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Preliminary systems architecture in chemical emergency linking the critical information to the mission.
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Situation awareness is an important function module contained in the information system for regional emergency rescue. To improve the existing emergency information system, a situation awareness model of chemical release is constructed containing three levels, i.e. Data Acquisition, Intelligent Analysis and Simulation & Prediction, in which Intelli...
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Context 1
... emergency decision-making and actions rely on the correct situation assessment of accident scenarios. Particularly in cases that flammable or toxic chemicals release and disperse, the deviations are very likely to aggravate the incidence of injuries. By reviewing Endsley's model, preliminary system architecture was established ( Fig. 2.), linking critical information to missions in chemical emergency rescue. For what we are most concerned about, as mentioned in Section 1, Level 3 Simulation & Prediction of situation awareness strongly need Level 2 Intelligence Analysis to detect the unknown source, while Level 1 Data Acquisition provides the raw data in real time. We ...
Citations
... Yee et al. proposed a Bayesian probabilistic inference method to estimate the location and intensity of the leakage source from the concentration data measured by the sensor [12]. Chen et al. introduced a particle swarm optimization algorithm to calculate multiple Sustainability 2024, 16, 1638 3 of 24 leakage source parameters, and verified the effectiveness of this method in leakage source inversion through a large number of simulation tests [13]. Under the concept of refined cost management, the deployment of a large amount of gas monitoring equipment in oil and gas stations will increase the operating cost. ...
... In the process of solving the particle swarm optimization algorithm, the information is transmitted in a single item, which prevents it falling into the early local optimal point and improves the convergence efficiency of the GA. (13) where v id is the d dimensional velocity of particle i in the t iteration; x id is the d dimensional position of particle i in the t iteration; c 1 and c 2 are acceleration factors; r 1 and r 2 are random numbers in [0, 1]; P id and P gd are the historical optimal position of particle I and the historical optimal position in population iteration, respectively; and ω is the inertia factor. Combined with the above three optimization points, the main process of the inversion positioning method based on the improved genetic algorithm in this study is as follows: ...
The safe and stable operation of oil and gas stations makes a positive contribution to the stability and reliability of the natural gas supply. In order to reduce the impact of leakage and diffusion accidents in the station, it is necessary to develop an effective method to monitor and locate the leakage source quickly and accurately. This study proposes a multi-point monitoring data grid model to achieve the full-coverage monitoring of oil and gas stations. In addition, on this basis, a leakage source inversion positioning model is established to realize the leakage positioning of the station. A field experiment was carried out with an oil and gas station as an example. The results show that the optimal layout of points needs to consider the influence of environmental factors, confirmed by computational fluid dynamics (CFD) simulation. The optimized data interpolation not only reduces the cost of the monitoring point layout. In addition, through the comparison of multi-objective optimization algorithms and a robustness test, it can be found that the convergence efficiency and accuracy of the inversion positioning algorithm in this study have been greatly improved. Compared with the manual auxiliary positioning method, this method effectively solves the problem of leakage monitoring and positioning of oil and gas stations, and can achieve the purpose of leakage risk monitoring and “reducing cost and increasing efficiency”.
... There exist a wide variety of methods for iteratively improving a population of one or more trial solutions using this approach. These include genetic algorithms (Holland, 1975;Goldberg, 1989;Haupt, 2005;Allen et al., 2006Allen et al., , 2007Haupt et al., 2006Haupt et al., , 2007Long et al., 2010;Rodriguez et al., 2011;Schmehl et al., 2012), evolutionary strategy (Beyer and Schwefel, 2002;Cervone et al., 2009), simulated annealing (Press et al., 2002;Thomson et al., 2007), Bayesian inference with Markov Chain Monte Carlo (MCMC) sampling (Braak, 2006;Delle Monache et al., 2008;Robins et al., 2009), gradient descent (Bieringer et al., 2010(Bieringer et al., , 2015Rodriguez, 2012), particle swarm (Kennedy and Eberhart, 2001;Guohua and Longkai, 2014), four-dimension variational (Elbern and Schmidt, 1999) and many others. All of these algorithms share the requirement for a component to create the initial trial solution(s), a component for updating them, and a component for evaluating their quality. ...
Modeling the downwind hazard area resulting from the unknown release of an atmospheric contaminant requires estimation of the source characteristics of a localized source from concentration or dosage observations and use of this information to model the subsequent transport and dispersion of the contaminant. This source term estimation problem is mathematically challenging because airborne material concentration observations and wind data are typically sparse and the turbulent wind field chaotic. Methods for addressing this problem fall into three general categories: forward modeling, inverse modeling, and nonlinear optimization. Because numerous methods have been developed on various foundations, they often have a disparate nomenclature. This situation poses challenges to those facing a new source term estimation problem, particularly when selecting the best method for the problem at hand. There is, however, much commonality between many of these methods, especially within each category. Here we seek to address the difficulties encountered when selecting an STE method by providing a synthesis of the various methods that highlights commonalities, potential opportunities for component exchange, and lessons learned that can be applied across methods.
