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One of the main barriers to the adoption of fuel cell vehicles (FCEVs) is the limited availability of hydrogen refuelling stations (HRSs). The presence of these stations is crucial in facilitating the provision of fuel for FCEVs, which rely on hydrogen as a source of power generation. Renewable energy sources offer significant advantages for hydrogen production at these stations, as they are environmentally friendly and can reduce costs. In this study, it is provided a techno-economic analysis of an on-site hydrogen refuelling station powered by a hybrid renewable energy generation system using HOMER software in Niğde, Türkiye, and Zaragoza, Spain. Three different power system scenarios were evaluated to refuel 24 vehicles per day for each region throughout the year. The results of the analysis showed that the most optimal system architecture for Niğde was with a solar panel power generation system, with a levelized cost of hydrogen (LCOH) of 6.15 $/kg and the net present cost (NPC) of $6,832,393. The most optimal system architecture for Zaragoza was a wind turbine-photovoltaic panel power generation system, with an LCOH of 5.83 $/kg and NPC of $6,499,723. The annual amount of CO 2 emissions avoided by using renewable resources in hydrogen production was calculated as 2,673,453 kg for Niğde and 2,366,573 kg for Zaragoza. The study also found that the cost of hydrogen production increases with decreasing the HRS production capacity. The use of renewable energy generation systems for hydrogen production will enable countries to achieve net-zero emission targets and reduce the need to import fossil fuels to meet energy demands in the transportation sector. The present study makes several contributions towards the achievement of the United Nations Sustainable Development Goals (3, 7, 11, and 13) and has the potential to facilitate the rapid adoption of FCEVs. • An on-site production hydrogen station is proposed for fuel cell vehicles. • Hydrogen production cost is chosen as the optimization objective. • Levelized Cost of Hydrogen is estimated from 5.84 to 10.87 $/kg. • The factors affecting HRS costs are investigated using sensitivity analysis. • Hydrogen production on-site integrated with renewable power systems is economically viable. [ABSTRACT FROM AUTHOR]

Hydrogen is one of the main candidates in replacing fossil fuels in the forthcoming years. However, hydrogen technologies must deal with safety aspects due to the specific substance properties. This study aims to provide an overview on the loss of integrity (LOI) of hydrogen equipment, which may lead to serious consequences, such as fires and explosions. Substantial information regarding the hydrogen lifecycle, its properties, and safety related aspects has gathered. Furthermore, focus has placed on the phenomena responsible for the LOI (e.g. hydrogen embrittlement) and material selection for hydrogen services. Moreover, a systematic review on the hydrogen LOI topic has conducted to identify and connect the most relevant and active research group within the topic. In conclusion, a significant dearth of knowledge in material behaviour of hydrogen technologies has highlighted. It is thought that is possible to bridge this gap by strengthening the collaborations between scientists from different research fields. • A systematic review on loss of integrity of hydrogen technologies was conducted. • Loss of integrity phenomena are responsible for several hydrogen accident scenarios. • Material selection may avoid the loss of integrity phenomena formation. • Material science and hydrogen safety are often two separated research fields. • Recent hydrogen accidents demonstrate the need for safety enhancement. [ABSTRACT FROM AUTHOR]

• We suggest a risk assessment approach based on machine learning. • A deep neural network (DNN) model is developed and tested. • A drive-off scenario involving an Oil & Gas drilling rig was considered. • Results show reasonable accuracy and suitability of DNN for risk assessment. • Nevertheless, model selection and customization should be carefully carried out. Risk assessment has a primary role in safety-critical industries. However, it faces a series of overall challenges, partially related to technology advancements and increasing needs. There is currently a call for continuous risk assessment, improvement in learning past lessons and definition of techniques to process relevant data, which are to be coupled with adequate capability to deal with unexpected events and provide the right support to enable risk management. Through this work, we suggest a risk assessment approach based on machine learning. In particular, a deep neural network (DNN) model is developed and tested for a drive-off scenario involving an Oil & Gas drilling rig. Results show reasonable accuracy for DNN predictions and general suitability to (partially) overcome risk assessment challenges. Nevertheless, intrinsic model limitations should be taken into account and appropriate model selection and customization should be carefully carried out to deliver appropriate support for safety-related decision-making. [ABSTRACT FROM AUTHOR]

In Occupational Safety and Health (OSH) and operational safety, confined spaces are high-risk working areas, where frequent serious and fatal incidents occur. However, there is a limited use of data-driven approaches based on Machine Learning (ML) techniques for learning from such incidents. In this context, our study proposes a systematic approach to develop a structured database suitable for ML-based analyses, considering various data sources with unstructured or semi-structured data. The approach was applied to 1346 incidents that happened in confined spaces mainly containing chemicals and located in manufacturing and process industries. This allowed building models predicting the fatal or non-fatal consequences and direct causes of incidents. The recognition of the approach potentialities and difficulties stimulated the development of an improved version of it to be adapted to different safety-related undesired events. The generalised systematic approach supports safety analysts during the process of learning from undesired events to maximise the lessons learned. [ABSTRACT FROM AUTHOR]

Oil and gas installations in sensitive areas with harsh environmental conditions may require improved risk management. Intensified monitoring, assessment and mitigation of risk on a (quasi-) real time basis would advantage not only the operators, but also the surrounding environment. A systematic tool for continuous quantitative evaluation of safety and environmental issues is still lacking. The present work introduces a novel methodology for the integrated assessment of human and environmental risk. A dynamic perspective is adopted to systematically consider the performance of safety barriers. Environmental risk is further investigated by using the risk matrix approach, which evaluates both frequency and severity of oil spill. The methodology is applied to the case of a real oil platform in the Barents Sea. A set of simulations on how the platform is conducted demonstrated that the proposed method may be suitable for risk analysis in such critical conditions. It also showed that dynamic risk assessment may allow identifying critical safety barrier elements, whose correct performance needs to be prioritized to control risk. This is also supported by environmental risk assessment, showing that further safety measures may be considered for biological and environmental conservation. [ABSTRACT FROM AUTHOR]

