Estimating contaminant source in chemical industry park using UAV-based monitoring platform, artificial neural network and atmospheric dispersion simulation

Qiu, Sihang; Chen, Bin; Wang, Rongxiao; Zhu, Zhengqiu; Wang, Yuan; Qiu, Xiaogang

doi:10.1039/c7ra05637k

Cited by 27 publications

(16 citation statements)

References 28 publications

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“…Other than the mechanism-modeling, data-modeling approaches like machine learning for black-box system is also widely used in atmospheric dispersion [24,35,36]. Unlike the mechanism model, the machine learning method builds a mapping relationship between specific parameters and dispersion concentration through a predefined training set [37]. The training sets are always based on some datasets of real experiments, e.g., the Prairie Grass dataset [38] and the Indianapolis dataset [39].…”

Section: Atmospheric Dispersion Modeling Methodsmentioning

confidence: 99%

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

Chen

Wang

et al. 2020

Symmetry

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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.

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Section: Atmospheric Dispersion Modeling Methodsmentioning

confidence: 99%

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

Chen

Wang

et al. 2020

Symmetry

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“…Similar to previous research [ 31 , 42 ], a chemical cluster in Shanghai is selected as our study area in this paper. Figure 4 shows a concise GIS map of a chemical cluster in Shanghai, China.…”

Section: Datasets and Experimental Set-upsmentioning

confidence: 99%

A data-driven approach for optimal design of integrated air quality monitoring network in a chemical cluster

et al. 2018

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The chemical industry is of paramount importance to the world economy and this industrial sector represents a substantial income source for developing countries. However, the chemical plants producing inside an industrial district pose a great threat to the surrounding atmospheric environment and human health. Therefore, designing an appropriate and available air quality monitoring network (AQMN) is essential for assessing the effectiveness of deployed pollution-controlling strategies and facilities. As monitoring facilities located at inappropriate sites would affect data validity, a two-stage data-driven approach constituted of a spatio-temporal technique (i.e. Bayesian maximum entropy) and a multi-objective optimization model (i.e. maximum concentration detection capability and maximum dosage detection capability) is proposed in this paper. The approach aims at optimizing the design of an AQMN formed by gas sensor modules. Owing to the lack of long-term measurement data, our developed atmospheric dispersion simulation system was employed to generate simulated data for the above method. Finally, an illustrative case study was implemented to illustrate the feasibility of the proposed approach, and results imply that this work is able to design an appropriate AQMN with acceptable accuracy and efficiency.

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“…To conduct surveillance on these chemical plants, an inspection agency is equipped with

high-accuracy air quality monitoring stations, and a large number of gas sensor modules spread all over the chemical cluster. The successful detection probability of the irregularities of chemical plants through source estimation methods [ 26 , 27 , 28 , 29 ], with only the discharging data from the gas sensor modules, is defined as

, while the successful detection probability with the discharging data, from the integrated information of monitoring stations and the gas sensor modules, is defined as

. Clearly, the value of

is larger than that of

because the measurements from the high-accuracy air quality monitoring stations are more accurate and reliable.…”

Section: Model Descriptionmentioning

confidence: 99%

“…Figure 2 shows a typical refinery map of a chemical industrial park in Shanghai, China, which is also the study area used in previous research [ 8 , 26 ]. The detailed information and explanations of this case are also provided in these research studies, and interested readers are referred to these references.…”

Section: Case Studymentioning

confidence: 99%

An Extended Chemical Plant Environmental Protection Game on Addressing Uncertainties of Human Adversaries

Zhu

Chen

Qiu

et al. 2018

IJERPH

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Chemical production activities in industrial districts pose great threats to the surrounding atmospheric environment and human health. Therefore, developing appropriate and intelligent pollution controlling strategies for the management team to monitor chemical production processes is significantly essential in a chemical industrial district. The literature shows that playing a chemical plant environmental protection (CPEP) game can force the chemical plants to be more compliant with environmental protection authorities and reduce the potential risks of hazardous gas dispersion accidents. However, results of the current literature strictly rely on several perfect assumptions which rarely hold in real-world domains, especially when dealing with human adversaries. To address bounded rationality and limited observability in human cognition, the CPEP game is extended to generate robust schedules of inspection resources for inspection agencies. The present paper is innovative on the following contributions: (i) The CPEP model is extended by taking observation frequency and observation cost of adversaries into account, and thus better reflects the industrial reality; (ii) Uncertainties such as attackers with bounded rationality, attackers with limited observation and incomplete information (i.e., the attacker’s parameters) are integrated into the extended CPEP model; (iii) Learning curve theory is employed to determine the attacker’s observability in the game solver. Results in the case study imply that this work improves the decision-making process for environmental protection authorities in practical fields by bringing more rewards to the inspection agencies and by acquiring more compliance from chemical plants.

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Estimating contaminant source in chemical industry park using UAV-based monitoring platform, artificial neural network and atmospheric dispersion simulation

Abstract: The ANN-based source estimation approach can effectively locate and quantify the emission source using data from a UAV.

Cited by 27 publications

References 28 publications

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

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

A data-driven approach for optimal design of integrated air quality monitoring network in a chemical cluster

An Extended Chemical Plant Environmental Protection Game on Addressing Uncertainties of Human Adversaries

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