Hazardous Source Estimation Using an Artificial Neural Network, Particle Swarm Optimization and a Simulated Annealing Algorithm

Wang

et al. 2020

Symmetry

Self Cite

With the profound understanding of the world, modeling and simulation has been used to solve the problems of complex systems. Generally, mechanism-models are often used to model the engineering systems following the Newton laws, and this kind of modeling approach is called white-box modeling; however, when the internal structure and characteristics of some systems are hard to understand, the black-box modeling based on statistic and data-modeling is often used. For most complex real systems, a single modeling approach can hardly describe the target system accurately. In this paper, we firstly discuss and compare the white-box and black-box modeling approaches. Then, to mitigate the limitations of these two modeling methods in mechanism-partially-observed systems, the gray-box based modeling approach integrating both a mechanism model and data model is proposed. In order to explain the idea of gray-box based modeling, the atmosphere dispersion modeling is studied in practical cases from two symmetric aspects. Specifically, the framework of data assimilation is used to illustrate the modeling from white-box to gray-box, while the Gauss features based Support Vector Regression (SVR) models are used to illustrate the modeling from black-box to gray-box. To verify the feasibility of the gray-box modeling method, we conducted both simulation experiments and real dataset symmetry experiments. The experiment results show the enhanced performance of the gray-box based modeling approach. In the end, we expect that this gray-box based modeling approach will be an alternative modeling approach for different existing systems.

Section: Case 2: Atmospheric Dispersion Modeling Methods Based On Gausmentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

The Gray-Box Based Modeling Approach Integrating Both Mechanism-Model and Data-Model: The Case of Atmospheric Contaminant Dispersion

Wang

et al. 2020

Symmetry

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“…In Formula (9), fcpep is a site-specific constant. Taking into the characteristics of source estimation methods [28,29,30,31], we selected two main factors to model the probability fp. A release in site i starts at time t and lasts for ki time slices with rsi release spots.…”

Section: Chemical Cluster Environmental Protection Patrolling Gamementioning

confidence: 99%

Strategically Patrolling in a Chemical Cluster Addressing Gas Pollutants’ Releases through a Game-Theoretic Model

Zhu

et al. 2019

IJERPH

Self Cite

Chemical production activities in chemical clusters, if not well managed, will pose great threats to the surrounding air environment and impose great burden on emergency handling. Therefore, it is urgent and substantial in a chemical cluster to develop proper and suitable pollution controlling strategies for an inspection agency to monitor chemical production processes. Apart from the static monitoring resources (e.g., monitoring stations and gas sensor modules), patrolling by mobile vehicle resources is arranged for better detecting the illegal releasing behaviors of emission spots in different chemical plants. However, it has been proven that the commonly used patrolling strategies (i.e., the fixed route strategy and the purely randomized route strategy) are non-optimal and fail to interact with intelligent chemical plants. Therefore, we proposed the Chemical Cluster Environmental Protection Patrolling (CCEPP) game to tackle the problem in this paper. Through combining the source estimation process, the game is modeled to detect the illegal releasing behaviors of chemical plants by randomly and strategically arranging the patrolling routes and intensities in different chemical sites. In this game-theoretic model, players (patroller and chemical sites), strategies, payoffs, and game solvers are modeled in sequence. More importantly, this game model also considers traffic delays or bounded cognition of patrollers on patrolling plans. Therefore, a discrete Markov decision process was used to model this stochastic process. Further, the model is illustrated by a case study. Results imply that the patrolling strategy suggested by the CCEPP game outperforms both the fixed route strategy and the purely randomized route strategy.

“…In addition, the swarm intelligent optimization (SIO) algorithms have also been adopted to solve the inverse problem in gas source determination. Ma and Zhang [29] and Wang et al [30] employed particle swarm optimization (PSO) to estimate hazardous source term. In fact, heuristic algorithms are always inspired from the biological behavior or the natural law in the real world.…”

Section: Introductionmentioning

confidence: 99%

An Improved Firefly Algorithm for Gas Emission Source Parameter Estimation in Atmosphere

Gao

Zhang

et al. 2019

IEEE Access

It is important to estimate the source term for a gas emission event in atmosphere. Optimization method is one of the useful tools to identify the source parameters by solving the inverse problem. Swarm intelligent optimization (SIO) algorithm has been used to estimate the source term successfully. However, there are still some issues for the SIO method. Therefore, an active firefly algorithm (AFA) method was proposed to improve the estimation performance of common passive firefly algorithm (PFA) for source estimation. Then, the release experiment cases were used to test the AFA method. The comparison results prove that AFA has much higher computation efficiency than PFA and PSO as well as higher estimation accuracy. Further, different effect factors on the performance of AFA were discussed. Compared with common PFA, the estimation results of AFA are more robust with different population number, and the estimation accuracy of AFA with less population scale is better than that of PFA. The estimation of AFA with less generation is better than that of PFA, and the computation efficiency of AFA is improved significantly. Finally, the effect of sensor numbers on AFA method was discussed. The estimation accuracy increases with the sensor numbers. AFA can still obtain a better estimation result with less sensor amount than PFA. Hence, the proposed AFA method has better performance than the commonly used SIO method to estimate the atmospheric source term. It is a potentially useful method for gas emission inverse problem.