Location of contaminant emission source in atmosphere based on optimal correlated matching of concentration distribution

[Display omitted] •OCMCD model based on distribution matching to locate the emission source is proposed.•The method improves the efficiency compared with multiple parameters estimation.•OCMCD model with modified terms improves the estimation property.•OCMCD method depends less on absolute data noise...

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Veröffentlicht in:Process safety and environmental protection 2018-07, Vol.117, p.498-510
Hauptverfasser: Ma, Denglong, Tan, Wei, Wang, Qingsheng, Zhang, Zaoxiao, Gao, Jianmin, Wang, Xiaoqiao, Xia, Fengshe
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container_end_page 510
container_issue
container_start_page 498
container_title Process safety and environmental protection
container_volume 117
creator Ma, Denglong
Tan, Wei
Wang, Qingsheng
Zhang, Zaoxiao
Gao, Jianmin
Wang, Xiaoqiao
Xia, Fengshe
description [Display omitted] •OCMCD model based on distribution matching to locate the emission source is proposed.•The method improves the efficiency compared with multiple parameters estimation.•OCMCD model with modified terms improves the estimation property.•OCMCD method depends less on absolute data noises.•The method can be used to determine source location in the case of mobile sensor. Source location is crucial to manage contaminant emissions in atmosphere, In order to determine the source location without dependence on the absolute measurement data, a method based on optimal correlated matching of concentration distribution (OCMCD) was proposed. First, the estimation efficiency, accuracy and dependence on source strength of OCMCD were compared with the common method which estimates multiple parameters of the source term simultaneously. The results show that the method of OCMCD performs better than the common multiple parameters estimation method based on the mean errors between prediction and measurement in both estimation accuracy and efficiency. The test results with different sets of source strength manifest that OCMCD relies minimally on the source strength Then, a wind direction correction parameter and a weighted term of normalization concentration error were introduced into the model to compensate some missed information and improve the location results. The influence of data noises on the estimation accuracy of OCMCD method was also verified by adding extra manual noises on the measurement data. Then, the dependence of estimation performance with OCMCD method on atmosphere conditions were investigated statistically with experiment release cases. The results showed that source location was identified well in most of cases. Finally, OCMCD method was extended to determine the source location during the source trace process with a mobile sensor. The test results with a simulation scenario based on Zigzag search strategy demonstrate that the source location determined by OCMCD source criterion is much closer to the real source position than that determined by the criterion of the maximum concentration. Therefore, the results have proven the feasibility and superiority of OCMCD proposed in this paper to estimate source location in cases of both static sensor distribution and mobile sensors. OCMCD will be a potentially useful method to identify emission source location in atmosphere.
doi_str_mv 10.1016/j.psep.2018.05.028
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Source location is crucial to manage contaminant emissions in atmosphere, In order to determine the source location without dependence on the absolute measurement data, a method based on optimal correlated matching of concentration distribution (OCMCD) was proposed. First, the estimation efficiency, accuracy and dependence on source strength of OCMCD were compared with the common method which estimates multiple parameters of the source term simultaneously. The results show that the method of OCMCD performs better than the common multiple parameters estimation method based on the mean errors between prediction and measurement in both estimation accuracy and efficiency. The test results with different sets of source strength manifest that OCMCD relies minimally on the source strength Then, a wind direction correction parameter and a weighted term of normalization concentration error were introduced into the model to compensate some missed information and improve the location results. The influence of data noises on the estimation accuracy of OCMCD method was also verified by adding extra manual noises on the measurement data. Then, the dependence of estimation performance with OCMCD method on atmosphere conditions were investigated statistically with experiment release cases. The results showed that source location was identified well in most of cases. Finally, OCMCD method was extended to determine the source location during the source trace process with a mobile sensor. The test results with a simulation scenario based on Zigzag search strategy demonstrate that the source location determined by OCMCD source criterion is much closer to the real source position than that determined by the criterion of the maximum concentration. Therefore, the results have proven the feasibility and superiority of OCMCD proposed in this paper to estimate source location in cases of both static sensor distribution and mobile sensors. OCMCD will be a potentially useful method to identify emission source location in atmosphere.