Hazardous events in process plants like the leakage of dangerous substances can result in severe damage, and such an event is often defined as the TOP event of a fault tree analysis (FTA) in a quantitative risk analysis. The TOP event probability can then be calculated if the basic events probabilities are provided. These probabilities are often determined based on generic reliability data which do not necessarily reflect the operational and environmental characteristics of a plant of interest. This paper presents an approach based on Bayesian network (BN) analysis to explicitly include experience data collected during the plant operation to make the generic probabilities more plant specific. The approach is illustrated via a pressure vessel containing a toxic substance in an Ammonia production plant. In this case study, the failure rate distribution in the BN is updated as the new information becomes available during plant operation. The results show that the suggested approach effectively reflects the operating experience of a specific plant.@en

Emergency response planning for coping with domino effects at chemical and process plants is essential to protect the public and workers’ health and safety, reduce the environmental impacts, and accelerate the resumption of normal operations. The need for domino effects emergency response becomes even more critical in the case of chemical industrial areas (chemical clusters) since a primary fire or explosion at a single plant may trigger external domino effects and impact the entire cluster. So far, much attention has been addressed to developing and implementing emergency planning in single plants with very little attention to multiplant emergency response planning in chemical clusters. This chapter is aimed at developing a multiplant emergency response decision support system for coping with domino effects at chemical clusters. In this way, a primary fire or explosion within the chemical cluster can be effectively handled before it evolves as a domino effect.@en

Emerging risk models are still scarce and far from agreed upon. They are currently the focus of increasing interest in the occupational context. Consequently, frameworks that deal with emerging risk management in industrial contexts are very recent or, even still, in the development and maturation stage. Uncertainty should be considered as the main characteristic of emerging risk in this context. It is as such that the main objective of this paper is to develop a qualitative approach inspired by meta-learning lessons to the selection of strategies for emerging risk management, considering uncertainty as the main decision variable in occupational contexts. To this end, uncertainty has been integrated, as a combination of knowledge and understanding, in a theoretical framework on emerging risk. An emerging risk classification scheme has been developed with the results obtained. This scheme makes it possible to estimate the level of emerging risk and management strategies based on the combination of uncertainty and the potential consequences of emerging risk. Such approach has been applied to three case studies with different evolutionary degrees of emerging risk: exoskeletons; nanomaterials; and industrial automation. The proposed approach could be considered primarily as a qualitative tool applicable to the process of pre-assessment and communication of emerging risk.

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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.

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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.

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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.

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As a result of the important and rapid technological advances in industrial processes, in recent years the scientific interest in the field of occupational health and safety (OH&S) is increasing. This scenario of continuous change generates both new opportunities and new challenges in the workplace. This circumstance allows eliminating or minimizing familiar or traditional risks, but it can also generate other risks called emerging risks. Recently the ISO 45001:2018 standard has been published, which specifies the requirements for an OH&S management system. ISO 45001 standard includes some aspects related to the new hazards, for example during the hazard identification process and the change management process. However, the standard does not explicitly distinguish between the concepts of emerging risk and traditional risk. In this way, the general objective of this paper is to identify the main differences between emerging risk management and traditional risk management in the context of the management system defined by ISO 45001. This standard has been analyzed together with other management systems and relevant scientific literature to achieve the stated objective. All this has been aligned with the results previously published by the authors of this work, within the framework of a research project on emerging risks. As the main conclusion of this work, the monitoring and assessment process of the emerging risk uncertainty should be integrated into the iterative process Plan-Do-Check-Act (PDCA) used by organizations to achieve continual improvement.@en

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.

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Accident investigations in process industry indicate that inadequate barrier management has been one of the main causes of many major accidents. Barriers degrade over time and at different rates, and these degradations may gradually and unnoticeably drift the system towards a state of high risk. Conventional barrier management approaches apply fixed inspection and maintenance intervals with little direct influence on daily operations to evaluate safety barriers performance. Such issues are addressed by the dynamic barrier management (DBM) concept through the combination of all available information, such as inspection, preventive maintenance, audit, sensors, process control, and near misses or incident records. DBM main goal is to infer barrier status in near real-time and evaluate its impact on risk level. However, the framework for DBM is not detailed and easy to implement and therefore requires further development to clarify the steps. An approach based on system thinking is suggested. Systems engineering (SE) is a suitable approach for managing complex problems by considering the big picture and the SPADE framework from SE is applied to provide a practical roadmap for DBM.

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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.

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The publication in 2018 of ISO 45001 is the first international ISO standard in the field of occupational health and safety management systems. ISO 45001 is the result of an international consensus on the subject and describes the best international preventive practices and incorporates the requirements of a management system aligned with the so-called High-Level Structure of the ISO standards of management systems. Simultaneously, ISO/IEC 31010:2009 provides for the selection and application of systematic techniques for risk assessment. However, this standard does not address safety in a specific way. It is a generic standard for risk management and any reference to safety is simply informative. Thus, the main objective of the present work is to identify and classify the main techniques included in the ISO/ IEC 31010:2009 standard applicable in the field of occupational safety and in line with the requirements for the ISO Standard 45001. As a secondary objective, it is sought in the same context to identify the main non-standard techniques of new or emerging nature. This process of identification and classification has been carried out by means of a systematic review of the scientific literature specialized in the matter. The results have been classified according to bibliometric indicators in the following groups: (a) Main standard techniques for application to occupational safety; (b) Main techniques developed through specific standards (such as: IEC 61882:2016 – Hazard and operability studies [HAZOP studies] – Application guide; IEC 61025:2006 – Fault Tree Analysis [FTA]); (c) Main non-standard techniques of a new or emerging nature for application to occupational safety. Finally, the results obtained by the classification mentioned above have been analyzed in order to determine the degree of coverage and standardization of the main risk assessment techniques applied to occupational safety.

