A novel CFD-MILP-ANN approach for optimizing sensor placement, number, and source localization in large-scale gas dispersion from unknown locations
Illegal practices like open electronic waste incineration release hazardous pollutants, endangering the environment and human health. The Internet of Things (IoT) enables online real-time gas concentrations, but its capability to predict leak sources accurately remains a challenge. A large amount of...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Digital Chemical Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772508124000784 |
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| author | Yiming Lang Michelle Xin Yi Ng Kai Xiang Yu Binghui Chen Peng Chee Tan Khang Wei Tan Weng Hoong Lam Parthiban Siwayanan Kek Seong Kim Thomas Shean Yaw Choong Joon Yoon Ten Zhen Hong Ban |
| author_facet | Yiming Lang Michelle Xin Yi Ng Kai Xiang Yu Binghui Chen Peng Chee Tan Khang Wei Tan Weng Hoong Lam Parthiban Siwayanan Kek Seong Kim Thomas Shean Yaw Choong Joon Yoon Ten Zhen Hong Ban |
| author_sort | Yiming Lang |
| collection | DOAJ |
| description | Illegal practices like open electronic waste incineration release hazardous pollutants, endangering the environment and human health. The Internet of Things (IoT) enables online real-time gas concentrations, but its capability to predict leak sources accurately remains a challenge. A large amount of historical data is required to train the source localization model, as gas dispersion is affected by wind speed and wind direction. Furthermore, sensor placement critically affects precise detection and prediction. This study introduces an innovative approach integrating Computational Fluid Dynamics (CFD), Mixed-Integer Linear Programming (MILP), and Artificial Neural Network modeling (ANN). CFD was utilized for machine learning model training. The MILP was used to optimize sensor placement, while the ANN model was used to optimize sensor number. The source localization model was again realized by the ANN model with optimized sensors data. The trained model was able to identify the unknown illegal electronic waste treatment locations with 97.22 % accuracy in this study. This method allows for the rapid detection of gas sources, as well as the execution of an emergency response, in line with Sustainable Development Goal Target 3.9. |
| format | Article |
| id | doaj-art-f44eca62f12d411f8c73c7f5bac67d36 |
| institution | Kabale University |
| issn | 2772-5081 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Digital Chemical Engineering |
| spelling | doaj-art-f44eca62f12d411f8c73c7f5bac67d362025-01-08T04:53:50ZengElsevierDigital Chemical Engineering2772-50812025-03-0114100216A novel CFD-MILP-ANN approach for optimizing sensor placement, number, and source localization in large-scale gas dispersion from unknown locationsYiming Lang0Michelle Xin Yi Ng1Kai Xiang Yu2Binghui Chen3Peng Chee Tan4Khang Wei Tan5Weng Hoong Lam6Parthiban Siwayanan7Kek Seong Kim8Thomas Shean Yaw Choong9Joon Yoon Ten10Zhen Hong Ban11School of Energy and Chemical Engineering, Xiamen University Malaysia, Sepang, Selangor 43900, MalaysiaSchool of Energy and Chemical Engineering, Xiamen University Malaysia, Sepang, Selangor 43900, MalaysiaSchool of Energy and Chemical Engineering, Xiamen University Malaysia, Sepang, Selangor 43900, MalaysiaSchool of Energy and Chemical Engineering, Xiamen University Malaysia, Sepang, Selangor 43900, Malaysia; College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, ChinaSchool of Energy and Chemical Engineering, Xiamen University Malaysia, Sepang, Selangor 43900, Malaysia; College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, ChinaSchool of Energy and Chemical Engineering, Xiamen University Malaysia, Sepang, Selangor 43900, Malaysia; College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, ChinaSchool of Energy and Chemical Engineering, Xiamen University Malaysia, Sepang, Selangor 43900, Malaysia; College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, ChinaSchool of Energy and Chemical Engineering, Xiamen University Malaysia, Sepang, Selangor 43900, Malaysia; College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, ChinaSchool of Energy and Chemical Engineering, Xiamen University Malaysia, Sepang, Selangor 43900, Malaysia; College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, ChinaFaculty of Engineering, Universiti Putra Malaysia, Seri Kembangan, Selangor, 43400, MalaysiaSchool of Energy and Chemical Engineering, Xiamen University Malaysia, Sepang, Selangor 43900, Malaysia; College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China; Corresponding authors.School of Energy and Chemical Engineering, Xiamen University Malaysia, Sepang, Selangor 43900, Malaysia; College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China; Corresponding authors.Illegal practices like open electronic waste incineration release hazardous pollutants, endangering the environment and human health. The Internet of Things (IoT) enables online real-time gas concentrations, but its capability to predict leak sources accurately remains a challenge. A large amount of historical data is required to train the source localization model, as gas dispersion is affected by wind speed and wind direction. Furthermore, sensor placement critically affects precise detection and prediction. This study introduces an innovative approach integrating Computational Fluid Dynamics (CFD), Mixed-Integer Linear Programming (MILP), and Artificial Neural Network modeling (ANN). CFD was utilized for machine learning model training. The MILP was used to optimize sensor placement, while the ANN model was used to optimize sensor number. The source localization model was again realized by the ANN model with optimized sensors data. The trained model was able to identify the unknown illegal electronic waste treatment locations with 97.22 % accuracy in this study. This method allows for the rapid detection of gas sources, as well as the execution of an emergency response, in line with Sustainable Development Goal Target 3.9.http://www.sciencedirect.com/science/article/pii/S2772508124000784Source localizationMachine learningCFD simulationOptimizationGas dispersion |
| spellingShingle | Yiming Lang Michelle Xin Yi Ng Kai Xiang Yu Binghui Chen Peng Chee Tan Khang Wei Tan Weng Hoong Lam Parthiban Siwayanan Kek Seong Kim Thomas Shean Yaw Choong Joon Yoon Ten Zhen Hong Ban A novel CFD-MILP-ANN approach for optimizing sensor placement, number, and source localization in large-scale gas dispersion from unknown locations Digital Chemical Engineering Source localization Machine learning CFD simulation Optimization Gas dispersion |
| title | A novel CFD-MILP-ANN approach for optimizing sensor placement, number, and source localization in large-scale gas dispersion from unknown locations |
| title_full | A novel CFD-MILP-ANN approach for optimizing sensor placement, number, and source localization in large-scale gas dispersion from unknown locations |
| title_fullStr | A novel CFD-MILP-ANN approach for optimizing sensor placement, number, and source localization in large-scale gas dispersion from unknown locations |
| title_full_unstemmed | A novel CFD-MILP-ANN approach for optimizing sensor placement, number, and source localization in large-scale gas dispersion from unknown locations |
| title_short | A novel CFD-MILP-ANN approach for optimizing sensor placement, number, and source localization in large-scale gas dispersion from unknown locations |
| title_sort | novel cfd milp ann approach for optimizing sensor placement number and source localization in large scale gas dispersion from unknown locations |
| topic | Source localization Machine learning CFD simulation Optimization Gas dispersion |
| url | http://www.sciencedirect.com/science/article/pii/S2772508124000784 |
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