further optimization is possible through data selection and pre-processing. The model needs to be further tested for wide range of leak scenarios. At its present condition, the combined application of convolutional network and BiLSTM shows promising results for early and accurate leak detection in natural gas facilities. Experimental results are needed to confirm the effectiveness of the model and data uncertainty.@en

Digital technologies have been reshaping how the process industries operate. The extensive use of physical and information digitalization called by ambitious revolution to redesign process plants significantly transforms the process safety landscape in the process industries. Digitalization depends on the reliable use of data. This becomes a new focus of process safety in digitalized process systems. Digitalization brings the opportunity to generate and collect digital operational data and reduce human operation for effective process monitoring and control for safety assurance. The system's capability of processing massive data becomes essential for process safety. Meanwhile, digitalization increases the complexity of human-computer interaction and consequently leads to new research and practical problems. With standalone processes connected to the Internet of Things, process plants become more attractive to terrorists. This inevitably invites cybersecurity concerns to process safety solutions. This chapter provides a brief overview of these benefits and issues.@en

This paper investigates the linkage between the acute impacts on apex marine mammals with polar cod responses to an oil spill. It proposes a Bayesian network-based model to link these direct and indirect effects on the apex marine mammals. The model predicts a recruitment collapse (for the scenarios considered), causing a higher risk of mortality of polar bears, beluga whales, and Narwhals in the Arctic region. Whales (adult and calves) were predicted to be at higher risk when the spill was under thick ice, while adult polar bears were at higher risk when the spill occurred on thin ice. A spill over the thick ice caused the least risk to whale and adult polar bears. The spill's timing and location have a significant impact on the animals in the Arctic region due to its unique sea ice dynamics, simple food web, and short periods of food abundance.

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Subsea gas release is an industrial hazard that can impose fire hazards on offshore facilities near the gas surfacing area. However, risk assessment of the fire caused by subsea gas release is challenged due to inadequate recognition of the knowledge of subsea gas release mechanism and resulting hazards. At present, minimal researches involving risk assessment of offshore fire resulting from a subsea gas release were reported, and this paper is an extension of the previous works on subsea gas behavior. This paper focuses on modeling fire risk on offshore facilities due to subsea gas release. A numerical simulation is carried out using the Computational Fluid Dynamic technique of Fire Dynamics Simulator (FDS) to analyze fire propagation characteristics and assess the impact of fire on personnel and assets. A probit model is adopted to calculate the probabilities of injury or death caused by fire hazards. This study also investigates the effect of wind speed, gas release rate and the distance between gas pool and platform on fire impacts and casualty probabilities. The present study can support safety measure design to mitigate or avoid the impacts of offshore fire events from subsea gas release.

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Managed Pressure Drilling (MPD) system is widely used in the deepwater drilling operation. Reliability assessment plays a critical role in the MPD system in the management of drilling operation risk and the prevention of blowouts. However, the reliability assessment of the MPD system is challenged due to its sequential operations and multiple processes. Consequently, the present work proposes a sequence-based dynamic reliability assessment method, which focuses on the dynamic modeling of sequential operations for the MPD system by integrating GO-FLOW and dynamic Bayesian Network (DBN). GO-FLOW models are firstly used to define the time interaction between multiple phases for complex systems. A sequence-based mapping method is also proposed for the DBN to construct the reliability model of the MPD system throughout the entire drilling cycle. In the end, the case study analyzed by the proposed framework indicates that the reliability of the MPD system decreases with increasing drilling depth, and the reliability of “tripping in” is highest among four different phases, while the “drilling process” is the lowest. The method provides an important technique that can be implemented with online condition monitoring tools to assess and monitor the reliability of the MPD operation in real-time.

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Conventional risk assessment of chemical plants considers process accident related causal factors. In the current geopolitical situation, chemical plants have become the target of terrorism attacks, making security concerns as important as safety. To protect the public and environment from undue risks, security related causal factors need to be considered as part of the risk analysis of chemical plants. This paper presents an integrated approach to dynamically assess the occurrence probability of abnormal events. The abnormal event is a state of a process plant arrived either due to a process accident or an intentional (terrorist) threat. This approach considers both safety and security related risk factors in a unified framework. A Bayesian network is used to model specific evolution scenarios of process accidents directly initiated from security related factors and the interaction of causal factors. This model enables to dynamically analyze the occurrence probabilities of abnormal events and causal factors given evidence; it could also capture the impacts of interaction among safety and security related causal factors on these occurrence probabilities. The proposed approach is applied to an oil storage tank to demonstrate its applicability and effectiveness. It is observed that the effect of dependency between correlative accidental and security related factors significantly change the occurrence probability of abnormal events in dynamical assessment.@en

Processing facilities handling large amounts of hazardous substances are attractive targets for terrorists. Thus, these work sites are exposed not only to accidents but also to intentional threats. Some research has separately studied risk caused by either potential accidental events or terrorist acts. However, studies focusing on integrated risk assessment and management (dealing with both safety and security issues) are lacking. This paper proposes an approach to assess and manage integrated risks. This method is based on an influence diagram which incorporates safety and security-related factors into one framework. It considers the effects of management actions on both accidental and intentional risks. This method can help to detect hidden risk (i.e., the risk not recognized during design and operation stages) and ensure to reduce the real risk to an acceptable level by guiding the selection of management actions. The effectiveness of the proposed method is demonstrated using the overfilling risk management of an oil tank.

