Frequency Of Hazard Events Research Articles

Energy inequality in climate hazards: Empirical evidence of social and spatial disparities in managed and hazard-induced power outages

Due to the effects of climate change and urbanization, the severity and frequency of hazard events is expected to increase. The energy sector in the United States is ever more vulnerable to extreme climatic hazards. Hurricane winds can damage electrical lines, causing hazard-induced power outages. Extreme heat and freezing temperatures can imbalance the supply and demand for energy resulting in managed power outages. Utility companies reportedly prioritize the restoration of power systems based on the number of outages and the size of affected populations. This approach fails to account for unequal impacts of hazard-induced and managed power outages. Research in equitable infrastructure emphasizes that certain populations, such as lower income and racial-ethnic minority households, are disproportionately impacted by disruptions in the power system. Moreover, the connected network qualities of the power system suggests an element of spatial vulnerabilities. However, little empirical evidence exists regarding the presence and extent of energy inequality. A main roadblock is the data collection process, in that outage data is often perishable and not found at granular spatial scales to allow the undertaking of a comprehensive analysis on impacts of power losses. Recognizing this important gap, this study collected and analyzed observational data related to the managed power outages during Winter Storm Uri (2021) and the hazard-induced outages during Hurricane Ida (2021). The research quantified the period of recovery at a granular spatial scale using an equitable-focused analysis to detect social and spatial inequalities through an exploratory lens. In extreme cases of power outage, census tracts of lower income and higher percentage of Hispanic population had longer median durations of recovery during Winter Storm Uri. In the hazard-induced outages of Hurricane Ida, non-coastal zip codes with lower income had a 1.00-day longer median duration of recovery and higher percentage of Black population had a 2.00-day longer median duration of recovery while coastal zip codes with higher percentage of Black population had a 1.00-day longer median of recovery. Non-coastal regions had 63% greater spatial Gini values and 16% greater value in infrastructure inequality when compared to coastal regions. The managed power outages resulted in a 3% to 19% greater value of infrastructure inequality to the hazard-induced power outages. The findings provide evidence of pervasive social and spatial inequality in power outages during climate hazards and highlight the importance of integrating equity into the manner in which utility managers and emergency planners restore power outages.

A Consistent Analytical Method to Assess Reliability of Redundant Safety Instrumented Systems

The significance of determining Safety Integrity Level (SIL) has grown substantially in numerous industry sectors since the publication of international standard for functional safety (IEC61508). To detect SIL using IEC61508, the demand mode must first be recognised. However, there is not any 100% accurate method to identify the demand mode. Design engineers must assume the demand mode based on their assessment of the likely number of failures each year in the system under consideration, referred to as demand rate. This signifies that a defective demand mode assumption leads to an incorrect SIL determination. In addition, the formulas provided by the IEC standards for the SIL determination did not include the demand duration. The majority of prior publications in the literature relied on SIL determination using IEC61508. As a result, the literature suffers from the constraints of the IEC61508 standard, which include ignoring demand duration and uncertainty in assuming the system’s demand mode for determining SIL. This paper addresses the aforementioned issues in literature through proposing a scenario based-formula to calculate Hazardous Event Frequency (HEF) using Markov process for safety system with dual redundancy. The HEF calculated can then be utilized to select SIL while considering the demand duration and obviating the requirement to use the SIS demand mode. The results demonstrated that the proposed method provides higher accuracy in calculating the HEF value compared to the existing traditional formulas.

Connected and autonomous vehicle injury loss events: Potential risk and actuarial considerations for primary insurers

AbstractThe introduction of connected and autonomous vehicles (CAVs) to the road transport ecosystem will change the manner of collisions. CAVs are expected to optimize the safety of road users and the wider environment, while alleviating traffic congestion and maximizing occupant comfort. The net result is a reduction in the frequency of motor vehicle collisions, and a reduction in the number of injuries currently seen as “preventable.” A changing risk ecosystem will introduce new challenges and opportunities for primary insurers. Prior studies have highlighted the economic benefit provided by reductions in the frequency of hazardous events. This economic benefit, however, will be offset by the economic detriment incurred by emerging risks and the increased scrutiny placed on existing risks. We posit four plausible scenarios detailing how an introduction of these technologies could result in a larger relative rate of injury claims currently characterized as tail‐risk events. In such a scenario, the culmination of these losses will present as a second “hump” in actuarial loss models. We discuss how CAV risk factors and traffic dynamics may combine to make a second “hump” a plausible reality, and discuss a number of opportunities that may arise for primary insurers from a changing road environment.