Air emissions from fishing vessels must be reduced to comply with progressively stringent environmental regulations. Among the available solutions, liquefied natural gas (LNG) fuel may represent a promising solution, particularly from an environmental perspective. However, the use of LNG as a marine fuel creates different types of hazards than those that exist for traditional fuels. In addition, the increased complexity, safety requirements, and space required for LNG installation increase the capital cost. This article uses a systems engineering approach to clarify the technical aspects of LNG-fuelled systems, their potential implementation costs, and the expertise and training required to operate them safely. Ship owners can use such an approach to aid decision-making and trade-off analyses. Naval architects may also benefit from better information management. Finally, crews may better understand the logic behind the safety actions they are instructed to take. [ABSTRACT FROM AUTHOR]

The objective of this review is to analyse the progress of Risk Assessment during the last decades and to offer an overview on its recent advancements and possible future direction for chemical and process industries. Despite the general approach of Quantitative Risk Assessment (QRA) is unchanged since its origin in the early 1980s, QRA has continuously evolved in different forms and its fields of application have enlarged significantly beyond process safety, where it has always been traditionally developed and used for chemical process industries. Now risk assessment techniques play a fundamental role in process design, implementation of safety systems, inspection and maintenance planning as well as operation management. Eventually risk assessment has become an essential tool for the development, continued operation and expansion of process installations. On the other hand, QRA limitations, such as its inability to update the risk picture, led to the development of several recent dynamic risk assessment approaches, whose methodological and applicative contributions are presented in this paper. This demonstrates that risk assessment is in continuous development; nevertheless, it still shows many challenges to face: the way forward is improving its preciseness and its capability to be dynamically updated, that it will be useful to support real-time decision-making. [ABSTRACT FROM AUTHOR]

The likelihood of leaks from process equipment is a key input to any Quantitative Risk Assessment. This study is aimed at developing a methodology supporting the tailorization of leak frequency values. Specific modification factors for the facilities of the Oil & Gas (O&G) upstream sector are used for this purpose. The method (TEC2O--frequency modification methodology based on TEChnical Operational and Organizational factors) is based on an aggregated set of indicators, whose contribution to the expected leak frequency is systematically evaluated through a specific procedure. Periodic revision and update represents an added value to the method, because it may be adopted to drive the identification of critical safety issues in a facility, integrating technical and managerial aspects and supporting continuous monitoring. The method is compared with similar literature methods and applied to a representative case study in order to demonstrate its potential and advantages. [ABSTRACT FROM AUTHOR]

Emerging risks may arise from process intensification and new scenarios due to the innovative technologies and higher potentialities of new LNG regasification facilities. In the conventional hazard identification process it is difficult to include new scenarios related to innovative technologies or facilities, for which limited or no operational experience is available. In the present study, a new technique for HAZard IDentification (HAZID), named Dynamic Procedure for Atypical Scenarios Identification (DyPASI), was applied to identify atypical accident scenarios in LNG terminals. The technique aims to make easier and more systematic the process of learning from early warnings and identify atypical accident scenarios otherwise disregarded by common HAZID techniques. The comparison with a survey of the accident scenarios typically considered in available Environmental Impact Assessment (EIA) studies evidences that DyPASI is a valuable tool to obtain a complete and updated overview of potential hazards in particular for new or innovative technologies, where limited operational experience is available. [ABSTRACT FROM AUTHOR]

Several major accidents caused by metal dusts were recorded in the past few years. For instance, in 2011, three accidents caused by iron dust killed five workers at the Hoeganaes Corp. facility in Gallatin, Tennessee (USA). In order to prevent such accidents, a dynamic approach to risk management was defined in this study. The method is able to take into account new risk notions and early warnings and to systematically update the related risk. It may be applied not only in the design phase of a system, but also throughout the system lifetime as a support to a more precise and robust decision making process. The synergy of two specific techniques for hazard identification and risk assessment was obtained: the Dynamic Procedure for Atypical Scenarios Identification (DyPASI) and the Dynamic Risk Assessment (DRA) methods. To demonstrate its effectiveness, this approach was applied to the analysis of Gallatin metal dust accidents. The application allowed collecting a number of risk notions related to the plant, equipment and materials used. The analysis of risk notions by means of this dynamic approach could have led to enhanced hazard identification and dynamic real-time risk assessment. However, the approach described is effective only if associated to a proper safety culture, in order to produce an appropriate and robust decision making response to emerging risk issues. [ABSTRACT FROM AUTHOR]

With the advent of Carbon Capture and Sequestration (CCS) technology the extent of CO2 handling is set to increase dramatically. However, lack of substantial operational experience in such a novel process can lead to significant difficulties in identifying the associated hazards. This field may be characterized by atypical accident scenarios, i.e. scenarios not captured by common HAZard IDentification (HAZID) techniques because of omissions, errors or lack of knowledge. Recent atypical events evidence that consequences may exceed by far those of worst-case reference scenarios. Identification of atypical scenarios related to CCS is a challenge, considering also the public concern that this technology raises. This study focuses on new and emerging technologies of carbon capture and transport. A HAZID analysis was carried out by means of two different approaches ('top-down' and 'DyPASI'). This allowed not only for a double check of results, but also for the comparative assessment of the methodologies and of their applicability. A general overview of the accident scenarios related to these technologies was given. No absolute showstoppers were found. Rather, a number of potential hazards were identified which will require the adoption of safe design principles to eliminate, prevent, control or mitigate them. Some possible safety barriers required for implementation were identified as a starting point in this process. [ABSTRACT FROM AUTHOR]

