Focusing on the effective configuration of emergency response systems in utility tunnels, this study proposes an innovative approach to optimize existing emergency response systems based on a consequence rapid prediction model and genetic algorithm. In the proposed approach, the interactions between different emergency response components are considered to perform a rapid gas dispersion prediction. Furthermore, the predicted gas concentration distribution is employed to estimate the quantitative explosion risks by combining the equivalent cloud method and the Baker-Strehlow model. Finally, the cumulative and cascading risk index are proposed and combined for systematic optimization by using a genetic algorithm. A case study is performed to demonstrate the feasibility of the proposed approach. The results indicate that the optimized emergency response systems effectively reduce both the cumulative and cascading risk level. This study provides technical support for emergency response system design and helps to improve the safety-risk-control capabilities of utility tunnels.

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As an effective way to facilitate the increasing demand for reliable infrastructure, energy supply and sustainable urban development, underground utility tunnels have been developed rapidly in recent years. Due to the widespread distribution of utility tunnels, the safe operation of natural gas pipelines accommodated in utility tunnels has caused great concern considering fire, explosion, and other coupling consequences induced by the gas pipeline leakage. However, the limited information on leakage source terms in accidental leakage scenarios could preclude timely consequence assessment and effective emergency response. In this study, a BI-IEnKF coupling source term estimation (STE) model is developed, with the combination of gas dispersion model, Bayesian inference (BI) and iterative ensemble Kalman filter (IEnKF) method, to achieve the effective source term estimation (including leakage location and leakage rate) and gas concentration distribution prediction. The newly developed model is first evaluated by the twin experiment with good reliability and accuracy. Furthermore, three contributing factors affecting the performance of the developed BI-IEnKF coupling STE model were investigated to assist parameter selection for practical use. Additionally, the novel application of mobile sensors serving as an alternative for fixed sensors is explored, and an application framework is sequentially given to guide the deployment of the developed coupling model in utility tunnels. The results show that the developed model has great performance in accuracy, efficiency and robustness, as well as the potential to be applied in actual utility tunnel scenarios. This study can provide technical supports for safety control and emergency response in the case of natural gas pipeline leakage accidents in utility tunnels. Also, it could be helpful to reasonable references for gas lekage monitoring system design.@en

As a kind of critical infrastructure of energy transportation, so-called ‘utility tunnels’ have been developed around the world. Hosting a natural gas pipeline inside the natural gas compartment of a utility tunnel facilitates its maintenance but also brings potential explosion concerns due to the confined space. Although some work focuses on the risk analysis of the natural gas pipeline inside utility tunnels, a resilience assessment is needed for dynamically modeling leakage with interacting safety barriers. In this paper, a resilience assessment model of the natural gas compartment of utility tunnels is elaborated based on numerical simulation considering interacting barrier modeling, including sensors, a ventilation system, and the possibility of emergency shutdown. Based on the calculated (natural gas compartment) resilience for casualty and economic loss, ventilation strategies and sensor layouts can be recommended and optimization is possible. Meanwhile, the delay effect of safety barriers is investigated in this work, and the unequal interval layouts of sensors are explored and proven to be effective without any further cost. The proposed resilience assessment model can be important to further improve the safety management of utility tunnels and other confined spaces where hazardous gases are transported.@en

Toxic gas leakage represents a type of major process accident scenario threatening human life. Technical and non-technical safety barriers are employed to prevent toxic gas leakage accidents or mitigate the possible catastrophic consequences. Evacuation must be executed in severe toxic gas release scenarios. The performance assessment of technical safety barriers and evacuations in these accident scenarios, although very important, has never been investigated in previous studies. This paper proposes an approach integrating event tree analysis (ETA), computational fluid dynamics (CFD) simulation, and evacuation modeling (EM), for risk assessment of toxic gas leakage accidents in chemical plants. In the proposed approach, the spatiotemporal distribution of toxic gas is predicted by CFD simulations. A dynamic evacuation is determined by a cellular automaton (CA)-based model. Synergistic interventions resulting from technical safety barriers and evacuations are considered in the risk assessment. Considering safety barrier failures in the event tree analysis, individual fatality risks due to toxic gas leakage scenarios are calculated. For illustrative purposes, the proposed method is applied to a case of ammonia leakage. The results show that worse scenarios would be ignored without considering the failure probabilities of technical safety barriers, which can cause underestimated individual fatality risks. Timely gas detection & alarm has the potential to expedite the starting time of evacuations and thus may shorten the time that evacuees stay in the toxicity area to reduce individual fatality risks.@en

