A Novel Method for Source Tracking of Chemical Gas Leakage: Outlier Mutation Optimization Algorithm
Chemical industrial parks, which act as critical infrastructures in many cities, need to be responsive to chemical gas leakage accidents. Once a chemical gas leakage accident occurs, risks of poisoning, fire, and explosion will follow. In order to meet the primary emergency response demands in chemi...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/1/71 |
| _version_ | 1827667624589262848 |
|---|---|
| author | Zhiyu Xia Zhengyi Xu Dan Li Jianming Wei |
| author_facet | Zhiyu Xia Zhengyi Xu Dan Li Jianming Wei |
| author_sort | Zhiyu Xia |
| collection | DOAJ |
| description | Chemical industrial parks, which act as critical infrastructures in many cities, need to be responsive to chemical gas leakage accidents. Once a chemical gas leakage accident occurs, risks of poisoning, fire, and explosion will follow. In order to meet the primary emergency response demands in chemical gas leakage accidents, source tracking technology of chemical gas leakage has been proposed and evolved. This paper proposes a novel method, Outlier Mutation Optimization (OMO) algorithm, aimed to quickly and accurately track the source of chemical gas leakage. The OMO algorithm introduces a random walk exploration mode and, based on Swarm Intelligence (SI), increases the probability of individual mutation. Compared with other optimization algorithms, the OMO algorithm has the advantages of a wider exploration range and more convergence modes. In the algorithm test session, a series of chemical gas leakage accident application examples with random parameters are first assumed based on the Gaussian plume model; next, the qualitative experiments and analysis of the OMO algorithm are conducted, based on the application example. The test results show that the OMO algorithm with default parameters has superior comprehensive performance, including the extremely high average calculation accuracy: the optimal value, which represents the error between the final objective function value obtained by the optimization algorithm and the ideal value, reaches 2.464e-15 when the number of sensors is 16; 2.356e-13 when the number of sensors is 9; and 5.694e-23 when the number of sensors is 4. There is a satisfactory calculation time: 12.743 s/50 times when the number of sensors is 16; 10.304 s/50 times when the number of sensors is 9; and 8.644 s/50 times when the number of sensors is 4. The analysis of the OMO algorithm’s characteristic parameters proves the flexibility and robustness of this method. In addition, compared with other algorithms, the OMO algorithm can obtain an excellent leakage source tracing result in the application examples of 16, 9 and 4 sensors, and the accuracy exceeds the direct search algorithm, evolutionary algorithm, and other swarm intelligence algorithms. |
| first_indexed | 2024-03-10T03:21:55Z |
| format | Article |
| id | doaj.art-3acd8a444da143ec9ce22d2bf4749287 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:21:55Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3acd8a444da143ec9ce22d2bf47492872023-11-23T12:16:28ZengMDPI AGSensors1424-82202021-12-012217110.3390/s22010071A Novel Method for Source Tracking of Chemical Gas Leakage: Outlier Mutation Optimization AlgorithmZhiyu Xia0Zhengyi Xu1Dan Li2Jianming Wei3Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, ChinaShanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, ChinaShanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, ChinaShanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, ChinaChemical industrial parks, which act as critical infrastructures in many cities, need to be responsive to chemical gas leakage accidents. Once a chemical gas leakage accident occurs, risks of poisoning, fire, and explosion will follow. In order to meet the primary emergency response demands in chemical gas leakage accidents, source tracking technology of chemical gas leakage has been proposed and evolved. This paper proposes a novel method, Outlier Mutation Optimization (OMO) algorithm, aimed to quickly and accurately track the source of chemical gas leakage. The OMO algorithm introduces a random walk exploration mode and, based on Swarm Intelligence (SI), increases the probability of individual mutation. Compared with other optimization algorithms, the OMO algorithm has the advantages of a wider exploration range and more convergence modes. In the algorithm test session, a series of chemical gas leakage accident application examples with random parameters are first assumed based on the Gaussian plume model; next, the qualitative experiments and analysis of the OMO algorithm are conducted, based on the application example. The test results show that the OMO algorithm with default parameters has superior comprehensive performance, including the extremely high average calculation accuracy: the optimal value, which represents the error between the final objective function value obtained by the optimization algorithm and the ideal value, reaches 2.464e-15 when the number of sensors is 16; 2.356e-13 when the number of sensors is 9; and 5.694e-23 when the number of sensors is 4. There is a satisfactory calculation time: 12.743 s/50 times when the number of sensors is 16; 10.304 s/50 times when the number of sensors is 9; and 8.644 s/50 times when the number of sensors is 4. The analysis of the OMO algorithm’s characteristic parameters proves the flexibility and robustness of this method. In addition, compared with other algorithms, the OMO algorithm can obtain an excellent leakage source tracing result in the application examples of 16, 9 and 4 sensors, and the accuracy exceeds the direct search algorithm, evolutionary algorithm, and other swarm intelligence algorithms.https://www.mdpi.com/1424-8220/22/1/71Outlier Mutation Optimization algorithmGaussian plume modelrandom walkemergency responseleakage tracking |
| spellingShingle | Zhiyu Xia Zhengyi Xu Dan Li Jianming Wei A Novel Method for Source Tracking of Chemical Gas Leakage: Outlier Mutation Optimization Algorithm Sensors Outlier Mutation Optimization algorithm Gaussian plume model random walk emergency response leakage tracking |
| title | A Novel Method for Source Tracking of Chemical Gas Leakage: Outlier Mutation Optimization Algorithm |
| title_full | A Novel Method for Source Tracking of Chemical Gas Leakage: Outlier Mutation Optimization Algorithm |
| title_fullStr | A Novel Method for Source Tracking of Chemical Gas Leakage: Outlier Mutation Optimization Algorithm |
| title_full_unstemmed | A Novel Method for Source Tracking of Chemical Gas Leakage: Outlier Mutation Optimization Algorithm |
| title_short | A Novel Method for Source Tracking of Chemical Gas Leakage: Outlier Mutation Optimization Algorithm |
| title_sort | novel method for source tracking of chemical gas leakage outlier mutation optimization algorithm |
| topic | Outlier Mutation Optimization algorithm Gaussian plume model random walk emergency response leakage tracking |
| url | https://www.mdpi.com/1424-8220/22/1/71 |
| work_keys_str_mv | AT zhiyuxia anovelmethodforsourcetrackingofchemicalgasleakageoutliermutationoptimizationalgorithm AT zhengyixu anovelmethodforsourcetrackingofchemicalgasleakageoutliermutationoptimizationalgorithm AT danli anovelmethodforsourcetrackingofchemicalgasleakageoutliermutationoptimizationalgorithm AT jianmingwei anovelmethodforsourcetrackingofchemicalgasleakageoutliermutationoptimizationalgorithm AT zhiyuxia novelmethodforsourcetrackingofchemicalgasleakageoutliermutationoptimizationalgorithm AT zhengyixu novelmethodforsourcetrackingofchemicalgasleakageoutliermutationoptimizationalgorithm AT danli novelmethodforsourcetrackingofchemicalgasleakageoutliermutationoptimizationalgorithm AT jianmingwei novelmethodforsourcetrackingofchemicalgasleakageoutliermutationoptimizationalgorithm |