Transportation Of Hazardous Chemicals Research Articles

How have regional evacuation conditions changed over time? evacuation model for alternative scenarios given the accident environment, regional environment, and social systems.

The rapid evacuation in major leak accidents during hazardous chemical transportation is critical for protecting people in risk areas. An approach integrating the accident environment, regional environment, and social system is proposed to perform evacuation evaluation in such accidents. An agent-based modeling framework consisting of warning mechanisms, evacuation preparation process, as well as evacuation modes and movement process, is developed to comprehensively model the evacuation process. The regional environmental and social system data from 1964 to 2020 of Jiangsu Province in China is applied to analyze whether the changes in evacuation conditions have correspondingly affected evacuation effectiveness. A regional evacuation model simulating a real liquid chlorine leak accident during transportation in Wuzhong of Jiangsu Province is constructed to test the effectiveness and applicability of the proposed method. It is found that a 9% change in the diffusion rate of warning messages triggered by environmental cues with the evolution of the study area settlements following the chronological changes; while the evolution of media technology allows for the rapid diffusion of warning messages and the rapid loading of a large number of individuals into the regional evacuation network; Years with faster warning diffusion have not been the most efficient ones in terms of overall evacuation; The distribution of buildings and population substantially impacts the overall evacuation effectiveness, while the former has a more significant impact. The study results can provide information for public emergency authorities to develop effective early warning resource allocation and evacuation organization plans.

Quantitative risk analysis of road transportation of hazardous materials in coastal areas

Given the complex climate conditions in coastal areas and their role as key transportation hubs for hazardous chemicals, this study proposes a method to quantitatively and comprehensively evaluate transportation risks. Initially, accident data were analyzed to identify risk factors from five aspects: human, vehicle, materials, environment, and management, based on system safety theory. Subsequently, a risk analysis model was developed using Decision-making Trial and Evaluation Laboratory, interpretive structural model theory, and Bayesian theory to quantitatively assess accident risk levels. The model was applied to a case involving a hazardous chemical accident on a cross-sea bridge, where Bayesian backward reasoning was used to analyze the sensitivity and importance of risk factors. This approach facilitated the key risk factors affecting the safety of hazardous chemical transportation systems. Notably, the study incorporated scenarios involving hazardous material transport vehicles crossing sea bridges into the risk assessment framework, offering valuable insights for management authorities. It also considered the impact of strong side winds-a factor often overlooked-in hazardous material transport. The validation process demonstrated that the method accurately quantifies the risk of hazardous chemical transportation and identifies the key factors influencing accident occurrence. The research highlights that strong gusts of wind significantly impact safety, and human factors are crucial in the overall risk system.

Assessment of the Hazard Risk That May Arise During the Transportation of Hazardous Chemicals by Vehicle Transport

Assessment of the Hazard Risk That May Arise During the Transportation of Hazardous Chemicals by Vehicle Transport

Study on risk assessment models for the aggregation of vehicles transporting hazardous chemicals

Aggregation of vehicles transporting hazardous chemicals is part of a complex system of hazardous chemical transportation with frequent accidents that threaten public safety. In order to avoid catastrophic consequences in the vehicle aggregation areas, dynamic risk assessment models have been studied in this paper. The macro quantitative risk assessment (M-QRA) model determines the accident probability through the probability of leakage and the probability of induced secondary accidents and takes into account the domino effect to determine the risk in conjunction with the consequence of pool fire, but some data are not readily available. Therefore, in order to accurately assess the risk in case of incomplete data, a rapid quantitative risk assessment (R-QRA) model considering the number of vehicles and the critical mass of hazardous chemicals was developed. In this paper, the M-QRA and R-QRA model were used to calculate the real-time risk of aggregation of vehicles transporting hazardous chemicals respectively, and the two models were compared in terms of calculation accuracy and assessment efficiency. The results show that the risks obtained from the two models are basically consistent, but the R-QRA model is faster in calculation, which is of great practical significance for actual engineering applications and helps safety management.