Abstract: Some severe major accidents occurred in Europe in recent years (e.g. the Vapour Cloud Explosion at Buncefield in 2005), which were not foreseen by their site “Seveso-II” safety reports. Detailed analyses of such “atypical” scenarios demonstrated that they are the result of a number of failures at different technical and organizational levels. Thus, their prevention is a major challenge and must be coordinated through different kinds of approaches, among which improved early detection plays an important role. Proactive methodologies for the development of early warning indicators can unveil early deviations in the causal chain. Two examples are the Resilience-based Early Warning Indicator (REWI) method and the so-called “Dual Assurance” method. The aim of this study was to analyse the possible integration of early warning indicators in the hazard identification process. A Buncefield-like site was analysed to obtain indicators that were compared with the actual causes that led to the accident at Buncefield (and to similar accident scenarios). The results show that indicators from both methods could have prevented the accidents from happening. However, one main difference is related to the issue of hazard identification, which is fundamental for the prevention of atypical accident scenarios. The REWI method is not dependent on the outcome of the hazard identification process. Instead it provides complementarities to the first prevention approach (improved identification of atypical scenarios), demonstrating that a mutual activity would be an effective strategy in which human, organizational, cultural and technical factors are treated in an integrated manner. [Copyright &y& Elsevier]

The European iNTeg-Risk project is a large-scale integrated project aimed at improving the management of emerging risks related to new technologies in European industry. The project aims to build a new management paradigm for emerging risks as a set of principles supported by a common language, agreed tools and methods, and key performance indicators, all integrated into a single framework. It is using a number of Emerging Risk Representative Applications (ERRAs), or case studies, to inform the development of the framework; one of which concerns the carbon capture and storage (CCS) process. This paper describes the iNTeg-Risk CCS ERRA. Relevant hazards and properties of carbon dioxide are described and the emerging risks from CCS are discussed. Three new tools have been developed or trialled within the ERRA. These are: the DyPASI methodology for taking account of atypical (not usually identified) events during hazard identification; a methodology for including the time dimension in a risk assessment; and life-cycle approaches for risk management and communication. For CCS, the risk assessment needs to include both short-term potential accidents from capture, transport or injection, as well as very long-term risks from storage. Knowledge gaps which are generic to emerging risks are also identified. [ABSTRACT FROM AUTHOR]

The potential reduction of risk in LPG (Liquefied Petroleum Gas) road transport due to the adoption of passive fire protections was investigated. Experimental data available for small scale vessels fully engulfed by a fire were extended to real scale road and rail tankers through a finite elements model. The results of mathematical simulations of real scale fire engulfment scenarios that may follow accidents involving LPG tankers proved the effectiveness of the thermal protections in preventing the “fired” BLEVE (Boiling Liquid Expanding Vapour Explosion) scenario. The presence of a thermal coating greatly increases the “time to failure”, providing a time lapse that in the European experience may be considered sufficient to allow the start of effective mitigation actions by fire brigades. The results obtained were used to calculate the expected reduction of individual and societal risk due to LPG transportation in real case scenarios. The analysis confirmed that the introduction of passive fire protections turns out in a significant reduction of risk, up to an order of magnitude in the case of individual risk and of about 50% if the expectation value is considered. Thus, the adoption of passive fire protections, not compulsory in European regulations, may be an effective technical measure for risk reduction, and may contribute to achieve the control of “major accidents hazards” cited by the European legislation. [Copyright &y& Elsevier]

• Complex failure of Viking Sky cruise ship, in 2019, Hustadvika, Norway. • Learning from the Viking Sky's failure through a systematic approach - CAST. • Accident/failure precursors and resilience indicators which impacted Viking Sky. • Power generation capacity of Viking Sky was under the design propulsion power. • Digital twin based condition monitoring as a preventive action in maritime industry. This article brings to attention learning from the failure - blackout, loss of propulsion and near grounding - of Viking Sky cruise ship which occurred in Hustadvika, Norway, in March 2019. Failures and accidents in the cruise ship industry attract the global media and can severely impact reputation and business performance of companies and authorities involved. A system approach investigation and analysis - CAST - was employed with the aim to maximize learning from the Viking Sky's failure through a systematic approach and to contribute to failure reduction in the cruise ship industry. Three main recommendations emerged from this study: an overview of the accident or failure precursors and resilience indicators; safety recommendations for other cruise ships; lessons and strategies of actions for the increased cruise operations in the Arctic and Antarctic areas. It was found that several accident or failure precursors, for example, a low level of lubricating oil, the failure of a turbocharger, an inoperative large diesel generator, lack of functionality for safety equipment due to bad weather, and others precursors contributed to failure and highly critical situation encountered by Viking Sky in Hustadvika. Resilience indicators such as the master's immediate decision to launch mayday, the crew preparedness, and the way how the emergency situation was handled were found to have positive impacts on critical situation of Viking Sky. This article highlights also that adaptations and improvement of standards and regulations for harsh environmental conditions can play an important role in prevention of marine accidents. Furthermore, for a better understanding of correlation between environmental loads and their effects on machinery systems, digital solutions such as digital twin for condition monitoring of cruise ships in the Polar areas are seen as possible innovative solutions yet to be fully implemented in the marine industry. [ABSTRACT FROM AUTHOR]

• We propose ProMetaUS, a novel proactive framework based on meta-learning concepts. • ProMetaUS ranks models for Dynamic Risk Management considering uncertainty. • Meta-features from datasets and performance measures of models are defined. • A case study about models assessing ODH in working environments is presented. • The results are obtained through widely spread tools, e.g. Microsoft Excel®. Safety managers, practitioners, and researchers can employ different models for estimating and assessing hazards, consequences, likelihoods, risks, and/or mitigation measures in the safety field. The selection of a specific model may depend on the uncertainty associated with its estimation and its impact on the safety-related decision-making process. The recognition of this issue as an example of Algorithm Selection Problem (ASP) allows investigating the applicability of meta-learning principles that are scarcely adopted in the risk and safety literature. Consequently, we propose a novel meta-learning inspired framework to proactively rank a set of candidate models for Dynamic Risk Management (DRM) based on desired uncertainty conditions. We denominate this framework ProMetaUS (Proactive Meta-learning and Uncertainty-based Selection for dynamic risk management). To achieve this purpose, our meta-learning system acquires knowledge that relates the characteristics extracted both directly and indirectly from datasets (e.g. data-based, domain-based, simple and fast uncertainty-based, simple and fast sensitivity-based meta-features) to some performance measures of the models. Performance measures include confidence information, shape measurable quantities, safety decision criteria and threshold limits, and sensitivity analysis outputs. We tested the proposed framework in a case study about Oxygen Deficiency Hazard (ODH) assessment by means of @RISK. For each of the five datasets, single-performance measure rankings and a final ranking of the three models are generated. Such rankings are aggregated to obtain the global recommended ranking. [ABSTRACT FROM AUTHOR]

