1,720,983 research outputs found
From risk assessment to resilience assessment. an application to a hazmat storage plant
No full textThe purpose of this work is to outline a framework for assessing the resilience of a petrochemical storage plant, through the construction of a dynamic hierarchical Bayesian network. The BN approach allows keeping memory of the states, in order to manage the actual safety and reliability evidences during the petrol transfer operation from storage tank to trucks in a repository of oil products. The proposed framework aims at assessing risk in process plants by analysing continuous process hazard data from a Bayesian point of view. A sequence of hazard functions derived for the FTAs, is modelled with a hidden Markov chain. The capability of the model implemented by means of Markov Chain Monte Carlo methods are tested at a real scale plant. Keywords: Data driven model, hidden Markov models, resilience, semi-supervised learning,
Critical aspects of natural gas pipelines risk assessments. A case-study application on buried layout
No full textThe safety aspects of pipelines conveying hazardous materials are included neither under the umbrella of Seveso Directives aiming at preventing major accidents at industrial facilities, nor in other EU legislations, such as the Pressure Equipment Directive (PED). Starting from evidence that in the last decades the international natural gas market has been growing at a very high rate and continues to exhibit an increasing trend, in this paper we focus on consequences deriving from accidents on high pressure buried Natural Gas Pipelines (NGP) and related probabilities of the various outcomes. A survey on historical accidents occurred on NG pipelines in the USA, Canada and EU allowed the attainment of significant statistics concerning the main factors responsible for the accident evolution, namely failure mode, immediate and root cause, evolving scenario, degree of confinement produced by the surroundings and ignition timing. In this paper, we focus on a refined Event Tree framework, to overcome the limitations of the amply applied over-conservative IP UKOOA approach. In order to evidence the capability of the approach, the use of refined PET is exemplified by means of a real case-study of a high pressure buried NG pipeline, contrasting the actual results with those obtained by conventional methods, in terms of evolving scenario probability and damage. Conclusions are drawn about the effective application of the framework within risk assessment and the uncertainties and sensitivities in the pipeline accident modelling
Risk assessment of buried natural gas pipelines. Critical aspects of event tree analysis
No full textThe safety aspects of pipelines conveying hazardous materials are not included neither under the umbrella of Seveso Directives aiming at preventing major accidents at industrial facilities, nor in other EU legislation such as the Pressure Equipment Directive (PED). A review of relevant past accidents can provide statistical evidence on the extent to which pipelines present a risk potentially comparable to that of Seveso installations and on the degree to which the pipeline hazards are adequately controlled. Starting from evidence that in the last decades, the international natural gas market has been growing at a very high rate and continues to exhibit an increasing trend, in this paper we focus on consequences deriving from accidents on high pressure buried Natural Gas Pipelines (NGP) and related probabilities of the various outcomes. The paper focuses on a novel Event Tree framework, to overcome the limitations of the amply applied over-conservative IP UKOOA approach. In order to evidence the capability of the approach, the use of refined PET is exemplified by means of a real case-study of a high pressure buried NG pipeline, contrasting the actual results with those obtained by conventional methods, in terms of evolving scenario probability and damage. Conclusions are drawn about the effective application of the framework within risk assessment and related uncertainties in pipeline accident modelling
Process safety management quality in industrial corporation for sustainable development
No full textIn recent years, also in connection with Covid‐19 pandemics and enforced restrictions, there has been the formation of large industrial corporations gathering separate companies with similar, sometimes complementary production profiles. This evolving trend has brought usually positive economic effects; however, it has also created some integration problems that include the process safety management. The Texas City BP accident in 2005 and its tremendous human and economic losses underlined the obstacles in defining a well‐structured corporation process safety management. The main causes of the above‐mentioned accident were connected to an inadequate safety culture at the managerial level. Strong leadership and high standards of corporate governance are required to inspire correct safety behavior in the staff. The so‐called soft skills become even more important in the Industry 4.0 arena, where the foundation of the whole system is based on an intelligent use and interpretation of data. The importance of this aspect is confirmed by several post-accidental analyses of past events. Although some research on this topic has been already done, it is worth it to dedicate some effort to identifying specific factors which influence the corporate process safety management quality, and, once identified, to assess them. This paper applies the concept of “lessons learnt” for the identification of organizational and managerial aspects worth consideration in process safety management. Based on accident and literature reviews and expert opinions, the aim is to identify the major contributing factors among leadership and safety culture, risk aware-ness, knowledge and competence, communication, and information and decision‐making processes. To self‐assess the level of commitment of the top leaders in process safety management, a checklist approach is proposed, combined with a quantitative, weighted evaluation based on the Relative Efficiency Indicator (REI). Positive value of REI may ensure the effectiveness of process safety management in major hazard industries and their appropriate adaptation to the corporation community. The proposed method, which is validated in an actual case study, underlines the importance of an appropriate education, and of a more careful selection of HSE managers
A Dynamic Approach to Fault Tree Analysis based on Bayesian Beliefs Networks
No full textAccording to the Seveso Directives, the risk assessment is crucial for an effective control of major accident hazard. Nevertheless, the complexity of many Seveso sites, due to human, technical and organizational factors makes recognized common practices limited because of their intrinsic static nature. In this paper, a dynamic approach for risk assessment is proposed, which allows evaluating moment by moment the state of the system under analysis by Bayesian belief networks. A petrochemical coastal storage was selected as applicative case-study to verify the capability of the dynamic approach. Network training is performed by entering historical reliability data, near-miss and accidents data series collected on-site by periodical inspection plans on critical elements, as well as from the evidences of SMS reports. Upon proper refinement and further validation with reliable field data, the predictive approach may be used as a management decision-making tool