SAM‐Y: Attention‐enhanced hazardous vehicle object detection algorithm

AbstractVehicle transportation of hazardous chemicals is one of the important mobile hazards in modern logistics, and its unsafe factors bring serious threats to people's lives, property and environmental safety. Although the current object detection algorithm has certain applications in the detection of hazardous chemical vehicles, due to the complexity of the transportation environment, the small size and low resolution of the vehicle target etc., object detection becomes more difficult in the face of a complex background. In order to solve these problems, the authors propose an improved algorithm based on YOLOv5 to enhance the detection accuracy and efficiency of hazardous chemical vehicles. Firstly, in order to better capture the details and semantic information of hazardous chemical vehicles, the algorithm solves the problem of mismatch between the receptive field of the detector and the target object by introducing the receptive field expansion block into the backbone network, so as to improve the ability of the model to capture the detailed information of hazardous chemical vehicles. Secondly, in order to improve the ability of the model to express the characteristics of hazardous chemical vehicles, the authors introduce a separable attention mechanism in the multi‐scale target detection stage, and enhances the prediction ability of the model by combining the object detection head and attention mechanism coherently in the feature layer of scale perception, the spatial location of spatial perception and the output channel of task perception. Experimental results show that the improved model significantly surpasses the baseline model in terms of accuracy and achieves more accurate object detection. At the same time, the model also has a certain improvement in inference speed and achieves faster inference ability.

A Possibility Evaluation Model for Road Transportation of Hazardous Chemicals Based on Bow-tie Theory and Bayesian Model

This paper collected 1115 road transportation accidents of hazardous chemicals in China from 2013 to 2021, and constructed a total of 120 accident chains, among which the most common accident chain was "impact → rear-end collision → fuel tank damage → leakage", with a total of 195 times. Based on the analysis of historical accident data, the occurrence mechanism of road transportation accidents of hazardous chemicals was discussed. Based on the bow-tie theory, the bow-tie diagram was established to realize the construction of qualitative reasoning bow-tie diagram for multi-disaster coupling accidents in road transportation of hazardous chemicals. The mapping rules between Bow-tie model and Bayesian network were established, and the structure of Bayesian network for road transportation of hazardous chemicals was constructed. The prior probability and conditional probability of Bayesian network nodes are determined by multi-source data, and the real-time accident probability evaluation is realized by combining quantitative and qualitative methods. Finally, a real time evaluation of the possibility of multi-disaster coupling accidents is realized every 30 seconds through a numerical example, which reflects the influence law of multi-disaster coupling scenarios on risks.

Hazardous Chemicals Logistics Internet of Vehicles Based on Encryption Algorithm

With the advancement of information and communication technology, vehicles role in people's lives is not just for transportation but also equipment for the carrier of mobile communication. The new direction is provided by the Internet for the automobiles development and upgrading. Internet of Vehicles (IoVs) includes smart vehicles, Autonomous Vehicles (AVs) as well as roadside units (RSUs) that communicate for providing the enhanced transportation services such as high traffic efficiency and reduced congestion and accidents. In the hazardous chemical logistics sector, the integration of Internet of Vehicles (IoVs) introduces significant security, privacy, and trust challenges. Vulnerabilities to cyberattacks, such as hacking and data manipulation, threaten the integrity of sensitive information regarding hazardous cargo, while concerns about location privacy and data minimization arise due to the tracking of vehicle movements. Ensuring authentication, authorization, and compliance with regulatory standards is essential for building trust within the IoV ecosystem, as any compromise in supply chain integrity could lead to safety hazards or legal liabilities. Robust encryption algorithms like AES and RSA play a crucial role in securing data transmission, but proper implementation and key management practices are necessary to prevent cryptographic weaknesses. Addressing these issues comprehensively is vital for safeguarding the transportation of hazardous chemicals and ensuring the safety of both the environment and the public. IoVs, suffer from security, privacy and trust issues. In order to solve the problem of hazardous chemical logistics of encryption algorithm, the author proposed a research on the information security of the Internet of Vehicles. Firstly, the information characteristics of dangerous chemicals vehicles are analyzed, then analyze the Data Encryption Standard (DES) algorithm. Advanced Encryption Standard (AES) algorithm is analyzed in the symmetric cryptosystem and then RSA algorithm is analyzed in the public key cryptosystem. Finally, the performance of the algorithm and the characteristics of vehicle information are comprehensively analyzed. The security of Internet of Vehicle's data transmission is efficiently improved by the proposed method.