Impact of common cause failure on reliability performance of redundant safety related systems subject to process demand

Common Cause Failures (CCFs) can compromise reliability performance of safety related systems and hence configurations with identical redundant units receive special attention in many industries, including in automotive, aviation and process applications. This paper introduces a new reliability model for redundant safety related systems using Markov analysis technique. The proposed model entails process demand in conjunction with CCF and established system failure modes such as dangerous undetected failures for the first time and evaluates their impact on the reliability performance of the system. The reliability of the safety related systems is measured using the Probability of Failure on Demand (PFD) for low demand systems. The safety performance of the system is also appraised using Hazardous Event Frequency (HEF) to quantify the frequency of system entering a hazardous state that will lead to an accident if the situation is not controlled accordingly. The accuracy of the proposed Markov model is verified for a case study of flammable liquid storage tank overpressure protection system. It is demonstrated that the proposed approach provides sufficiently robust results for all demand rates, demand durations, dangerous undetected and CCF frequencies and associated repair rates for redundant safety related systems utilised in low demand mode of operation.

Unavailability assessment of redundant safety instrumented systems subject to process demand

The process industry has always been faced with the challenging task of determining the overall unavailability of safeguarding systems such as the safety instrumented systems (SISs). This paper proposes an unavailability model for a redundant SIS using Markov chains. The proposed model incorporates process demands in conjunction with dangerous detected and undetected failures for the first time and evaluates their impacts on the unavailability quantification of SIS. The unavailability of the safety instrumented system is quantified by considering the probability of failure on demand (PFD) for low demand systems. The safety performance of the system is also assessed using hazardous event frequency (HEF) to measure the frequency of system entering a hazardous state that will lead to an accident. The accuracy of the proposed Markov model is verified for a case study of a chemical reactor protection system. It is demonstrated that the proposed approach provides a sufficiently robust result for all demand rates, demand durations, dangerous detected and undetected failure rates and associated repair rates for safety instrumented systems utilised in low demand mode of operation. The effectiveness of the proposed model offers a robust opportunity to conduct unavailability assessment of redundant SISs subject to process demands.

Reliability modelling of redundant safety systems without automatic diagnostics incorporating common cause failures and process demand

Redundant safety systems are commonly used in the process industry to respond to hazardous events. In redundant systems composed of identical units, Common Cause Failures (CCFs) can significantly influence system performance with regards to reliability and safety. However, their impact has been overlooked due to the inherent complexity of modelling common cause induced failures. This article develops a reliability model for a redundant safety system using Markov analysis approach. The proposed model incorporates process demands in conjunction with CCF for the first time and evaluates their impacts on the reliability quantification of safety systems without automatic diagnostics. The reliability of the Markov model is quantified by considering the Probability of Failure on Demand (PFD) as a measure for low demand systems. The safety performance of the model is analysed using Hazardous Event Frequency (HEF) to evaluate the frequency of entering a hazardous state that will lead to an accident if the situation is not controlled. The utilisation of Markov model for a simple case study of a pressure protection system is demonstrated and it is shown that the proposed approach gives a sufficiently accurate result for all demand rates, durations, component failure rates and corresponding repair rates for low demand mode of operation. The Markov model proposed in this paper assumes the absence of automatic diagnostics, along with multiple stage repair strategy for CCFs and restoration of the system from hazardous state to the “as good as new” state.

Use of hazardous event frequency to evaluate safety integrity level of subsea blowout preventer

Generally, the Safety Integrity Level (SIL) of a subsea Blowout Preventer (BOP) is evaluated by determining the Probability of Failure on Demand (PFD), a low demand mode evaluation indicator. However, some SIL results are above the PFD's effective area despite the subsea BOP's demand rate being within the PFD's effective range. Determining a Hazardous Event Frequency (HEF) that can cover all demand rates could be useful when establishing the effective BOP SIL. This study focused on subsea BOP functions that follow guideline 070 of the Norwegian Oil and Gas. Events that control subsea well kicks are defined. The HEF of each BOP function is analyzed and compared with the PFD by investigating the frequency for each event and the demand rate for the components. In addition, risk control options related to PFD and HEF improvements are compared, and the effectiveness of HEF as a SIL verification for subsea BOP is assessed.