Crime issues have been attracting widespread attention from citizens and managers of cities due to their unexpected and massive consequences. As an effective technique to prevent and control urban crimes, the data-driven spatial–temporal crime prediction can provide reasonable estimations associated with the crime hotspot. It thus contributes to the decision making of relevant departments under limited resources, as well as promotes civilized urban development. However, the deficient performance in the aspect of the daily spatial–temporal crime prediction at the urban-district-scale needs to be further resolved, which serves as a critical role in police resource allocation. In order to establish a practical and effective daily crime prediction framework at an urban police-district-scale, an “online” integrated graph model is proposed. A residual neural network (ResNet), graph convolutional network (GCN), and long short-term memory (LSTM) are integrated with an attention mechanism in the proposed model to extract and fuse the spatial–temporal features, topological graphs, and external features. Then, the “online” integrated graph model is validated by daily theft and assault data within 22 police districts in the city of Chicago, US from 1 January 2015 to 7 January 2020. Additionally, several widely used baseline models, including autoregressive integrated moving average (ARIMA), ridge regression, support vector regression (SVR), random forest, extreme gradient boosting (XGBoost), LSTM, convolutional neural network (CNN), and Conv-LSTM models, are compared with the proposed model from a quantitative point of view by using the same dataset. The results show that the predicted spatial–temporal patterns by the proposed model are close to the observations. Moreover, the integrated graph model performs more accurately since it has lower average values of the mean absolute error (MAE) and root mean square error (RMSE) than the other eight models. Therefore, the proposed model has great potential in supporting the decision making for the police in the fields of patrolling and investigation, as well as resource allocation.@en

Natural gas compartment accommodated in utility tunnels is beneficial in meeting the pressing demand of energy supply and sustainable urban environment. However, the leaking gas characterized by flammable and explosive can pose a huge threat to the safe operation of the utility tunnel. When an unexpected gas leakage accident happens in the actual situation, the prior information associated with the leakage source is commonly unclear or unknown. Therefore, the absence of an available tool for reasonable leakage and dispersion prediction in the above scenario precludes the timely and appropriate emergency response treatment. In this study, a three-dimensional source term estimation (3D-STE) model with the combination of the computational fluid dynamics (CFD) and ensemble Kalman filter (EnKF) algorithm is proposed to achieve spatiotemporal gas concentration prediction and gas emission source estimation. In the proposed approach, the observation data can be incorporated into the gas dispersion simulations continuously, thus the simulation results can be revised by the observation data and the source term estimation of gas leakage can be achieved by employing the EnKF algorithm. A twin experiment is employed to validate the effectiveness and practicability of the proposed model. The results show that the proposed model can revise the prior errors in the gas leakage rate significantly and obtain an accurate prediction of gas concentration distribution as well as gas leakage rate. A feasible framework is also proposed serving as a good paradigm for the 3D-STE model application. This study helps for consequence assessment and emergency response of gas leakage accidents in utility tunnels.@en

As a kind of clean fuel, increasing quantities of natural gas have been transported as liquefied natural gas (LNG) worldwide. The safety of LNG storage has gained the concerns from the public due to the potential severe consequences that may arise from LNG leakage. In this paper, a three-dimensional model with the combination of computational fluid dynamics (CFD) and the ensemble Kalman filter (EnKF) is proposed to predict LNG vapor dispersion and estimate the strength of the LNG leakage source. The LNG vapor dispersion CFD model is validated by the experimental data with good feasibility, and is further demonstrated with the reasonable modeling of the characteristics of the LNG vapor dispersion in a typical receiving terminal. The effectiveness of the proposed CFD and EnKF coupling model is evaluated and validated by a twin experiment. The results of the twin experiment indicate that the proposed CFD and EnKF coupling model allows the integration of observation data into the CFD simulations to enhance the prediction accuracy of the LNG vapor spatial-temporal distribution and thereby realizing a reasonable estimation of the LNG leakage velocity under complex environments. This study can provide technical supports for safety control, loss prevention and emergency response in case of LNG leakage accidents.

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Gas drainage system is a critical technique to prevent gas outbursts in the underground coal mine. The leakage of gas drainage pipelines can pose serious threats to the safety production of underground mining. In this paper, a multi-factors gas drainage pipeline leakage and diffusion (GDPLD) model is proposed based on the OpenFOAM platform, which can analyze the leakage and diffusion characteristics inside the pipelines. With field measurement data in a coal mine, the GDPLD model is verified with good practicability. Furthermore, scenario analysis in the context of different leak sizes, locations, and pipeline diameters is presented to evaluate the specific characteristics of gas leakage and diffusion inside the pipeline with negative pressure. The results showed that the leakage accident close to the pump station with a large leak size and small pipeline diameter usually represents the worst case, and when gas sensors are installed downstream of the leakage location, it is helpful to realize effective detection of the leakage accident. This study can help to improve the understanding of the leakage and diffusion characteristics of gas drainage pipelines and provide technical supports for the monitoring system design of the gas drainage pipelines in underground coal mines.

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