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The growing interest for Arctic and Antarctic shipping activities due to the decreasing ice cover will also increase the risks of accidents on these waters. The design of ships for ice has traditionally been based on the practical experience without a clear link to the physics of the ship-ice interaction. The rules are, however, getting more towards the goal based approach, which require good knowledge of all the various element important for design. Risk based ship design (RBSD) is also widely applied e.g. for the passengers ships. Therefore, the scope of this paper is the review of the knowledge necessary for RBSD for Arctic conditions. The main focus is on ice loads and ship structures. Accident prevention and environmental consequences of oil spills are also discussed, but more briefly. In risk analysis, there is a recent focus on the treatment of uncertainty, or conversely, the strength of knowledge underlying the risk quantification. In light of this, the review is performed with specific focus on the strength of evidence of the different fields of knowledge needed to perform RBSD in ice conditions. The results indicate that the risk based design for Arctic operations is challenging as the ice environment, together with all the possible ship-ice contact scenarios, are complicated to define properly, especially on proper probabilistic terms. The main challenges are still related how to describe the ship-ice interaction parameters such as ship-ice contact characteristics, pressure distributions, and load levels in all the various ice conditions. In addition, the possible environmental consequences of the accidents need further research. Finally, human factors need to be incorporated in risk analysis techniques.@en

The process industry is confronted with terrorism threats. Effective security management demands the ability to defend facilities against different intrusion scenarios. This study first presented various intrusion scenarios to explain the corresponding intrusion process using graphical barriers. Subsequently, this work dynamically analyzed the successful intrusion probabilities and security potentials of barriers using a Bayesian network considering the dependency of barriers and interaction of different intrusion scenarios. It was observed that successful intrusion probabilities and security potentials are strong functions of intrusion scenarios. Therefore, extensive intrusion scenarios must be considered while assessing and designing the security systems of process facilities.

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Fire remains a serious threat to a floating liquefied natural gas facility. It is of greater concern given the remote locations and limited accessibility of emergency services. This study aims to present a rigorous procedure to study potential accident scenarios in an offshore (floating processing) facility with different ignition source locations and verify the effectiveness of safety measures using computational fluid dynamics code. The uniqueness of the present study is the integration of release, dispersion and fire modeling scenarios, simplifying the fire analysis and increasing its effectiveness from the offshore process system design and analysis perspectives. The first step of the procedure is to identify the range of potential release scenarios and their strength of dispersion in confined and semi-confined spaces. Subsequently, potential fire scenarios are analyzed considering the influence of the location. Computational fluid dynamics models are used to analyze these three steps of the scenarios. Application of the procedure is demonstrated on an offshore facility by analyzing 14 credible scenarios. The ranges of safety measures of these fires are also studied to determine their effectiveness to prevent fires and mitigate their impact. This study provides a simple and efficient way to analyze the impact of key design parameters. In this study, the transition from fire to explosion is not considered and all the environmental factors are assumed to be constants in the simulation.

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This paper proposes a dynamic economic risk analysis methodology for process systems. The Bayesian Tree Augmented Naïve Bayes (TAN) algorithm is applied to model the precise and concise probabilistic dependencies that exist among key operational process variables to detect faults and predict the time dependent probability of system deviation. The modified inverted normal loss function is used to define system economic losses as a function of process deviation. The time dependent probability of system deviation owing to an abnormal event is constantly updated based on the present state of the relevant process variables. The integration of real time probability of system deviation with potential losses provides the risk profile of the system at any instant. This risk profile can be used as the basis for operational decision making and also to activate the emergency safety system. The proposed methodology is tested and verified using the Richmond refinery accident.

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Exposure to Technologically Enhanced Naturally Occurring Nuclear Radioactive Material (TENORM) from oil and gas drilling and production activities can have effects on both the environment and workers involved in the industry. There is a significant lack of available information regarding dynamic modeling and risk assessment of TENORM occupational exposure in the oil and gas industry, and available studies show that workers in the field are at risk of being exposed to varying levels of radiation. This paper presents a methodology to bridge this knowledge gap by modeling workforce TENORM radiation exposure at different oil and gas operation stages. This was achieved by integrating SHIPP (System Hazard Identification, Prediction and Prevention) Methodology And Rational Theory (SMART approach). The SMART approach was applied to develop an integrated framework for TENORM occupational exposure risk assessment. Application of the proposed approach is illustrated with a scenario, and outcomes from modeling this scenario explain how system degraded as a function of safety barrier performance.