• A new method for detecting and quantifying alarm chatter is proposed. • Machine Learning is used to predict alarm chatter based on actual process conditions. • Three Classification algorithms are trained and compared: Linear, Deep, Wide&Deep. • Machine Learning can be used to exploit historical data and improve process safety. • The method is presented and discussed using an industrial alarm database. Alarm floods represent a widespread issue for modern chemical plants. During these conditions, the number of alarms may be unmanageable, and the operator may miss safety-critical alarms. Chattering alarms, which repeatedly change between the active and non-active states, are responsible for most of the alarm records within a flood episode. Typically, chattering alarms are only addressed and removed retrospectively (e.g. during periodic performance assessments). This study proposes a Machine-Learning based approach for alarm chattering prediction. Specifically, a method for dynamic chattering quantification has been developed, whose results have been used to train three different Machine Learning models – Linear, Deep, and Wide&Deep models. The algorithms have been employed to predict future chattering behavior based on actual plant conditions. Performance metrics have been calculated to assess the correctness of predictions and to compare the performance of the three models. [ABSTRACT FROM AUTHOR]

• Accidents of marine structures on Norwegian Continental Shelf over 40 years. • Accident investigation based on life-cycle research approach applied on WOAD. • 96% of accidents occurrence were linked with operation and 4% with installation. • 13% of accidents linked to marine operations in both operation and installation. • Jackets and semi-submersibles were identified with highest number of accidents. This paper investigates accidents, major accidents and disasters which occurred on the Norwegian Continental Shelf (NCS) over a period of more than 40 years time (1972-2013). An accident investigation based on the system life-cycle was applied on the data provided by the World Offshore Accident Database (WOAD) where the operation (in-service) stage of the life-cycle was found to be the stage with 96% and the installation stage with 4% of accident occurrences. The marine operations linked to both installation and the operation (in-service) stages are identified to be where 13% of accidents had occurred. In terms of structural types, jackets and semi-submersibles are identified with the highest number of accidents, while the highest rate of accidents per marine structure type is linked to the concrete structures where in average 5.5 accidents per each concrete structures were recorded. 1980 was the year with the highest number of fatalities on NCS within 40 years time span with the occurrence of Alexander L. Kielland disaster. There has been a reduction of number of fatalities over the years, but injuries had always been present. It was found that possible correlations can be established among occurrence of accidents and environmental loads for some months. The results and discussions contributes to learning from the 40 years accidents on the NCS with the aim of risk reduction in operation of marine structures. The predictive and preventive maintenance strategy and condition monitoring during operation (in-service) stage for each individual marine structure is promoted. However, the uncertainty is still present and risk can never be reduced to zero. [ABSTRACT FROM AUTHOR]

The failure of emission control systems in industrial processes undergoing emission regulations can cause severe harm to the environment. In this context, safety engineering principles can be applied to analyze process deviations and identify suitable safety barriers to mitigate harmful emissions during critical events. However, the selection, design, and assessment of proper safety barriers may be complex due to several contingencies such as the inability to perform extensive field tests on systems under strict emission regulations. In this study, an approach is proposed to couple conventional hazard identification techniques with a digital model of a flue gas treatment system to support the identification and performance assessment of safety barriers for emission control. Resilience analysis is used to evaluate the behavior of the most relevant safety barrier options, selected through a screening with conventional hazard identification tools. Barriers are simulated using the digital model of the system, gathering key information for their design and evaluation, and overcoming the limitations to field tests at the real plant. The methodology is illustrated with reference to acid gas removal in waste-to-energy facilities, a relevant example of an emission control system that is typically exposed to significant process deviations. [ABSTRACT FROM AUTHOR]

Hydrogen could gradually replace fossil fuels, mitigating the human impact on the environment. However, equipment exposed to hydrogen is subjected to damaging effects due to H 2 absorption and permeation through metals. Hence, inspection activities are necessary to preserve the physical integrity of the containment systems, and the risk-based (RBI) methodology is considered the most beneficial approach. This review aims to provide relevant information regarding hydrogen embrittlement, its effect on materials' properties, and the synergistic interplay of the factors influencing its occurrence. Moreover, an overview of predictive maintenance strategies is presented, focusing on the RBI methodology. A systematic review was carried out to identify examples of the application of RBI to equipment exposed to hydrogenated environments and to identify the most active research groups. In conclusion, a significant lack of knowledge has been highlighted, along with difficulties in applying the RBI methodology for equipment operating in a pure hydrogen environment. • The occurrence of hydrogen-induced material failures requires safety enhancements. • Hydrogen embrittlement relies on a synergistic interplay of several factors. • Tailored inspection activities could aid to monitor equipment exposed to hydrogen. • RBI approaches are not used for planning inspections of hydrogen technologies. • Material science and safety are often considered two separated research fields. [ABSTRACT FROM AUTHOR]

There is an increasing interest in risk and safety research to reflect risk fluctuations in operational phase to avoid major accidents from happening. A structured review that covers operational risk assessment, dynamic risk assessment and real-time risk assessment has been conducted to clarify the concept of operational risk that needs to be assessed under operational risk assessment. It is found that different terms are used in different ways, covering different purposes. The finding reveals a need to strengthen the link between risk assessment methodologies and supporting decisions by clarifying what needs to be measured for what purpose. Operational risk assessment, dynamic risk assessment, and real-time risk assessment have their key aspects to address, namely what information we need to make decisions, how the analysis is updated and how the data required for updating is gathered. The paper recommends using the terms in a consistent way to facilitate a common understanding. [ABSTRACT FROM AUTHOR]

