Dynamic evaluation of risk: From safety indicators to proactive techniques

This contribution presents a short review of dynamic risk evaluation techniques based on human and organizational factors, from the first approaches developing indicators to the aggregation methodologies integrating risk analysis. A methodology for the evaluation and update of expected release frequencies is taken as example of last generation techniques. The methodology aiming to support dynamic risk assessment studies is named TEC2O - Frequency modification methodology based on TEChnical Operational and Organizational factors. The potential of such methodology is described also in terms of support to risk based decision making for Oil&Gas integrated operations

The Earthquake Disaster Risk in Japan and Iran and the Necessity of Dynamic Learning from Large Earthquake Disasters over Time

This book chapter targets how learning from large earthquakes disasters occurred and developed in Japan and Iran in the last 100 years. As research case studies, large earthquake disasters in Japan and Iran were investigated and analyzed. Normal distribution was found to be a good estimate of the magnitude distribution for earthquakes, in both the countries. In Japan, there is almost a linear correlation between magnitude of earthquakes and number of dead people. However, such correlation is not present for Iran. This lack of correlation in Iran and existence of linear correlation in Japan highlights that the magnitude of earthquakes directly affects the number of fatalities and extent of destruction in Japan, while in Iran, there is an increased complexity with regard to the factors affecting earthquake consequences. A correlation is suggested between earthquake culture and learning from large earthquake disasters in both Japan and Iran. Learning from large earthquake disasters is impacted by a multitude of factors, but the rhythm of learning in Japan is much higher if compared with Iran. For both Japan and Iran, a reactive learning approach based on past earthquake disasters needs to be constantly backed up by a proactive approach and dynamic learning

Predicting chattering alarms: A machine Learning approach

Abstract 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

Learning from Major Accidents: a Machine Learning Approach

A B S T R A C T Learning from past mistakes is crucial to prevent the reoccurrence of accidents involving dangerous sub-stances. 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 sci-ence 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.(c) 2022 The Authors. Published by Elsevier Ltd.This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/

Digital Twin Concept for Risk Analysis of Oil Storage Tanks in Operations: a Systems Engineering Approach

This paper presents an approach to develop a risk monitoring tool for oil storage facilities. The suggested approach is derived from the existing dynamic risk analysis (DRA) methods and the digital twin concepts. One of the main challenges in practical applications of DRA methods is insufficient amount of relevant data, and it seems that digital twin models can overcome this challenge by offering increased availability of real-time data. It can be interesting to judge if their combination can provide the intended advantages with a structured and more holistic viewpoint. Therefore, this paper demonstrates how a representative systems engineering (SE) methodology may be used to facilitate the process of developing an improved risk monitoring tool.publishedVersio

An innovative and comprehensive approach for the consequence analysis of liquid hydrogen vessel explosions

Abstract Hydrogen is one of the most suitable solutions to replace hydrocarbons in the future. Hydrogen consumption is expected to grow in the next years. Hydrogen liquefaction is one of the processes that allows for increase of hydrogen density and it is suggested when a large amount of substance must be stored or transported. Despite being a clean fuel, its chemical and physical properties often arise concerns about the safety of the hydrogen technologies. A potentially critical scenario for the liquid hydrogen (LH2) tanks is the catastrophic rupture causing a consequent boiling liquid expanding vapour explosion (BLEVE), with consequent overpressure, fragments projection and eventually a fireball. In this work, all the BLEVE consequence typologies are evaluated through theoretical and analytical models. These models are validated with the experimental results provided by the BMW care manufacturer safety tests conducted during the 1990's. After the validation, the most suitable methods are selected to perform a blind prediction study of the forthcoming LH2 BLEVE experiments of the Safe Hydrogen fuel handling and Use for Efficient Implementation (SH2IFT) project. The models drawbacks together with the uncertainties and the knowledge gap in LH2 physical explosions are highlighted. Finally, future works on the modelling activity of the LH2 BLEVE are suggested

identification of hazards and environmental impact assessment for an integrated approach to emerging risks of co2 capture installations

Abstract New and intensified technologies are being defined within the field of Carbon Capture and Sequestration (CCS) and the uptake is set to increase dramatically. This contribution focuses on three representative installations for CCS capture, whose safety and environmental issues might potentially be underestimated based on their presence in other industrial fields, but with different scales and uses. A simplified Life Cycle Assessment (LCA) and the new hazard identification technique denominated DyPASI (Dynamic Procedure for Atypical Scenarios Identification) were used to identify respectively environmental impact and atypical accident scenarios and add a useful dimension to risk information that can particularly help in determining the best technological options

Assessment of Safety Barrier Performance in Environmentally Critical Facilities: Bridging Conventional Risk Assessment Techniques with Data-Driven Modelling

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

A Risk Aspect of Periodic Testing on Pressure Relief Valves

A pressure relief valve (PSV) is a key safety barrier to prevent the catastrophic rupture of pressure equipment in a process plant. The safety function of a PSV is to open and relieve the pressure when the equipment pressure exceeds the predefined set point. To achieve the desired availability of the PSV function, periodic function testing is regularly performed to confirm the correct functioning of a PSV. If a fault of the PSV function is detected by a function test, the PSV is repaired to a functioning state. For this reason, the interval between function tests has a direct influence on the probability of failure on demand (PFD) of the PSV function. On the other hand, unwanted leakage can occur due to human errors made during the preparation prior to a test and the reinstatement after the test. Such leakage is not desired due to the potential for being ignited and causing a major accident, but this aspect is often not considered in the availability assessment of PSVs. Therefore, this paper suggests a multi-phase Markov approach that can estimate the PFD of a PSV as well as the frequency of the leaks induced by the periodic tests. The suggested approach may be suitable for supporting the decision about the test interval for a PSV, considering both reliability and risk effect of extending the function test interval.publishedVersio

Spatio-Temporal Decision Fusion for Quickest Fault Detection Within Industrial Plants: The Oil and Gas Scenario

In this work, we present a spatio-temporal decision fusion approach aimed at performing quickest detection of faults within an Oil and Gas subsea production system. Specifically, a sensor network collectively monitors the state of different pieces of equipment and reports the collected decisions to a fusion center. Therein, a spatial aggregation is performed and a global decision is taken. Such decisions are then aggregated in time by a post-processing center, which performs quickest detection of system fault according to a Bayesian criterion which exploits change-time statistical distributions originated by system components’ datasheets. The performance of our approach is analyzed in terms of both detection- and reliability-focused metrics, with a focus on (fast & inspection-cost-limited) leak detection in a real-world oil platform located in the Barents Sea.acceptedVersio