Journal of Loss Prevention in The Process Industries最新文献

Leakage accidents of crude oil storage tanks (LACOST) occasionally occur during the production and storage processes of the petroleum and chemical industry, significantly impacting lives, the environment, and private property. To enhance the risk assessment of LACOST, our study sought to construct a fuzzy Bayesian network (FBN) through expert evaluation based on an improved analytic hierarchy process (AHP). Subsequently, the societal risk of LACOST was analyzed in conjunction with the surrounding population density. Applying the proposed method to a crude oil storage depot in China revealed that incorporating the improved AHP significantly enhances the FBN's risk assessment capability, leading to more accurate predictions of LACOST likelihood. Furthermore, the importance of basic events was assessed, thereby effectively and reliably identifying critical events of LACOST. The rationality of the layout of buildings and population density in the oil depot was assessed through societal risk analysis. Collectively, our findings demonstrated that the proposed method can effectively identify changes in both LACOST probabilities and consequences, enabling decision-makers to optimize risk management strategies and achieve efficient resource allocation.

In industries dealing with chemical substances, accidents can pose threats not only the workplace but also to neighboring communities. Therefore, it is crucial to assess and manage these risks. In South Korea, conducting risk assessments is mandatory as a preventive measure to avert accidents. However, determining the acceptability of risk levels and estimating the effectiveness of risk-reducing measures can be challenging during these assessments, despite prioritizing existing measures. This study focuses on evaluating the risk reduction rate of the Hierarchy of Controls. To address the challenges associated with estimating the risk reduction rate, especially in the face of unpredictability and uncertainties, we utilized the Fuzzy Bayesian Network (FBN). FBN combines Fuzzy set theory with the Bayesian Network, providing a more reliable approach to risk assessment. Specifically, our study examines quantifying the risk reduction rate of Controls concerning fire and explosion risks, considering the severity of potential accidents. The findings from this research have the potential to enhance the efficiency of decision-making processes in risk assessments, contributing to improved safety measures.

Leaks may occur in existing pipelines, even when designed with quality construction and appropriate regulations. The economic impact of oil spills and natural gas dispersion from leaks can be huge. Failure to detect pipeline leaks promptly will have an adverse impact on life, the economy, the environment, and corporate reputation. Therefore, early detection of leaks, their location, and their size with high sensitivity and reliability are important for efficient hydrocarbon transportation through a pipeline, both in onshore and offshore applications. Although several studies have been conducted on leak detection using various techniques, recent literature that comprehensively investigates and summarizes the different multiphase leak detection techniques could not be found. Therefore, this paper provides a comprehensive review of the different leak detection techniques in pipelines, wellbores, and subsurface sequestration wells. This is done by studying the different multiphase flow leak detection techniques using various Computational Fluid Dynamics (CFD), Mechanistic, Machine Learning models, and digital twin techniques in the pipeline as well as in sub-surface sequestration sites. A comprehensive investigation revealed that a few studies have been conducted related to integrated multiphase flow leak experiments, computational fluid dynamics, mechanistic models, and implementing extended real-time transient monitoring using machine learning. This type of systematic investigation is deemed to be more useful for field applications. Furthermore, a new set of recommendations is provided in the last section which shows how experimental, mechanistic, and CFD simulation data can be used to drive a statistical approach based on modern deep learning and digital twin techniques. This allows for the precise understanding of the leak events such as size, location, and orientation of the leak, without sending a remotely operated underwater vehicle or aircraft to scan the whole pipeline and ocean.