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Several recent events raised the attention toward possible major accidents triggered by external acts of interference in industrial facilities. In particular, a growing concern is present with respect to the intentional release of dangerous substances resulting in environmental and eco-terroristic attacks. Therefore, optimal selection and allocation of preventive security measures is becoming more important for decision-makers. Despite the existence of economic models supporting the decision-making process, their applications within the chemical industry security context are relatively limited. This study describes a specific model for economic analysis and selection of physical security measures, with respect to potential environmental and eco-terroristic attacks in chemical facilities. An example of application to a relevant case study is presented to show the model capabilities. Site-specific analysis of the baseline physical security system performance allows comparing the costs of different security upgrades with the benefits related to either prospective or retrospective losses, meanwhile accounting the uncertainties related to the threat probability. Selection of the most profitable security measures within budget constraints and definition of economic indicators are the main outputs of the model, in order to support decision-making processes for allocation of security barriers.

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The accident that occurred in Seveso in 1976 changed our approach to risk assessment forever. For instance, it led to drafting specific European directives to prevent major accident hazards. Their focus is on industrial sites handling dangerous substances and denominated “Seveso sites”. Their operators have the obligation to provide specific information to the competent authorities, such as safety reports. However, risk analysis studies usually provide a static picture of the site status, while the system constantly evolves or degrades. For this reason, this contribution suggests a dynamic risk analysis approach aiming to continuously calibrate and improve, based on new related evidence and lessons learned. The work suggests focusing on such early deviations to lower the probability of high impact low probability (HILP) events. Three complementary methods may be used to process such information: dynamic hazard identification, dynamic analysis of initiating events, and dynamic analysis of consequences. A representative example of their potential is provided by comparing their capabilities with the causes that led to the Seveso catastrophe. Despite its limitations, Dynamic Risk Analysis represents an opportunity for improved decision-making support and critical risk communication.

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In the last few years, several events have highlighted the risk of possible major accidents triggered by terrorist attacks within chemical and process facilities. Due to the increased attention for security issues, an optimal allocation of security measures, including related cost issues, becomes progressively more important. Despite the existence of economic models for supporting decision-making processes in general, for instance cost-benefit and cost-effectiveness analyses, no specific economic models are available in the domain of operational security (including counter-terrorism decision-making) to be applied within the chemical and process industry. In this study, a novel method for cost-benefit and cost-effectiveness analysis for the allocation of physical security measures was elaborated and tested using a case study. Starting from a site-specific analysis of the baseline physical security system performance, the model allows comparing and evaluating the costs of different security upgrades with the (hypothetical) benefits related to the avoided losses. The approach developed enables selecting economically feasible security measures, or a combination of such measures with a maximum net present value, within the budget constraints of a chemical plant.@en

Dynamic safety analysis in chemical and process facilities is necessary to prevent unwanted events that may cause catastrophic accidents. Probability updating and adapting of stochastic events and dynamic processes, which evolve over time, are the key to dynamic safety analysis. Conventional risk assessment techniques, such as fault tree, event tree, and bow-tie analyses, have long been used for effective safety analysis of process plants; however, owing to their static characteristics, their application to dynamic safety analysis has been relatively limited. Bayesian methods, such as hierarchical Bayesian analysis and Bayesian network, are effective techniques with ample potential for application in dynamic safety analysis. This chapter is aimed at presenting the state-of-the-art application of Bayesian analysis and especially the Bayesian network method in dynamic safety analysis of process systems.@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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In this chapter an innovative approach is described to evaluate investments in safety measures under an economics perspective. By accurate risk and safety assessment it is possible to assign costs and benefits to a safety measure. In this way, the foundation of robust and long-sighted risk and safety analysis can be built for operational safety within any organization. Decision-making might be significantly improved by prioritizing and refocusing on safety rather than on short-term organizational objectives. Cost-benefit analysis can be used to evaluate investment decisions by translating safety and risk concepts into monetary values integrating well-known indicators and measures from economic theory such as net present value, internal rate of return, and payback period.@en

This chapter is intended to address cost-benefit analysis of safety investments aimed at controlling low-probability, high-impact accidents. Such events are characterized by high uncertainty owing to relative lack of data, and challenges may be encountered in the calculation of the related economic measures. For this reason, the notions of cost and benefit need to be broadened. A disproportion factor may be used as an adjustment in favor of safety when partial quantitative data about costs and benefits are available. When quantitative data are not available or qualitative measures associated with the safety investments are to be evaluated by decision-makers, cost-benefit analysis based on a multicriteria decision-making approach is suggested. Both the techniques are described by means of tutorials and specific examples of application.@en