Modeling and Improvement Strategies for Safety Resilience in Maritime Hazardous Chemical Transportation System Based on Dissipative Structure Theory and System Dynamics

Maritime hazardous chemical transportation accidents have the characteristics of strong suddenness, wide influence, and great harm. To analyze the ability of a maritime hazardous chemical transportation system (MHCTS) to cope with sudden disturbance events, “resilience” is introduced into MHCTS safety research. The key to studying resilience is modeling its evolutionary process. Based on the dissipative structure theory, this study analyzes the entropy flow mechanism of MHCTS safety resilience evolution. Through a statistical analysis of 197 investigation reports on maritime hazardous chemical transportation accidents, the factors influencing the safety resilience of the MHCTS were determined. The entropy value and weight of each influencing factor were calculated using the entropy method and entropy weight method, respectively. Based on this, an entropy model of the safety resilience evolution of the MHCTS was established. The evolution process falls under four categories of disturbance strengths, which were simulated using the system dynamics method. The degree of contribution of absorptive, adaptive, and restorative capacities to the improvement of system safety resilience under four disturbance conditions and the sensitivity of each influencing factor to the absorptive, adaptive, and restorative capacities were analyzed. Based on the analyses, targeted resilience improvement strategies are proposed. The research results provide a theoretical reference for the study of safety resilience mechanisms and resilience management in the MHCTS.

Route Optimization for Hazardous Chemicals Transportation under Time-Varying Conditions

Since accidents of hazardous chemicals transportation will cause serious loss to the surrounding environment and lives and properties, this paper studies the transportation route optimization problem of hazardous chemicals under dynamic time-varying conditions. Combined with the goal of green sustainable development, a multiobjective nonlinear optimization model is constructed to minimize the transportation risk, transportation cost, and carbon emissions generated in the transportation. The model is solved by the improved Fast Non-Dominated Sorting Genetic Algorithm with Elite Strategy (NSGA-II) algorithm. The effectiveness of the model and the algorithm are tested on the Sioux Falls network. The experimental results show that under time-varying conditions, a vehicle’s departure at different times will generate different transportation costs and risks. Therefore, enterprises need to rationally arrange the departure time of vehicles according to the time windows of customer nodes and road conditions. In additio, from the relationship between the optimization objectives, in order to achieve green, sustainable and low-risk transportation, enterprises should first reduce their transportation costs.

Совершенствование транспортировки АХОВ на автомобильном транспорте

Purpose: The issue of hazardous chemicals transportation by motor vehicles is especially crucial nowadays, given recent geopolitical situation. Transportation inside the country has become a key task for the Russian Federation. The improvement of motor vehicle construction is required in order to provide security of the transportation process. Methods: The tool for research and analysis of the issue of choosing the optimal crosssection of the relief valve on a tank car is the flow section diameter of the tank car’s relief valve. Results: The article proposes calculations for the cross-section of the relief valve of a tank with a larger diameter than those currently installed on tank cars in Russia. The article also provides statistical data on accidents in road transport from 2018 to 2020. Practical significance: Semi-empirical models of thermophysical processes inside a tank during the occurrence of excess pressure have been described by numerous authors. The level of development of the topic of this research is relatively low, unlike topics dedicated to excess pressure in pressure-relief tanks and thermodynamic processes. The article discusses the improvement of designs of tank cars used for the transportation of light petroleum products. The authors have offered a way to improve the construction of a tank with the help of installation of a relief valve of a bigger section. The results of the calculations of the optimal section in the relief valve are presented in a tabular form. The calculation has practical significance as in the case of integration of the data on relief (breather) valves there exists a possibility of increasing the stability of the thin shell of a tank and so of increasing the security during the transportation by motor vehicles. The enlargement of flow area of the tank relief valve is a technically efficient and economically profitable way to increase the degree of transportation process security.