In July 2019, an arson using motor gasoline occurred in Kyoto. After the incident, the Fire Services Law in Japan was revised to regulate the sale of motor gasoline in portable cans to prevent similar incidents. However, other petroleum combustible liquids, such as white gasoline and lighter oil, remain easily accessible. Hence, the burning behavior when petroleum combustible liquids and motor gasoline are spilled should be elucidated. Although the burning behavior of spilled motor gasoline on a floor has been studied, the burning behavior of other petroleum combustible liquids has not yet been reported. Therefore, this study aims to investigate the burning behavior of eight types of petroleum combustible liquids, including motor gasoline. Gas chromatography–mass spectrometry analyses were performed to determine the sample compositions. The flashpoints were estimated from the measured saturated vapor pressures. A circular bank with a diameter of 1.6 m was created on a calcium silicate floor, and 4 L sample liquid was spilled inside the bank. From the results of the burning experiments, the flame temperature, surrounding heat flux, flame height, and burning duration of the petroleum combustible liquids were determined. Additionally, we attempted to predict the potential burn injuries to the human body around the flame based on the flux data. The results of this study can be applied to the fire risk assessment caused by petroleum combustible liquids and help predict burn injuries to victims.

The one-step oxidative esterification of methanol and methacrolein to yield methyl methacrylate (MMA) at elevated temperatures and pressures is a crucial chemical procedure for producing MMA. However, the conditions under which fire and explosions may occur during this procedure must be determined. This study models gas mixing to predict concentration distribution. Simulation results indicate stability with increasing pressure but wider gradients with higher temperature. Experimental studies on the flammable limits of methanol and methanol/methacrolein vapor mixtures under high temperatures and pressures show that the initial pressure has a significant impact on expanding the explosive limit of flammable vapor, whereas temperature has a relatively minor effect. The combustible limit of the mixed vapor is predicted using the heat balance approach, and a comparison with experimental data shows that the anticipated and real values coincide well.

Protective walls are crucial structures in mitigating damage from explosions, particularly in environments such as gas refueling stations. However, evaluating their performance typically involves intricate numerical analyses to derive Pressure-Impulse (P–I) diagrams. This paper presents a novel closed-form solution for deriving P–I diagrams specifically tailored for protective walls, considering flexural and shear resistance. By assuming perfect plastic resistance, the proposed solution offers an efficient alternative to numerical analysis, particularly beneficial in preliminary design stages. The validity of the closed-form solution is demonstrated through comparisons with numerically derived P–I diagrams, showing promising results across various design parameters. This approach enhances the accessibility and ease of designing protective walls for blast resistance, contributing to improved safety measures in critical infrastructure.

Arsenic is an extremely toxic metalloid. The majority of arsenic water pollution originates from industrial activities such as mining, smelting, and utilization of arsenic compounds in glass, pigment, semiconductor, and other industries. Arsenic-containing wastewater poses a threat to groundwater, freshwater, seawater, and soil. Bioaccumulation of arsenic as a result of accidental ingestion may have a harmful effect on the skin and cardiovascular, respiratory, and nervous systems, thereby posing a major health risk. Chemical coagulation, membrane filtration, and adsorption can be used to treat arsenic-containing wastewater. However, chemical coagulation produces a large amount of waste sludge, which is harmful and incurs high processing costs. Membrane filtration is difficult to adopt because of its high operation and maintenance costs. Adsorption is an inexpensive, easy-to-use, sludge-free water treatment method that enables adsorbent recycling. These characteristics make adsorption an economically feasible technique for water treatment. Magnetic nanoparticles (MNPs) are small particles with a high surface area to volume ratio. Their strong magnetic properties enable them to absorb specific water pollutants. MNPs are currently used as water treatment adsorbents. However, they have several drawbacks, including low acid and alkali buffer capacity and water oxidation. In this study, MNPs immobilized with polyvinyl alcohol (PVA) and sodium alginate (SA) were used to enhance the efficacy of wastewater treatment with magnetic materials. These modified MNPs exhibited magnetic characteristics and improved acid resistance. When 100 g/L PVA/SA-MNPs (pH 1–6) were stirred at 120 rpm for 30 min, the resulting iron concentration of the water was 16 mg/L. By contrast, the MNPs released iron in levels ranging from 18 to 4045 mg/L. The PVA/SA–MNPs efficiently adsorbed arsenic in the pH range 3–6, with a high removal efficiency of 76%–82%. At pH 5, the average adsorption capacity was 382 ± 27 μg/g. To recover the MNPs, they were rinsed with deionized water and immersed in acid three times, after which their adsorption capacity was 300 μg/g or higher. By contrast, the PVA/SA-MNPs could be recovered through spontaneous sedimentation, eliminating the need for a magnetic field and simplifying the collection process. In addition, iron dissolution in acidic solutions was minimized for the PVA/SA-MNPs, contributing to environmental protection.