• Neural networks are used to predict the time-to-failure of atmospheric tanks • Unmitigated and mitigated fire scenarios are considered • Minimal input data and low computation resources are required for predictions • The models overperform standard empirical correlations used in current practice Domino scenarios triggered by fire pose severe risks to workers, assets, and the environment. Accurate quantitative models are needed to support mitigation actions addressing the prevention of fire escalation, especially considering sensitive targets such as atmospheric tanks containing large quantities of dangerous substances. A novel approach based on neural networks was developed, allowing the accurate quantification of the time-to-failure (TTF) of atmospheric tanks exposed to external fires accounting for mitigation actions. Data from a lumped parameter model were used to train and assess neural networks' performance. The toolbox of models obtained provides the TTF of atmospheric tanks both in the case of unmitigated fire scenarios and considering safety barriers and protection measures, such as water deluges and fire monitors. Model predictions are fast, accurate, and supplemented with confidence intervals. The comparative analysis demonstrated the better performance of the model developed compared to simplified correlations widely used in the literature to predict TTF. The approach developed, based on the integration of neural networks in consequence analysis tools, shows significant potential for the advancement of a quantitative assessment of domino scenarios, providing accurate and user-friendly tools for a quick evaluation of domino fire scenarios under both mitigated and unmitigated conditions. [ABSTRACT FROM AUTHOR]

• A data-driven method to predict the severity of chemical accidents is proposed. • Machine learning is used to link accident features to severity categories. • Historical accident data is used to train the model. • The method is demonstrated in a test case study. • Three classification algorithms are compared: Linear, Deep, wide&deep. Learning from past mistakes is crucial to prevent the reoccurrence of accidents involving dangerous substances. Nevertheless, historical accident data are rarely used by the industry, and their full potential is largely unexpressed. In this setting, this study set out to take advantage of improvements in data science and Machine Learning to exploit accident data and build a predictive model for severity prediction. The proposed method makes use of classification algorithms to map the features of an accident to the corresponding severity category (i.e., the number of people that are killed and injured). Data extracted from existing databases is used to train the model. The method has been applied to a case study, where three classification models – i.e., Wide, Deep Neural Network, and Wide&Deep – have been trained and evaluated on the Major Hazard Incident Data Service database (MHIDAS). The results indicate that the Wide&Deep model offers the best performance. [ABSTRACT FROM AUTHOR]

This paper is written in response to the paper "How green is blue hydrogen?" by R. W. Howarth and M. Z. Jacobson. It aims at highlighting and discussing the method and assumptions of that paper, and thereby providing a more balanced perspective on blue hydrogen, which is in line with current best available practices and future plant specifications aiming at low CO2 emissions. More specifically, in this paper, we show that: (i) the simplified method that Howarth and Jacobson used to compute the energy balance of blue hydrogen plants leads to significant overestimation of CO2 emissions and natural gas (NG) consumption and (ii) the assumed methane leakage rate is at the high end of the estimated emissions from current NG production in the United States and cannot be considered representative of all‐NG and blue hydrogen value chains globally. By starting from the detailed and rigorously calculated mass and energy balances of two blue hydrogen plants in the literature, we show the impact that methane leakage rate has on the equivalent CO2 emissions of blue hydrogen. On the basis of our analysis, we show that it is possible for blue hydrogen to have significantly lower equivalent CO2 emissions than the direct use of NG, provided that hydrogen production processes and CO2 capture technologies are implemented that ensure a high CO2 capture rate, preferably above 90%, and a low‐emission NG supply chain. [ABSTRACT FROM AUTHOR]

Risk assessment and management are some of the major tasks of urban power-grid management. The growing amount of data from, e.g., prediction systems, sensors, and satellites has enabled access to numerous datasets originating from a diversity of heterogeneous data sources. While these advancements are of great importance for more accurate and trustable risk analyses, there is no guidance on selecting the best information available for power-grid risk analysis. This paper addresses this gap on the basis of existing standards in risk assessment. The key contributions of this research are twofold. First, it proposes a method for reinforcing data-related risk analysis steps. The use of this method ensures that risk analysts will methodically identify and assess the available data for informing the risk analysis key parameters. Second, it develops a method (named the three-phases method) based on metrology for selecting the best datasets according to their informative potential. The method, thus, formalizes, in a traceable and reproducible manner, the process for choosing one dataset to inform a parameter in detriment of another, which can lead to more accurate risk analyses. The method is applied to a case study of vegetation-related risk analysis in power grids, a common challenge faced by power-grid operators. The application demonstrates that a dataset originating from an initially less valued data source may be preferred to a dataset originating from a higher-ranked data source, the content of which is outdated or of too low quality. The results confirm that the method enables a dynamic optimization of dataset selection upfront of any risk analysis, supporting the application of dynamic risk analyses in real-case scenarios. [ABSTRACT FROM AUTHOR]

Hydrogen is one of the most suitable candidates in replacing fossil fuels. However, storage issues due to its very low density under ambient conditions are encountered in many applications. The liquefaction process can overcome such issues by increasing hydrogen's density and thus enhancing its storage capacity. A boiling liquid expanding vapour explosion (BLEVE) is a phenomenon in liquefied gas storage systems. It is a physical explosion that might occur after the catastrophic rupture of a vessel containing a liquid with a temperature above its boiling point at atmospheric pressure. Even though it is an atypical accident scenario (low probability), it should be always considered due to its high yield consequences. For all the above-mentioned reasons, the BLEVE phenomenon for liquid hydrogen (LH2) vessels was studied using the CFD methodology. Firstly, the CFD model was validated against a well-documented CO2 BLEVE experiment. Secondly, hydrogen BLEVE cases were simulated based on tests that were conducted in the 1990s on LH2 tanks designed for automotive purposes. The parametric CFD analysis examined different filling degrees, initial pressures and temperatures of the tank content with the aim of comprehending to what extent the initial conditions influence the blast wave. Good agreement was shown between the simulation outcomes and the LH2 bursting scenario tests results. [ABSTRACT FROM AUTHOR]

