1,720,980 research outputs found
DARMS - Dynamic Asset-integrity and Risk Management System - How Machine Learning and Systems Engineering cooperate to enhance the resilience of complex systems
No full text“Static, incomplete, superficial, wrong”. The traditional approach to risk analysis, as applied in the process industries, has been largely criticized in response to recent major accidents. Since it was first proposed, modifications and improvements have been made, and a formal accepted approach is included in several regulations and standards (as the recent development of guidelines for the ageing management in SEVESO installations). Quantitative Risk Assessment (QRA) is based on consolidated procedures.
Nevertheless, the need of safety improvement asks for more advanced tools for hazard identification and risk evaluation. Besides considering technical aspects (e.g., malfunctions and process upsets), operational errors, organizational aspects, such as lack of attention and motivation to the safety culture, may lead to risk increment in terms of likelihood of undesired failures. Not all those aspects may be investigated with conventional QRA techniques, which have also the disadvantage of being intrinsically static and failing to capture risk variations during the lifecycle of a plant or production site. Despite their proved effectiveness, many hazards identification and risk assessment techniques lack the dynamic dimension, which is the ability to learn from new risk notions, experience, and early warnings. Now’s the time to go beyond the limits of conventional static methods for hazard identification and risk assessment; the risk assessment is, indeed, a very useful approach in support of this change but at the same time it is not exhaustive to capture also the possible “failure” in the interface/interaction among the several single components of a complex system beside their specific failures.
This research work discusses a novel approach for dynamizing the risk assessment process, integrating measured process data, asset integrity and operative conditions.
In the first part of the thesis, the inferential process and the application of Machine Learning to inference is discussed, and various applications of standard, and tailored, machine learning algorithms to industrial and environmental risks are detailed as case studies.
The second part is focused on the resilience engineering. The resilience paradigm is discussed, as well as the concept of emerging properties of complex systems. it will be shown how real-time data analytics, through appropriate AI models, combined with the expert knowledge of process engineering, constitute the fundamental technological key to pursue the resilience of plants and processes.
The third section integrates the aforementioned concepts within the wide framework of Systems Engineering. Accordingly, a dynamic and systemic model is presented, to address the significant shortcomings of the current risk analysis models. The Dynamic Asset-integrity and Risk Management System (DARMS) is designed starting from the Bow-tie technique, integrated with improved Machine Learning algorithms, to overcome the epistemic uncertainty in the prior probabilities and likelihoods of escalation factors and barriers. Subsequently, a Hidden Markov Model (HMM), based on Bayesian Inference, is developed to analyze real-time risk, and produce reliable predictions on the state of the whole system during the operations. The application of the proposed model is demonstrated on an Oil and Gas terminal under Seveso legislation. The results of the case study provide a better understanding of the advanced Data Driven modeling of accident scenarios. The proposed model will serve as a useful tool for the operational safety management of complex systems
Safety in container terminal facilities: two different complementary Approaches to estimate the Consequences of the leakage of Flammable Substances.
No full textA very important issue for people involved in safety assessment to understand is the difference between what could happen after the failure of a containment system that causes the release – because of the failure itself – of a flammable substance, and what should be considered as an always present risk, also during normal conditions, without any evident failure. The scope of this article is to explain two approaches (one for each case) whose aim is totally different but if developed together it could help to reach a higher level of safety. Each approach will be applied to a different Italian container terminal facility
Atmospheric emissions from a fossil fuel power station: Dispersion modelling and experimental comparison
No full textIn this paper, a comparative study of different dispersion models considering stack emissions from a coalfired power plant. Modelling comparison study was performed utilizing Safe-Air II, a reliable Lagrangian model and ADMS 5adopting a skewed Gaussian representation. Fallouts were estimated starting from the statistical elaboration of meteorological data of the year 2012.Results evidenced a satisfactory agreement with measured data from monitoring stations, both in the discrete values and in the monthly and hourly averages. Furthermore, within an impact radius 40 km from the source, the areas of highest and lowest fallouts were identified in order to explore the possibility of considering lichens as potential bio-monitors, being well established their sensitivity to SO2 and eventual damage to the thalli
Ageing and creeping management in major accident plants according to seveso III directive
No full textThe focus of this paper is the management of critical equipment ageing within the context of lower and upper tier Seveso process plants, with a peculiar insight into the effectiveness of safety management systems in setting-up reliable procedure for critical element identification. Recent research studies in fact evidenced that in Europe nearly 50% of major 'loss of containment' events, arising from technical plant failures, were primarily due to ageing plant mechanisms such as erosion, corrosion and fatigue. The critical ageing elements should be included in maintenance, inspection and periodic monitoring programs in relation to their reliability, as assumed in the risk assessment and their lifetime or frequency ranges, based on their operational experience. This paper will accurately discuss how the issue of ageing is currently handled in the process industry. The methodology builds on the critical results of actual findings from the inspections on the safety management systems of major accident plants, which were performed by a working group. The primary objective is to stimulate the introduction of effective ageing management changes into the safety management of companies, by taking advantages of findings of the previous assessment and establishing proper and effective audits
LNG Operational and Navigational Risk: An Integrated Risk Assessment for a Resilient LNG Terminal