Analyzing the Risk Factors of Hazardous Chemical Road Transportation Accidents Based on Grounded Theory and a Bayesian Network

With the increase in the production and use of hazardous chemicals in China, road transportation safety issues are becoming increasingly prominent. To study the causes of road transportation accidents involving hazardous chemicals, prevent accident occurrence, and realize the safety and sustainable development of the transportation of hazardous chemicals, we combined grounded theory (GT) and a Bayesian network (BN) model to quantify the causal relationship of the interactions among the influencing factors leading to hazardous chemical road transportation accidents that occurred in China in the period of 2017–2020. The influencing factors of these accidents were classified into 5 core categories, 12 main categories, and 28 categories through the GT method, and then a BN-based model was established for these collected accidents. The conditional probability and posterior probability of each influencing factor leading to an accident were determined through BN learning, and then the causal relationship of the interactions between the influencing factors was quantified. The results indicated that the probability of road transportation accidents involving hazardous chemicals considered in this study reaches 72.5% under the combined influence of various factors, and the most likely causal chain of an accident is that equipment failure during the hazardous chemical transportation process contributes to an Unsafe Tanker State, which in turn leads to an accident. The sensitivity analysis confirmed that the key impact factor of hazardous chemical road transportation accidents is equipment failure, followed by improper operation. Overall, this study presents a reference and a foundation for avoiding or reducing risks as much as possible during daily hazardous chemical road transportation operations and risk supervision, realizing safe, sustainable development.

Properties of Superhydrophobic and Acid–Alkali-Resistant Polyester Fabric Produced Using Plasma Processing

During the processes of production, storage, transportation and use of hazardous chemicals, acid–alkali corrosive liquid spatter and leakage would cause serious casualties. In order to protect the lives and health of staff, the surface of fabrics should be treated to obtain hydrophobicity and acid–alkali resistance. In this paper, polyester fabric was used as the base cloth, and polydimethylsiloxane (PDMS) and polytetrafluoroethylene (PTFE) micro-powder were used as the functional materials to fabricate waterproof and breathable fabric with good acid–alkali resistance using a method of plasma pretreatment-impregnation- and plasma-induced crosslinking. The effects of PDMS, PTFE powder and plasma-induced crosslinking on the surface and physical and chemical properties of fabric were investigated. It was found that the use of PDMS and PTFE powder had little effect on the mechanical and wearing comfort properties. However, it could significantly improve the acid–alkali resistance, as the liquid repellent rate of the treated fabric surface was higher than 80%, and the penetration index was lower than 2%.

Dynamic risk assessment method for road transport of hazardous chemicals based on BP neural network algorithm

To achieve a dynamic comprehensive risk assessment of road transport of hazardous chemicals, this paper first collected 213 road transport accidents involving hazardous chemicals that occurred between 2015 and 2020 to build a case library. Then, it established a risk assessment system (including 9 primary indicators and 28 secondary indicators) based on the frequencies of reasons for accident cases and explored the quantification methods for indicators based on their definitions. Subsequently, this paper calculated the weights of indicators based on the backpropagation neural network algorithm and the data in the case library and formulated a model for dynamic risk assessment of road transport of hazardous chemicals. The model takes into account the impact of comprehensive indicators such as drivers, vehicles, goods, roads, and the environment. The accuracy of the model calculation results is 93%. Eventually, the study verified the model using simulation data in real scenarios. The validation results show that the model could achieve a dynamic comprehensive assessment of risks in road transport of hazardous chemicals.