Hydrogen is one of the most suitable candidates in replacing fossil fuels. However, storage issues due to its very low density under ambient conditions are encountered in many applications. The liquefaction process can overcome such issues by increasing hydrogen's density and thus enhancing its storage capacity. A boiling liquid expanding vapour explosion (BLEVE) is a phenomenon in liquefied gas storage systems. It is a physical explosion that might occur after the catastrophic rupture of a vessel containing a liquid with a temperature above its boiling point at atmospheric pressure. Even though it is an atypical accident scenario (low probability), it should be always considered due to its high yield consequences. For all the above-mentioned reasons, the BLEVE phenomenon for liquid hydrogen (LH 2) vessels was studied using the CFD methodology. Firstly, the CFD model was validated against a well-documented CO 2 BLEVE experiment. Secondly, hydrogen BLEVE cases were simulated based on tests that were conducted in the 1990s on LH 2 tanks designed for automotive purposes. The parametric CFD analysis examined different filling degrees, initial pressures and temperatures of the tank content with the aim of comprehending to what extent the initial conditions influence the blast wave. Good agreement was shown between the simulation outcomes and the LH 2 bursting scenario tests results. [ABSTRACT FROM AUTHOR]

Abstract: Major accidents are low frequency high consequence events which are not well supported by conventional statistical methods due to data scarcity. In the absence or shortage of major accident direct data, the use of partially related data of near accidents – accident precursor data – has drawn much attention. In the present work, a methodology has been proposed based on hierarchical Bayesian analysis and accident precursor data to risk analysis of major accidents. While hierarchical Bayesian analysis facilitates incorporation of generic data into the analysis, the dependency and interaction between accident and near accident data can be encoded via a multinomial likelihood function. We applied the proposed methodology to risk analysis of offshore blowouts and demonstrated its outperformance compared to conventional approaches. [Copyright &y& Elsevier]

Safety Improvement of engineering processes, especially Oil & Gas operations, has gained a lot of attention during the last decades. This fundamental vision results in risk remediation programs, minimizing the risks of failure, and reducing the associated costs for operation and maintenance. As failures may represent serious threats for both humans and the environment, a comprehensive tool is required to employ maintenance and avoid immoderate dangerous consequences. Traditional risk frameworks mainly include estimation approaches, such as Fault Tree (FT) and Event Tree (ET), producing more simplified models than other tools, such as Bayesian inference. The present work aimed at developing an advanced Risk-Based Maintenance (RBM) methodology for prioritizing maintenance operations, by addressing associated uncertainties through the accident modelling of the process. For this purpose, a Hierarchical Bayesian Approach (HBA) is applied to estimate the failure probabilities of each component while a Failure Mode, Effects and Criticality Analysis is performed to assess the severity. With Markov Chain Monte Carlo simulation from likelihood function and prior distribution, the HBA is capable of incorporating the fluctuations and uncertainties associated with operational data including the variability between the source of data and the correlation of observations. Lastly, to make a meaningful difference between different kinds of risk consequences, whether the risk has a direct or indirect loss, the cost of failures of components is accounted for. To demonstrate the application of the methodology, a Natural Gas Reduction and Measuring Station (NGRMS) is taken into account as a case study. The outcome of the case study proofed that PTG and pump are the most failures sensitive components among other if they being left unattended in the operation with an average number of failure occurrences of 67 and 45; While the THT pipelines and THT tank are less sensitive for being considered for major maintenance request with almost average of 5 times in their lifetime. The proposed method can be exploited by maintenance engineers, asset managers, and policymakers to reduce the downtime periods as well as the risks of on-going operations. [ABSTRACT FROM AUTHOR]

• This paper investigates HRA literature and provides clarity through bibliometric analyses. • The approach combines factor analysis, multi-dimensional scaling and bibliometric mapping. • Main documents, authors and terms were identified by reviewing over 1200 contributions. • Current research streams and the potential for future research were outlined. Humans play a crucial role in modern socio-technical systems. Rooted in reliability engineering, the discipline of Human Reliability Analysis (HRA) has been broadly applied in a variety of domains in order to understand, manage and prevent the potential for human errors. This paper investigates the existing literature pertaining to HRA and aims to provide clarity in the research field by synthesizing the literature in a systematic way through systematic bibliometric analyses. The multi-method approach followed in this research combines factor analysis, multi-dimensional scaling, and bibliometric mapping to identify main HRA research areas. This document reviews over 1200 contributions, with the ultimate goal of identifying current research streams and outlining the potential for future research via a large-scale analysis of contributions indexed in Scopus database. [ABSTRACT FROM AUTHOR]

• Applying Bayesian regression to predict the risk condition in an autonomous process. • Addressing the involved and, most of the time, unconsidered risks. • Simulating the likelihood function using Monte Carlo Markov Chain approach. • quantifying the uncertainties associated with operational data using Bayesian model. • The model was examined on Natural Gas Regulating and Metering Stations. The probabilistic analysis on condition monitoring data has been widely established through the energy supply process to specify the optimum risk remediation program. In such studies, the fluctuations and uncertainties of the operational data including the variability between source of data and the correlation of observations, have to be incorporated if the efficiency is of importance. This study presents a novel probabilistic methodology based on observation data for signifying the impact of risk factors on safety indicators when consideration is given to uncertainty quantification. It provides designers, risk managers and operators a framework for risk mitigation planning within the energy supply processes, whilst also assessing the online reliability. These calculations address the involved and, most of the time, unconsidered risk to make a prediction of safety conditions of the operation in future. To this end, the generalized linear model (GLM) is applied to offer the explanatory model as a regression function for risk factors and safety indicators. Hierarchical Bayesian approach (HBA) is then inferred for the calculations of regression function including interpretation of the intercept and coefficient factors. With Markov Chain Monte Carlo simulation from likelihood function and prior distribution, the HBA is capable of capturing the aforementioned fluctuations and uncertainties in the process of obtaining the posterior values of the intercept and coefficient factors. To illustrate the capabilities of the developed framework, an autonomous operation of Natural Gas Regulating and Metering Station in Italy has been considered as case study. [ABSTRACT FROM AUTHOR]