Correction factor determination on failure rate equation of MacLaurin series for low and high mode application

Abstract Safety Instrumented Function (SIF) is implemented on the system to prevent hazard in process industry. In general, most of SIF implementation in process industry works in low demand condition. Safety valuation of SIF that works in low demand can be solved by using quantitative method. The quantitative method is a simplified exponential equation form of MacLaurin series, which can be called simplified equation. Simplified equation used in high demand condition will generate a higher Safety Integrity Level (SIL) and it will affect the higher safety cost. Therefore, the value of low or high demand rate limit should be determined to prevent it. The result of this research is a first order equation that can fix the error of SIL, which arises from the usage of simplified equation, without looking the demand rate limit for low and high demand. This equation is applied for SIL determination on SIF with 1oo1 vote. The new equation from this research is λ = 0.9428 λ MC + 1.062E−04 H / P , with 5% average of error, where λ MC is a value of λ from the simplified equation, Hazardous event frequency ( H ) is a probabilistic frequency of hazard event and P is Probability of Failure on Demand (PFD) in Independent Protection Layers (IPLs). The equation generated from this research could correct SIL of SIF in various H and P . Therefore, SIL design problem could be solved and it provides an appropriate SIL.

Probabilistic Volcanic Ash Hazard Analysis (PVAHA) I: development of the VAPAH tool for emulating multi-scale volcanic ash fall analysis

Significant advances have been made in recent years in probabilistic analysis of geological hazards. Analyses of this kind are concerned with producing estimates of the probability of occurrence of a hazard at a site given the location, magnitude, and frequency of hazardous events around that site; in particular Probabilistic Seismic Hazard Analysis (PSHA). PSHA is a method for assessing and expressing the probability of earthquake hazard for a site of interest, at multiple spatial scales, in terms of probability of exceeding certain ground motion intensities. Probabilistic methods for multi-scale volcanic ash hazard assessment are less developed. The modelling framework presented here, Probabilistic Volcanic Ash Hazard Analysis (PVAHA), adapts the seismologically based PSHA technique for volcanic ash. PVAHA considers a magnitude-frequency distribution of eruptions and associated volcanic ash load attenuation relationships and integrates across all possible events to arrive at an annual exceedance probability for each site across a region of interest. The development and implementation of the Volcanic Ash Probabilistic Assessment tool for Hazard (VAPAH), as a mechanism for facilitating multi-scale PVAHA, is also introduced. VAPAH outputs are aggregated to generate maps that visualise the expected volcanic ash hazard for sites across a region at timeframes of interest and disaggregated to determine the causal factors which dominate volcanic ash hazard at individual sites. VAPAH can be used to identify priority areas for more detailed PVAHA or local scale ash dispersal modelling that can be used to inform disaster risk reduction efforts.

Consistent consequence severity estimation

AbstractMost risk analysis methods rely on a qualitative judgment of consequence severity, regardless of the analysis rigor applied to the estimation of hazardous event frequency. As the risk analysis is dependent on the estimated frequency and consequence severity of the hazardous event, the error associated with the consequence severity estimate directly impacts the estimated risk and ultimately the risk reduction requirements. Overstatement of the consequence severity creates excessive risk reduction requirements. Understatement results in inadequate risk reduction.Consistency in the consequence severity estimate can be substantially improved by implementing consequence estimation tools that assist process hazards analyses/layers of protection analysis (PHA/LOPA) team members in understanding the flammability, explosivity, or toxicity of process chemical releases. This article provides justification for developing semiquantitative look‐up tables to support the team assessment of consequence severity. Just as the frequency and risk reduction tables have greatly improved consistency in the estimate of the hazardous event frequency, consequence severity tables can significantly increase confidence in the severity estimate. © 2011 American Institute of Chemical Engineers Process Saf Prog, 2012