</description><identifier>ISSN: 0957-5820</identifier><identifier>EISSN: 1744-3598</identifier><identifier>DOI: 10.1016/j.psep.2018.05.028</identifier><language>eng</language><publisher>Rugby: Elsevier B.V</publisher><subject>Computer simulation ; Contaminants ; Criteria ; Dependence ; Emission ; Emissions ; Error correction ; Gas emission ; Hazard identification ; Hazardous materials ; Matching ; Optimization algorithms ; Parameter estimation ; Source term estimation ; Source trace ; Wind direction</subject><ispartof>Process safety and environmental protection, 2018-07, Vol.117, p.498-510</ispartof><rights>2018 Institution of Chemical Engineers</rights><rights>Copyright Elsevier Science Ltd. 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Source location is crucial to manage contaminant emissions in atmosphere, In order to determine the source location without dependence on the absolute measurement data, a method based on optimal correlated matching of concentration distribution (OCMCD) was proposed. First, the estimation efficiency, accuracy and dependence on source strength of OCMCD were compared with the common method which estimates multiple parameters of the source term simultaneously. The results show that the method of OCMCD performs better than the common multiple parameters estimation method based on the mean errors between prediction and measurement in both estimation accuracy and efficiency. The test results with different sets of source strength manifest that OCMCD relies minimally on the source strength Then, a wind direction correction parameter and a weighted term of normalization concentration error were introduced into the model to compensate some missed information and improve the location results. The influence of data noises on the estimation accuracy of OCMCD method was also verified by adding extra manual noises on the measurement data. Then, the dependence of estimation performance with OCMCD method on atmosphere conditions were investigated statistically with experiment release cases. The results showed that source location was identified well in most of cases. Finally, OCMCD method was extended to determine the source location during the source trace process with a mobile sensor. The test results with a simulation scenario based on Zigzag search strategy demonstrate that the source location determined by OCMCD source criterion is much closer to the real source position than that determined by the criterion of the maximum concentration. Therefore, the results have proven the feasibility and superiority of OCMCD proposed in this paper to estimate source location in cases of both static sensor distribution and mobile sensors. 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Source location is crucial to manage contaminant emissions in atmosphere, In order to determine the source location without dependence on the absolute measurement data, a method based on optimal correlated matching of concentration distribution (OCMCD) was proposed. First, the estimation efficiency, accuracy and dependence on source strength of OCMCD were compared with the common method which estimates multiple parameters of the source term simultaneously. The results show that the method of OCMCD performs better than the common multiple parameters estimation method based on the mean errors between prediction and measurement in both estimation accuracy and efficiency. The test results with different sets of source strength manifest that OCMCD relies minimally on the source strength Then, a wind direction correction parameter and a weighted term of normalization concentration error were introduced into the model to compensate some missed information and improve the location results. The influence of data noises on the estimation accuracy of OCMCD method was also verified by adding extra manual noises on the measurement data. Then, the dependence of estimation performance with OCMCD method on atmosphere conditions were investigated statistically with experiment release cases. The results showed that source location was identified well in most of cases. Finally, OCMCD method was extended to determine the source location during the source trace process with a mobile sensor. The test results with a simulation scenario based on Zigzag search strategy demonstrate that the source location determined by OCMCD source criterion is much closer to the real source position than that determined by the criterion of the maximum concentration. Therefore, the results have proven the feasibility and superiority of OCMCD proposed in this paper to estimate source location in cases of both static sensor distribution and mobile sensors. 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subjects Computer simulation
Contaminants
Criteria
Dependence
Emission
Emissions
Error correction
Gas emission
Hazard identification
Hazardous materials
Matching
Optimization algorithms
Parameter estimation
Source term estimation
Source trace
Wind direction
title Location of contaminant emission source in atmosphere based on optimal correlated matching of concentration distribution
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