• Applying Non-Homogenous Poisson Process (NHPP) and Homogenous Poisson Process (HPP) to predict the condition of a system. • Presenting the possible differentiation within failure assessment of random processes. • Investigating the effect of utilizing observed data in inter-arrival failure time modelling. • The model was examined on degradation process of the Natural Gas Regulating and Metering Stations (NGRMS). Failure modelling and reliability assessment of repairable systems has been receiving a great deal of attention due to its pivotal role in risk and safety management of process industries. Meanwhile, the level of uncertainty that comes with characterizing the parameters of reliability models require a sound parameter estimator tool. For the purpose of comparison and cross-verification, this paper aims at identifying the most efficient and minimal variance parameter estimator. Hierarchical Bayesian modelling (HBM) and Maximum Likelihood Estimation (MLE) approaches are applied to investigate the effect of utilizing observed data on inter-arrival failure time modelling. A case study of Natural Gas Regulating and Metering Stations in Italy has been considered to illustrate the application of proposed framework. The results highlight that relaxing the renewal process assumption and taking the time dependency of the observed data into account will result in more precise failure models. The outcomes of this study can help asset managers to find the optimum approach to reliability assessment of repairable systems. [ABSTRACT FROM AUTHOR]

• Comprehensive risk analysis is necessary for Oil&Gas activities in the Arctic. • Bayesian Networks for safety barrier assessment may be used for the Goliat platform. • This method results show accurate modelling of potential accident scenarios. • Outcome sensitivity to data quality and quantity is a method limitation. Oil&Gas activities in the arctic and subarctic regions are characterized by several challenges related to the harsh but sensitive environment in which they are carried out. The weather may deteriorate facility components at a higher rate, and delay operations, emergency and evacuation procedures. Moreover, these regions host unique ecosystems, and their preservation is a worldwide priority. For this reason, a comprehensive and systematic approach for risk analysis is necessary to prevent major accidents and comply with Arctic pollution control. A novel approach for dynamic risk assessment and management, based on Bayesian Networks and safety barrier assessment, is suggested. The method is applied to the Goliat Oil&Gas platform located in the Barents Sea and risk data on the Norwegian petroleum activities are used as evidence to simulate continuous update of risk assessment throughout the years. The case study shows the benefits and limitations of such approach. Accurate modelling of potential accident scenarios is possible through BNs, but time-consuming. The approach allows for drill-down capabilities, which enhance support of operations and definition of risk mitigating measures. However, the data used for dynamic risk assessment has a pivotal role, as data quality and quantity sensibly affect the outcome. Fortunately, the Oil&Gas industry is committed to improving collection of field data for the assessment of safety barrier performance. This approach represents a strategy to process deviations and resilient reactions, regularly iterating dynamic risk assessment to support risk management of critical systems, such as the Oil&Gas production in the arctic and sub-arctic regions. [ABSTRACT FROM AUTHOR]

Hydrogen is a clean substitute for hydrocarbon fuels in the marine sector. Liquid hydrogen (L H 2) can be used to move and store large amounts of hydrogen. This novel application needs further study to assess the potential risk and safety operation. A recent study of L H 2 large-scale release tests was conducted to replicate spills of L H 2 inside the ship's tank connection space and during bunkering operations. The tests were performed in a closed and outdoor facility. The L H 2 spills can lead to detonation, representing a safety concern. This study analyzed the aforementioned L H 2 experiments and proposed a novel application of the random forests algorithm to predict the oxygen phase change and to estimate whether the hydrogen concentration is above the lower flammability limit (LFL). The models show accurate predictions in different experimental conditions. The findings can be used to select reliable safety barriers and effective risk reduction measures in L H 2 spills. • A new application for predicting liquid hydrogen release consequences is proposed. • Random forests are used to predict oxygen phase change and hydrogen concentration. • The method is presented and discussed using large-scale release tests. • The findings can be used to improve process safety in liquid hydrogen spills. [ABSTRACT FROM AUTHOR]

• Common traits of 628 hydrogen-related undesired events reported in HIAD 2.0. • Business analytics tools for understanding hidden information in a safety dataset. • Lessons learned from reported failures of hydrogen technologies. • Necessity to update the existing recommended practices for inspection planning. Hydrogen has the potential to make countries energetically self-sufficient and independent in the long term. Nevertheless, its extreme combustion properties and its capability of permeating and embrittling most metallic materials produce significant safety concerns. The Hydrogen Incidents and Accidents Database 2.0 (HIAD 2.0) is a public repository that collects data on hydrogen-related undesired events mainly occurred in chemical and process industry. This study conducts an analysis of the HIAD 2.0 database, mining information systematically through a computer science approach known as Business Analytics. Moreover, several hydrogen-induced material failures are investigated to understand their root causes. As a result, a deficiency in planning effective inspection and maintenance activities is highlighted as the common cause of the most severe accidents. The lessons learned from HIAD 2.0 could help to promote a safety culture, to improve the abnormal and normal events management and to stimulate a widespread rollout of hydrogen technologies. [ABSTRACT FROM AUTHOR]

Fire in the topside production area of offshore Oil and Gas (O&G) platforms has the potential to trigger cascading events and may lead to catastrophic consequences for operators, environment and asset. Safety barriers, such as hardware systems and emergency response, are critical elements aimed at preventing the propagation of this type of scenarios. Harsh environmental conditions may strongly affect the integrity of protective equipment and significantly hinder an effective and timely emergency response, with a consequent increase in the risk level. The present study is aimed at developing a structured methodology to the quantitative performance assessment of safety barriers, specifically addressing the analysis of offshore facilities operating in harsh and sensitive environment. An expert judgment elicitation procedure is adopted to score external factors associated with harsh environmental conditions. Simplified relationships are established for availability and effectiveness evaluation of safety barriers in offshore O&G platforms. A dedicated framework is developed for the analysis of emergency response. The approach is tested through the application to a case study, aimed at the assessment of the influence of harsh environmental conditions on the risk due to cascading events in a reference offshore installation. [ABSTRACT FROM AUTHOR]