Risk analysis for hazardous chemical vehicle-bridge transportation system: A dynamic Bayesian network model incorporating vehicle dynamics

This study aims to analyze the risk of transporting hazardous chemicals on sea-crossing bridges using a dynamic Bayesian network (DBN) model that incorporates vehicle dynamics. Firstly, the cause-consequence relationship analysis is constructed using the bow-tie (BT) model, which is then translated into a Bayesian network (BN) by mapping algorithms. Based on the dynamic model, the occurrence probabilities of rollover and sideslip under different wind speeds are calculated as conditional probabilities. Secondly, a DBN model that satisfies the Markov assumption and time invariance is established to realize short-term risk prediction. Finally, the proposed model is applied to a sea-crossing bridge in Zhejiang, and other node parameters are obtained by combining the monitoring data of the vehicle-bridge transportation system (VBTS) monitoring platform and expert experience. The results indicate that vehicle failure has the highest impact on VBTS, and unsafe driver behavior and road alignment are the most vulnerable root causes, which should receive more attention. Additionally, wind sensitivity to VBTS is significant and cannot be ignored. The proposed method can effectively address the risks and challenges posed by hazardous chemical transportation on sea-crossing bridges and provides valuable insights with practical application to enhance transportation safety.

Intelligent-Technology-Empowered Active Emergency Command Strategy for Urban Hazardous Chemical Disaster Management

Urban safety production is a core component of social safety and is associated with the production, storage and transport of hazardous chemicals, which are potential sources of disaster in an urban area. Chemicals’ locations in a city present a hidden site of danger, which can easily become disaster sites if supervision is inadequate. Aiming to improve the processes and typical scenarios of the production, storage, transportation and use of hazardous chemicals, this paper proposes an intelligent-technology-empowered active emergency command strategy (ITAECS) for urban hazardous chemical disaster management (UHCDM) in smart–safe cities. This paper aims to provide a strategy for active emergency command that takes into account the disaster source; hidden danger site; or disaster site of hazardous chemicals such as natural gas, gasoline and hydrogen energy based on five aspects: intelligent perception technology and equipment, a dynamically perceived IoT system, the accurate deduction of disaster posture, virtual reality emergency rescue rehearsal and an immersive emergency command platform. This research is conducive to the safety, efficiency and greenness of the whole industrial chain, such as the production, storage, transportation, operation and use of hazardous chemicals. There are difficulties and challenges in introducing ITAECS to urban hazardous chemical production safety and emergency management, such as the need for joint promotion of enterprises, industries and governments; uneven technological development; and several scientific–technological issues to be solved, as well as non-uniform standards. Overall, this paper helps improve the emergency management of urban hazardous chemical safety production.

Propagation of the blast wave in the enclosed blast wall structure (EBWS) at a hazardous chemical storage yard—A mechanism study

The storage and transportation of hazardous chemicals is an essential aspect of modern economic activities. The explosion of hazardous chemicals can cause significant damage to surrounding infrastructure and human life, and has become a major public concern. The enclosed blast wall structure (EBWS) is commonly used in open-air storage yards to segregate explosion hazards. However, the understanding of blast wave propagation in an EBWS is limited. In this study, the propagation of blast waves in an EBWS at a hazardous chemical storage yard was analyzed through a combination of scaled field tests and numerical simulations. The study focuses on reflection and diffraction effects in the access passage, which is connected to the opening of the EBWS for transportation purposes. A decoupling technique was proposed to evaluate the contribution of blast waves propagating through the entrance of the access passage and diffracting from above the blast wall separately. Several special wave propagation effects in EBWS were identified, such as leakage pressure and oblique incidence. The blast load experienced in the access passage were compared with current codes and prediction methods. It was found that the commonly adopted assumptions of a semi-infinite blast wall and normal incidence of blast waves underestimates the peak overpressure in the access passage and could not predict the following multiple overpressure peaks. Finally, a calculation procedure based on the TNT equivalent was proposed to predict the overpressure behind the blast wall with a slant angle.