No full textLiquefied Natural Gas (LNG) terminals present a complex risk environment due to the inherent hazards associated with LNG handling and the navigational challenges within port areas. A comprehensive risk assessment (RA) framework for LNG terminals, encompassing both LNG operational and navigational risks in the terminal area is presented. Key factors such as LNG transfer operations, storage conditions, and vessel traffic patterns are systematically analysed. The framework incorporates navigational risk elements, including collision, grounding, and environmental conditions, to provide an all-embracing risk profile. Through extensive case studies and scenario analyses, the RA framework demonstrates its efficacy in identifying critical risk contributors and assessing mitigation measures. The results highlight the importance of a dual-focus approach in risk management, considering interdependencies between the components of the sociotechnical infrastructure and systems, e.g. in port areas, combining operational safety protocols for LNG handling with robust navigational safety strategies. The integrated assessment can support informed decision-making and enhance the overall safety and reliability of LNG terminal operations, ultimately contributing to a safer and more resilient energy transport infrastructure and port environment
Towards Operational Resilience Supported by Artificial Intelligence for Cargo Handling and Container Activities
No full textResilience engineering focuses on enhancing the ability of complex systems, organisations and processes to adapt to and recover from disruptions and unexpected events while maintaining their essential functions. It relies on the detection of early precursor of system failure states, on flexibility and controllability of processes, protective barriers construction and minimization of recovery time. Additionally, each process of the system should be characterised by learnability due to constant feedback and competently built management. It has been widely recognized the pivotal role of AI algorithms, which can analyse big data collected from sensors, historical records, and external sources to identify patterns, detect anomalies, and make predictive assessments. This paper critically explores the possibilities of applying text mining and Natural Language Processing techniques for entity extraction to construct an organisational resilience model more efficiently. Accordingly, visualization techniques are used to understand data patterns and trends and identify any areas for improvement (EDA – Exploratory Data Analysis). The textual analyses were based on accident reports obtained from Genoa port companies over 10 years. The data-driven decision-making enables proactive risk mitigation, early identification of potential failures, and optimization of safety protocols, and, in perspective, optimising the learning capacity of the port resilience system
Area risk analysis in an urban port: Personnel and major accident risk issues
No full textAs widely known, urban port areas represent considerably complex systems, both from the environmental and safety viewpoints, especially in case of nearness with installations of temporary storage, or handling of goods (known as "distriparks"). This paper offers a perspective on the different aspects of risk in an urban port area, considering both personnel and process related safety issues. Dealing with the latter aspect, area risk evaluation can be faced by recomposing the risk connected to loading/unloading activities and different transportation modes in the shipping areas, with the contribution due to the stationary industrial equipments/plants. The validity of the framework was tested considering a noticeable Italian case-study, starting from a detailed inventory of the more frequently dangerous goods handled in the area and the maximum credible amounts stored for a meaningful time (temporary storage)
Hydrogen jet-fire: Accident investigation and implementation of safety measures for the design of a downstream oil plant
No full textAs amply known, hydrogen plays a very significant role in the process industry exerting a vital functionality in oil refineries, namely for secondary level refining units such hydro-treating and hydrocracking sections. This paper starts from a statistical analysis on hydrogen accidents and a thorough investigation on the sequence and causes of an accident involving a hydrogen leakage in a downstream oil industry. We present some key features of the accident and comment some practical implications for setting up risk reduction options at the plant level. The applicative phase of the paper states the main prevention strategies and suggest possible mitigation measures for hydrogen leaks events, discussing some practical solutions applied in the design of a large refinery. The experience and lessons learned gained from the event investigation and the comparison of the accident with the predictions of the safety report leads to the formulation of proposals and design modifications aiming at preventing or at least minimizing the consequences
Unveiling the Achilles Heel: Detecting Organizational Weaknesses in the Energetic Transition Challenge
No full textTo be able to thrive in the grand challenges of the current historical moment, which includes important driving
phenomena such as climate change, digitalization and energy transition, the organizations need a
comprehensive understanding of the organizational and technical aspects that may pose opportunities and risks.
This paper presents a novel approach to identify weak organizational and technical factors within the context of
the energetic transition challenge. To accomplish this, a Machine Learning system is proposed, that integrates,
as input features, escalation and mitigation factors related to the risks that may arise in relation to the energetic
transition. The target variable is an indicator concerning the possible increase in the probability of accidents and
near misses, which is selected as an effective detector of potential weaknesses in the system. The primary
objective is to uncover organizational aspects that influence the mitigation, or enhancement of technological
risks during the energetic transition. By analysing the interplay between organizational and technical factors and
their role on preventive and mitigating barriers, this paper aims at identifying critical areas that require Attention
and improvement to ensure a smooth and successful energetic transition process. A reference case-study is
presented to demonstrate the actual capability of the presented framework. The findings of this study have
practical implications in the definition of organizational priorities in managing the energetic transition; the
identified weaknesses can serve as a basis for targeted interventions and strategic decision-making, allowing
for more effective risk management and improved outcomes during the energy transition
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