Accurate identification of source information (i.e., source strength and location) is crucial for the air pollution control or effective accidental response. Optimization inversion based on bio-inspired algorithms (BIOs) is an effective method for estimating source information. However, the impacts of different BIOs and the shared parameter of population size in BIOs on source inversion performance have not been revealed. Here the source inversion performance (i.e., accuracy and robustness) of six typical BIOs [i.e., bacterial foraging optimization algorithm (BFO), chicken swarm optimization algorithm (CSO), differential evolution algorithm (DE), genetic algorithm (GA), particle swarm optimization (PSO), and seeker optimization algorithm (SOA)], and their population sizes are evaluated based on the Prairie Grass dataset which covering different atmospheric conditions (i.e., Pasquill stability classes A, B, C, D, E, and F). Results indicated the population size has substantial influence on source inversion. The accuracy of all BIOs in source strength fluctuated greatly when the population size was small, whereas, tended to be stable as the population size increased. Overall, the BFO had the best accuracy with lowest deviations (74.5% for source strength and 29.7 m for location parameter x0), whereas SOA had the best robustness for all source parameters. Atmospheric conditions indicated an obvious influence on the inversion performance of the BIOs. The BFO and CSO performed the best with the lowest deviations [137.5 and 26.7% for unstable conditions (A, B, and C) and stable condition (E), respectively], all algorithms are comparable (67.4 ± 2.1%) in neutral condition (D), and BFO and CSO had the comparable performances (23.2 and 24.3%) and performed better under extremely stable condition (F). This study enhances the understanding of the factors influencing source inversion and provides a reference for the selection of appropriate bio-inspired algorithms and the reasonable setting of population size parameter for source inversion in practical environmental management.
Abrupt air pollution accidents can endanger people's health and destroy the local ecological environment. The appropriate emergency response can minimize the harmful effects of accidents and protect people's lives and property. This paper provides an overview of the key emergency response technologies for abrupt air pollution accidents around the globe with emphasis on the major achievements that China has obtained in recent years. With decades of effort, China has made significant progress in emergency monitoring technologies and equipment, source estimation technologies, pollutant dispersion simulation technologies and others. Many effective domestic emergency monitoring instruments (e.g., portable DOAS/FT-IR systems, portable FID/PID systems, portable GC-MS systems, scanning imaging remote sensing systems, and emergency monitoring vehicles) had been developed which can meet the demands for routine emergency response activities. A monitoring layout technique combining air dispersion simulation, fuzzy comprehensive evaluation, and a post-optimality analysis was proposed to identify the optimal monitoring layout scheme under the constraints of limited monitoring resources. Multiple source estimation technologies, including the forward method and the inversion method, have been established and evaluated under various scenarios. Multi-scale dynamic pollution dispersion simulation systems with high temporal and spatial resolution were further developed. A comprehensive emergency response platform integrating database support, source estimation, monitoring schemes, fast monitoring of pollutants, pollution predictions and risk assessment was developed based on the technical idea of "source identification - model simulation - environmental monitoring" dynamic interactive feedback. It is expected that the emergency response capability for abrupt air pollution accidents will gradually improve in China.
Estimating accurately airborne pollutant emissions source information (source strength and location) is important for achieving effective air pollution management or adequate emergency responses to accidents. Inversion method is one of the useful tools to identify the source parameters. The atmospheric dispersion scheme has been proven to be the key to determining the source inversion performance by influencing the accuracy of the dispersion models. Modifying the atmospheric dispersion scheme is an important potential method to improve the inversion performance, but this has not been studied previously. To fill this gap, a novel approach for parameter sensitivity analysis combined with an optimization method was proposed to improve the source inversion performance by optimizing empirical scheme. The dispersion coefficients σy and σz of the typical BRIGGS scheme under different atmospheric dispersion conditions were optimized and used for air pollutant dispersion and source inversion. The results showed that the prediction performance of the air pollutant concentrations was greatly improved with statistical indices |FB| and NMSE decreased by 0.22 and 2.07, respectively; FAC2 and R increased by 0.10, and 0.08, respectively. For source inversion, the results of the significance analysis suggested that the accuracy in the source strength and location parameter (x0) were both significantly improved by ∼271% (relative deviation reduced from 60.0% to 16.2%) and ∼121% (absolute deviation reduced from 27.6 to 12.5 m). The improvement of source strength inversion accuracy was more significant under unstable atmospheric conditions (stability class A, B, and C); the mean absolute relative deviation was reduced by 97.5%. These results can help to obtain more accurate source information and to provide reliable reference for air pollution managements or emergency response to accidents. This study provides a novel and versatile approach to improve estimation performance of pollutant emission sources and enhances our understanding of source inversion.