Liquid hydrogen (LH2) spills share many of the characteristics of liquefied natural gas (LNG) spills. LNG spills on water sometimes result in localized vapor explosions known as rapid phase transitions (RPTs), and are a concern in the LNG industry. LH2 RPT is not well understood, and its relevance to hydrogen safety is to be determined. Based on established theory from LNG research, we present a theoretical assessment of an accidental spill of a cryogen on water, including models for pool spreading, RPT triggering, and consequence quantification. The triggering model is built upon film-boiling theory, and predicts that the mechanism for RPT is a collapse of the gas film separating the two liquids (cryogen and water). The consequence model is based on thermodynamical analysis of the physical processes following a film-boiling collapse, and is able to predict peak pressure and energy yield. The models are applied both to LNG and LH2, and the results reveal that (i) an LNG pool will be larger than an LH2 pool given similar sized constant rate spills, (ii) triggering of an LH2 RPT event as a consequence of a spill on water is very unlikely or even impossible, and (iii) the consequences of a hypothetical LH2 RPT are small compared to LNG RPT. Hence, we conclude that LH2 RPT seems to be an issue of only minor concern. [ABSTRACT FROM AUTHOR]

A solid oxide fuel cell-based (SOFC) power generation plant is analyzed with a view to performance as well as thermo-mechanical stresses. Emphasis is given to design and operation constraints of individual components as well as control-relevant aspects. We use a multi-physics modeling approach for simulations in design and off-design (part-load) under steady-state and transient conditions. Simulations confirm that the temperature distribution plays a pivotal role. For the SOFC the electrolyte is exposed to higher stresses compared to those in the electrodes. Disturbances in operation, e.g. in terms of current extraction, should be avoided because of resulting temperature excursions. This systems engineering perspective in which performance, durability issues and control aspects are combined, provides additional insights on a system level of practical relevance. [ABSTRACT FROM AUTHOR]

We use a detailed solid oxide fuel cell (SOFC) model for micro-grid applications to analyze the effect of failure and degradation on system performance. Design and operational constraints on a component- and system level are presented. A degrees of freedom analysis identifies controlled and manipulated system variables which are important for control. Experimental data are included to model complex degradation phenomena of the SOFC unit. Rather than using a constant value, a spatially distributed degradation rate as function of temperature and current density is used that allows to study trajectory-based performance deterioration. The SOFC unit is assumed to consist of multiple stacks. The failure scenario studied is the loss of one individual SOFC stack, e.g. due to breakage of sealing or a series of fuel cells. Simulations reveal that degradation leads to significant drifts from the design operating point. Moreover, failure of individual stacks may bring the still operating power generation unit into a regime where further failures and accelerated degradation is more likely. It is shown that system design, dimensioning, operation and control are strongly linked. Apart from specific quantitative results perhaps the main practical contribution are the collected constraints and the degrees of freedom analysis. • System analysis of SOFC-based plant combining design, control and reliability aspects. • System simulation of degradation effects and failure scenario. • Parameter-dependent degradation rates with ohmic and activation polarization loss contributions. • Collection of design and operational constraints on plant level. • Set of controlled and manipulated variables of system for control. [ABSTRACT FROM AUTHOR]

• Risk management. • Oil and gas. • System engineering. In recent years, environmental protection has received more attention in society. In addition to the ecological effect of a hydrocarbon release into the sea, its environmental impact is assessed by considering the amount of pollutant discharged. Therefore, limiting the impact of a spill consists in lowering the volume of oil released and effective early detection is necessary for implementation of mitigating measures. Standards and guidelines have been established for developing effective sensor networks in the subsea templates for both monitoring purposes and data collection. Sensors provide various and heterogeneous amount of information about the subsea template they are monitoring. According to recent definitions of risk, the level of knowledge should be considered during the risk assessment and evaluation phases for better managing potential impacts. The information provided by sensor networks indeed may be used in this perspective. For example, sensor functionality may be included in dynamic fault tree analysis in updating the information about system deviations' frequency. The work in this paper is focused on risk management using information provided from subsea sensor networks. The study adopts a top-down approach inspired by systems engineering to analyse the communication patterns among the different stakeholders. A real reference case from the oil and gas industry located in an environmentally sensitive area on the Norwegian Continental Shelf is used for testing the suggested approach. The case study refers to subsea monitoring of oil leakages from the wellhead templates. Different sensor configurations are considered in order to identify the one able to provide the most reliable information. Insights from the case study highlight how sensor data analysis may improve risk management and support operational decision making. [ABSTRACT FROM AUTHOR]

A Belgian manufacturing company uses pallet movers for internal transport. Despite the company's efforts to improve occupational safety, accidents with pallet movers remain noteworthy. In order to control occupational accidents, it is crucial to have a clear view of the potential accident scenarios that are present in a company. The bow-tie method is a way to capture and visualize these accident processes in an integrative way. Included in the bow-tie are safety barriers (both technical as organizational and human) and management delivery systems that can intervene in these accident processes. Once bow-ties are composed, they are an excellent point of departure to assign indicators to the safety barriers and management delivery systems in order to control (i.e., prevent or mitigate) accident scenarios. Two types of indicators can be distinguished. Firstly, there are general indicators that are assigned to management delivery systems interrupting multiple accident scenarios, which can yield a higher safety gain (as they intervene in multiple accident scenarios). Secondly, there are scenario-specific indicators targeting one specific accident scenario, which can be valuable as they target a specific problem in the company. For the development of the bow-ties, a multi-method design with the inclusion of different data sources was used, leading to a comprehensive overview. This makes the bow-tie analysis of internal transport with pallet movers transferable to other settings where pallet movers are used for internal transport. [ABSTRACT FROM AUTHOR]