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Offshore drilling involves complex operations and equipment; thus, faces many operational challenges, including well control. Managed pressure drilling has been proved to resolve most of these challenges; however, this technology, for the most part, is still in its infancy. This paper explores the safety and reliability assessment of a managed pressure drilling operation by investigating the kick control operation of constant bottomhole pressure technique of managed pressure drilling. In addition, this study seeks to understand the components interactions in an MPD system and their modes of failure. Failure scenarios are first built using a Fault Tree model and then analyzed using a Bayesian Network model. The reliability assessments of kick control operation are performed in two ways: a basic-components approach and a system-barrier elements approach. The analysis identifies communication related components, including network device damage as the most safety-critical component due to their high-level of influence while flowline and pump line blockages/rupture are ranked second-most critical but with limited-level of influence. However, the system-barrier element approach ranks the managed pressure drilling control system as the most safety-critical equipment. Further detailed analysis confirms that the monitoring equipment are the most safety-critical components of the managed pressure drilling control system with Coriolis flow meter and Rig pump exhibiting the most critical monitoring equipment. Additionally, the managed pressure drilling system's components show high degree of dependencies on one another and exhibit non-sequential modes of failure during kick control operation.

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Offshore oil and gas processing equipment operating in harsh environments poses high risk. This risk is further increased by the susceptibility of the equipment to natural disasters such as hurricanes and snowstorms due to harsh environments. When equipment functionality is compromised, it can become a hazard to personnel as well as to other equipment. The key safety practice on the offshore facility is to isolate the equipment and minimize consequences associated with processing equipment failures. When and how to isolate vulnerable equipment is a challenge due to limited understanding of the equipment's susceptibility and dependency to failure causes and consequences. This paper presents a methodology to analyze potential failure scenarios considering causation dependency and also determine which parameter(s) have the most impact on the failure. The results of the analysis are used to identify most sensitive equipment and their potential failure causes. This analysis will help to develop effective risk management strategies focusing on critical equipment.

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The Arctic Ocean has drawn major attention in recent years due to its rich natural resources and shorter navigational routes. Arctic development and transportation involve significant risk caused by the unique features of this region, such as ice, severe operating conditions, unpredictable climatic changes, and remoteness. Considering the high degree of uncertainty in the performance of vessel operating systems and humans, robust risk analysis and management tools are required to provide decision-support to prevent accidents and ensure safety at sea. This paper proposes an Object-Oriented Bayesian Network model to dynamically predict ship-ice collision probability based on navigational and operational system states, weather and ice conditions, and human error. The model, when integrated with potential consequences, may help estimate risk. A case study related to oil tanker navigation on the Northern Sea Route (NSR) is used to show the application of the proposed model to predict oil tanker collision with sea ice.

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In this paper, a conceptual framework is developed to improve NaTech safety in the chemical industry. The concept is called EPIC, indicating that emphasis should be put on Education, learning and training, Proactive risk minimization and safety innovation, Intensified informed inspection and analysis, and Cooperation and transparency. Concrete initiatives addressing NaTech for every domain of the EPIC conceptual framework are given. The innovativeness of the EPIC framework resides in the potential of the simultaneous application of initiatives within the four areas of improvement (E, P, I, and C) to make chemical clusters much more resilient with respect to nature-related disasters. As such, the proposed EPIC framework may lead to a much-needed revolution of NaTech safety in the chemical industry.

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A model that can evaluate the ecological risk posed to the Arctic marine ecosystem is presented in this paper. The proposed model is aimed at evaluating the risk of an accidental oil release. The model incorporates a release and dispersion model, fate and transport model, and ecotoxicological modelling. Uncertainties in the proposed model and data are addressed through a probabilistic framework implemented using a fugacity model to estimate the exposure concentration in the different media that are in contact with oil. This is the focus of this paper. The 95th percentile of Predicted Exposure Concentration (PEC_95%) is compared with the 5th percentile of the Predicted No Effect Concentration (PNEC_5%) to produce a Risk Quotient (RQ) profile, which indicates the level of risk posed to the Arctic marine ecosystem. The application of the proposed model is illustrated through a case study. The RQ obtained is useful for making decisions on the management of safety for Arctic marine ecosystems, such as setting operational goals to prevent accidents and for designing emergency preparedness plans. The uniqueness of this work in comparison to ealier studies is that, the methodology takes into account all the significant component models needed to address a potential oil spill in a probabilistic way and demonstrated in an Arctic setting. This study also shows that the methodology is useful as a first step to decision making in the absence of data on accidental releases in the Arctic marine waters.

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