Research on the prediction model of hazardous chemical road transportation accidents

In recent years, hazardous chemicals road transport accidents have occurred frequently, causing huge casualties and property losses, and accident risk assessment has become the focus of researchers' research. To predict the risk probability value of hazardous chemical road transport accidents, first, we compiled data on road transportation accidents of hazardous chemicals in China in the past five years. And the nine nodes in the Bayesian network (BN) structure were defined in combination with relevant classification standards. The optimal Bayesian network structure for hazardous chemical road transport accidents was determined based on the K2 algorithm and the causalities between the nodes. Second, the node conditional probabilities were derived by parameter learning of the model using Netica, and the validity of the model was verified using the 5-fold cross-validation method. Last, the Bayesian network model of hazardous chemical road transport accidents is used to analyze accident examples, and the accident chain of “rear-end-leakage” is predicted, and the accident is most likely to be disposed of within 3–9 h. The study shows that the derived accident prediction model for hazardous chemical road transportation can reason reasonably about the evolution of accident scenarios and determine the probability values of accident risks under different parameter conditions.

A Quantitative Risk Assessment Model for Domino Accidents of Hazardous Chemicals Transportation

In recent years, hazardous materials transportation accidents have received increasing attention. Previous studies have focused on accidents involving a single vehicle. When vehicles loaded with materials gather on a stretch of road, a potential domino accident might cause terrible incidents. This paper prompts a quantitative risk assessment (QRA) model to estimate the risk of multi-vehicle incidents. The model calculates the possibility of leakage and explosion of hazardous chemicals using a dynamic Bayesian network (DBN). For different types of hazardous chemicals, the model uses event trees to list different scenarios and analyzes the probability of domino accidents caused by each scenario. The FN-curve and potential loss of life (PLL) are used as an index to evaluate social risk. A case involving multiple vehicles in the JinShan District, Shanghai, is analyzed. The result of the case shows that the state of the driver, the type of road, weather factors and the distance between vehicles have vital impacts on the societal risk resulting from hazardous materials transportation accidents.

Simulation of scenario characteristics of LPG tank explosion accident and its contribution to emergency planning: A case study

Leakage and explosion of hazardous chemicals during road transportation can cause serious building damage and casualties, and adoption of highly-efficient emergency rescue measures plays a critical role in reducing accidental hazards. Considering a liquefied petroleum gas (LPG) transport tanker explosion accident that occurred in Wenling, Zhejiang Province, China on June 13, 2020 as example, this study proposes a risk assessment framework. This framework recreates the leakage and explosion of the accident process using FLACS v10.9, suggests plans for evacuation, describes the rescue areas of different levels, and explores the influence of environmental factors on the evacuation and rescue areas. The results show that simulated and predicted distributions of fuel vapour cloud concentration and explosion overpressure can provide a reference basis for rapid rescue activities; the characterization of the dynamic effects of wind speed, wind direction, and temperature with respect to the evacuation and rescue areas can be used as theoretical support for on-site adjustment of rescue forces. The role of obstacles can prevent the expansion of the evacuation areas under low wind-speed conditions, and the presence of highly congested obstacles determines the level of the rescue area. The results obtained are important for the risk analysis and the development of emergency rescue measures in case of explosion accidents associated with transportation of hazardous chemicals on high-hazard and high-sensitive road sections.

CFD simulations of instantaneously released liquefied gas in urban areas: A case study of LPG tank truck accident in Wenling, China

The accidental release and ignition of flammable liquefied gas can cause catastrophic events, such as flash fires, vapor cloud explosions, and boiling liquid expanding vapor explosion, which severely endanger urban residents and the environment. In the current study, a method for simulating liquefied gas instantaneous release accidents was established to explore the vapor cloud diffusion behavior in urban areas. The rare liquefied petroleum gas instantaneous release accident in Wenling, China is considered as a test case. To this end, an instantaneous release source model that considers the gas phase, liquid pool, and droplets was developed. The CFD model was validated using the damage distribution at the accident site. The simulation results revealed that droplets in the release source, terrains, and large obstacles significantly affected the dispersion behavior and extension distance of the vapor cloud. The evaporation of droplets resulted in a mushroom-shaped cloud. In addition, when the cloud was ignited, the area covered by the LFL was 1.94 and 2.07 times larger in the cases with droplets than those without. The method developed in this study is applicable to developing hazardous chemical transportation and emergency response measures in urban environments to maintain the sustainable cities development.