Improving PHA/LOPA by consistent consequence severity estimation

Most risk analysis methods rely on a qualitative judgment of consequence severity, regardless of the analysis rigor applied to the estimation of hazardous event frequency. Since the risk analysis is dependent on the estimated frequency and consequence severity of the hazardous event, the error associated with the consequence severity estimate directly impacts the estimated risk and ultimately the risk reduction requirements. Overstatement of the consequence severity creates excessive risk reduction requirements. Understatement results in inadequate risk reduction. Consistency in the consequence severity estimate can be substantially improved by implementing consequence estimation tools that assist PHA/LOPA team members in understanding the flammability, explosivity, or toxicity of process chemical releases. This paper provides justification for developing semi-quantitative look-up tables to support the team assessment of consequence severity. Just as the frequency and risk reduction tables have greatly improved consistency in the estimate of the hazardous event frequency, consequence severity tables can significantly increase confidence in the severity estimate.

Using network methodology to define emergency response team location: the Brazilian refinery case study

The main objective of this study is to define an emergency response team's location in a specific area based on the risk of refinery's plants. The Centre of Gravity and Hakini network methodologies are both used to define the team's location in a network based on index values and distance between locations. These methodologies are different with respect to one basic concept concerning the possibility of defining critical locations in the network in the first case and boundaries on the second case. The index in this case will be the frequency of hazardous events in each plant located in the network. The two methodologies will be implemented and the results will be assessed. A sensitivity analysis will be carried out, looking at specific elements such as alternative routes and population dislocation in case of accidents. Furthermore, the real historical data and usual data used in Brazil for hazardous events will be assessed to check the influence on the final results. A third methodology, the Monte Carlo simulation, will be carried out to check the emergency team's availability, safety reliability and number of expected unwanted events to support the final decisions and verify if the best location takes some influence in emergency attendance efficiency. The refinery case study will be carried out to define emergency response team location in a Brazilian refinery.

SCADA data and the quantification of hazardous events for QMRA

The objective of this study was to assess the use of on-line monitoring to support the QMRA at water treatment plants studied in the EU MicroRisk project. SCADA data were obtained from three Catchment-to-Tap Systems (CTS) along with system descriptions, diary records, grab sample data and deviation reports. Particular attention was paid to estimating hazardous event frequency, duration and magnitude. Using Shewart and CUSUM we identified 'change-points' corresponding to events of between 10 min and >1 month duration in timeseries data. Our analysis confirmed it is possible to quantify hazardous event durations from turbidity, chlorine residual and pH records and distinguish them from non-hazardous variability in the timeseries dataset. The durations of most 'events' were short-term (0.5-2.3 h). These data were combined with QMRA to estimate pathogen infection risk arising from such events as chlorination failure. While analysis of SCADA data alone could identify events provisionally, its interpretation was severely constrained in the absence of diary records and other system information. SCADA data analysis should only complement traditional water sampling, rather than replace it. More work on on-line data management, quality control and interpretation is needed before it can be used routinely for event characterization.

Hazardous events associated with impaired olfactory function.

To evaluate the risk of olfactory-related hazardous events in patients with impaired olfactory function. Retrospective cohort study. A university-based clinic for smell and taste disorders. A total of 445 patients who underwent olfactory testing between 1983 and 2001. Patient interview, olfactory testing. (1) Frequency of olfactory-related hazardous events including cooking incidents (ie, burning pots or pans), undetected fires, undetected gas leaks, and ingestion of spoiled foods or toxic substances; (2) level of olfactory function (anosmia; severe, moderate, or mild hyposmia; or normosmia) as determined by olfactory testing. Olfactory testing revealed that 76% of patients had some degree of impairment; 30% had complete anosmia. Thirty-seven percent of patients with olfactory impairment but only 19% of patients without impairment experienced at least 1 olfactory-related hazardous event. Of the hazardous events reported by impaired patients, cooking-related incidents were most common, representing 45%, with ingestion of spoiled food (25%), inability to detect a gas leak (23%), and inability to smell a fire (7%) reported less frequently. There was a significant correlation between frequency of hazardous events and degree of olfactory impairment (Cochran-Armitage trend test, P<.001): at least 1 hazardous event was reported by 45.2% of patients with anosmia, 34.1% with severe hyposmia, 32.8% with moderate hyposmia, 24.2% with mild hyposmia, and 19.0% of patients with with normal olfaction by testing. Patients with impaired olfactory function are more likely to experience olfactory-related hazardous events than those with normal olfactory function.