Natech events triggered by floods have occurred frequently, resulting in physical damage to process equipment and subsequent releases of hazardous substances, threatening the operational safety of the process industry. Therefore, it is necessary to conduct flood fragility assessments on typical process equipment, such as storage tanks and process pipelines. Compared to storage tank, there are relatively few flood risk analysis methods applicable to process pipelines, and there is a lack of dedicated fragility models to quantify the probability of pipeline damage in flood hazard scenarios. Thus, this paper develops a process pipeline fragility model to support the quantitative risk assessment (QRA) of flood-induced Natech events more comprehensively. This model simultaneously considers the structural physical damage caused by internal pressure and external load in the pipeline and establishes the limit state equation (LSE) corresponding to the failure mode. On this basis, parametric fragility functions are trained using Monte Carlo simulations and logistic regression. A pipeline case is used to test the proposed fragility functions, and the results show that the fitted parameterized fragility model can sensitively capture changes in the failure probability curve caused by hazard intensity and pipeline characteristics changes. The proposed pipeline fragility model is applied to a composite area of pipelines and storage tanks, accurately assessing the failure probability of different types of pipelines in flood scenarios.

In the past years, the use of biomass has significantly increased and, therefore, so has the number of accidents related to its storage, transport, and use. To prevent these accidents, it is essential to properly know their flammability and explosion characteristics so their behaviour can be addressed as a first stop for preventing accidents. The present work studies the inertization with solid inerts of biomass layers and clouds (biomass powder suspended in air) as a possible solution to reduce their ignition tendency. To do so, two biomass samples were studied: wood pellets and dried sewage sludge; mixed with two different inert materials: recycled glass and sodium bicarbonate. In particular, the inert materials were mixed with biomass at three different concentrations (30%, 50% and 70%) and the ignition of the mixtures was studied, determining the minimum ignition temperature of layer and cloud (MIT_L and MIT_C) for each mixture, and detecting the needed concentration for avoiding the ignition. Additionally, samples were tested using TGA and DSC techniques to analyse their thermal behaviour and to determine the influence that the inert material has in the energetic power of the biofuel.

Different behaviours were observed depending on the different inert materials, showing that not only the amount of inert added is important but also its physico-chemical properties. If the results for a layer and a cloud are compared, it was noticed that inertization effect differs between biomass and test. Regarding TGA and DSC results, it was concluded that smaller percentages of inert material should be considered, as they substantially modify the energetic value.

Chemical process industries (CPIs) face significant safety and security risks that are closely intertwined. However, previous research has predominantly examined these risks separately, lacking a comprehensive scientific approach to simultaneously analyze safety and security in critical systems. This research aims to propose an integrated approach to assess safety and security risks in CPIs. To achieve this, a taxonomy of safety and security risk factors consisting of four dimensions (i.e., occurrence probability, severity, vulnerability, and securing) and their respective twenty contributing factors has been developed. The validity and reliability of the taxonomy were assessed using the Delphi method, involving Subject Matter Experts (N = 25), and through statistical analysis. Subsequently, the Fuzzy Analytical Hierarchy Process (FAHP) was employed to determine the importance level of each contributing factor and dimension, thereby enabling the integration of safety and security risk levels. The proposed approach was validated through reality checks, and independent peer review. The findings of this study highlight the dimensions and contributing factors that have the most significant impact on the integrated safety and security risk level. Moreover, the approach provides insights into effectively assigning countermeasures to establish a safe and resilient operation in CPIs. By adopting this integrated assessment approach, CPIs can gain a deeper understanding of the interplay between safety and security risks, allowing for more informed decision-making and the implementation of targeted risk mitigation strategies.