A data driven model for ozone concentration prediction in a coastal urban area
No full textAs amply known, ozone concentration in the coastal area of study is well relevant in connection with photochemical smog, due to high levels of solar radiation and temperature values and possible photochemical oxidation of volatile organic compounds (VOCs) in the presence of nitrogen oxides (NOx). In this paper, a framework for predicting ozone concentration in urban area is presented, relying a LightGBM algorithm for gradient boosting on decision trees. The system represents a pragmatic and scientifically credible approach to data driven modelling applied to complex and uncertain situations. The study concerns the application of data analytic standard methodologies to air quality analysis, which includes the pretreatment of data, the choice of a suitable configuration of the learning algorithm, the identification of the fitting parameters and error minimization. Training and verification data are significant statistical time-series over the past years validated from the air quality monitoring network in the urban area of Genoa (Italy). Keywords: Air quality, data driven model, machine learning, ozone, environmental quality
Oil Spill Identification and Monitoring from Sentinel-1 SAR satellite earth observations: A machine learning approach
No full textIdentification of an oil spill is essential to evaluate the potential spread and float from the source to coastal terrains, and their continued monitoring is essential for managing the environmental protection actions to confine the pollution and avoid further damage. The SAR sensor is perceived as the most significant remote sensing apparatus for the oil slick examination. One of the main aspects of oil spreading over sea surface is that it dampens the capillary waves and so, the backscatter radio waves are suppressed. As a result, oil spills are represented as black spots, while the brighter regions are usually related with unspoiled polluted sea areas. Additionally, the wide coverage that the sensor can provide is highly significant including long-range fate, as well as contextual information, such as sensitive coastal areas or vessels, which can be enclosed in the acquired image. However, oceanic natural phenomena such as low wind speed regions, weed beds and algae blooms, wave shadows behind land, grease ice, etc. can also be depicted as dark spots. These dark regions are commonly categorized as "look-Alikes and their discrimination is very challenging. Machine Learning techniques are the most appropriate choice to classify oil spills and look-Alikes. In the present work, a comparison between decision trees models and NN is performed to identify and extract the appropriate set of features characterizing an oil spill allowing effective evolution monitoring and setting up proper emergency actions
The impact of the COVID-19 pandemic on the safety management in Italian Seveso industries
No full textThe paper discusses the impact of the COVID-19 pandemic on the Italian chemical and process industries, where Directive 2012/18/EU Seveso III, for the control of Major Accident Hazard (MAH), is enforced. The Safety Management System (SMS) for the control of MAH, which has been mandatory for 20 years in Italian Seveso Establishments, has been highly stressed by the external pressure, related in some way to the COVID-19 pandemic. Fairly, most companies, in particular in oil and gas sectors, have demonstrated an adequate capability to reconcile operation continuity and health requirements. This experience is providing the establishment operators and the regulators with valuable suggestions for the improvements of the SMS-MAH. Within this framework, an innovative organisational resilience model is proposed, aiming at the development of a higher capability to face future new crisis. The current SMS-MAH already includes some basic pillars to enhance resilience, which were valuable during the pandemic crisis, but a full and rationale development is still needed. Starting from the first pandemic phase experience, this paper presents a novel tool to assess the degree of “resilience” of a SMS-MAH. It is based on a questionnaire, featuring 25 questions grouped into eight items, according to the typical SMS-MAH structure. A two level AHP model has been developed in order to define the weights to be assigned to each point. The AHP panel included industrial practitioners, regulators, authorities and researchers. The results are based on the COVID-19 experience and consequently the developed model is tailored to face health emergencies, but the approach may be easily transferred to other external crises
Accessibility for maintenance in the engine room: development and application of a prediction tool for operational costs estimation
No full textWhen dealing with maintenance in the ship's engine room, the space available around machinery and systems plays an important role. A proper clearance is usually indicated by the system supplier to perform maintenance operations. This space depends on the items dimensions, the kind of intervention and on the human operator, to avoid uncomfortable or dangerous positions. However sometimes the limited space in the engine rooms (as in warships, passenger ships, research vessels) implies critical issues in complying with such ideal clearances. This work aims to develop a tool to define a relation between the maintenance costs increase and the clearance reduction, regarding a single item and/or for the whole system. This tool improves the decision-making process during the design of engine room’s layout, enabling the comparison among different solutions in terms of operational costs. The approach relies on data-driven models and Bayesian inference. The predictive tool, inserted on the Systems Engineering methodology, has been tested on a real case
Hazardous Spray Release from a Pipeline under Maintenance: Causes and Lessons Learned by a Combined Accident Analysis Perspective
No full textAs widely acknowledged, learning from accidents represents one of the main source of knowledge for future loss prevention. An effective investigation may help enhancing the process of continuous improvement of the safety management system. Recently, during a pipeline batching operation between two storage facilities of the same corporation, a LOC from the flange caused a spary release of atomised diesel, impacting on the adjacent national road. Two complementary approaches for accident investigation are here considered, i.e., customized root cause analysis workflow and Causal Analysis using System Theory approach. The best approach lies in the systemic nature of the selected methods applied to the whole socio-technical hierarchy of the concerned process trying to improve on one hand existing hazard analysis and on the other hand accident analysis. The paper outlines the fact-finding process from the technical viewpoint, as well as preconditions and latent failures
- …
