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...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier Ltd
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/118650/ http://psasir.upm.edu.my/id/eprint/118650/1/118650.pdf |
| _version_ | 1848867563739545600 |
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| author | Lang, Yiming Ng, Michelle Xin Yi Yu, Kai Xiang Chen, Binghui Tan, Peng Chee Tan, Khang Wei Lam, Weng Hoong Siwayanan, Parthiban Kim, Kek Seong Choong, Thomas Shean Yaw Ten, Joon Yoon Ban, Zhen Hong |
| author_facet | Lang, Yiming Ng, Michelle Xin Yi Yu, Kai Xiang Chen, Binghui Tan, Peng Chee Tan, Khang Wei Lam, Weng Hoong Siwayanan, Parthiban Kim, Kek Seong Choong, Thomas Shean Yaw Ten, Joon Yoon Ban, Zhen Hong |
| author_sort | Lang, Yiming |
| building | UPM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T14:38:29Z |
| format | Article |
| id | upm-118650 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:38:29Z |
| publishDate | 2025 |
| publisher | Elsevier Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1186502025-07-21T06:39:12Z http://psasir.upm.edu.my/id/eprint/118650/ A novel CFD-MILP-ANN approach for optimizing sensor placement, number, and source localization in large-scale gas dispersion from unknown locations Lang, Yiming Ng, Michelle Xin Yi Yu, Kai Xiang Chen, Binghui Tan, Peng Chee Tan, Khang Wei Lam, Weng Hoong Siwayanan, Parthiban Kim, Kek Seong Choong, Thomas Shean Yaw Ten, Joon Yoon Ban, Zhen Hong 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. Elsevier Ltd 2025-03 Article PeerReviewed text en cc_by_nc_nd_4 http://psasir.upm.edu.my/id/eprint/118650/1/118650.pdf Lang, Yiming and Ng, Michelle Xin Yi and Yu, Kai Xiang and Chen, Binghui and Tan, Peng Chee and Tan, Khang Wei and Lam, Weng Hoong and Siwayanan, Parthiban and Kim, Kek Seong and Choong, Thomas Shean Yaw and Ten, Joon Yoon and Ban, Zhen Hong (2025) 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, 14. art. no. 100216. pp. 1-22. ISSN 2772-5081; eISSN: 2772-5081 https://linkinghub.elsevier.com/retrieve/pii/S2772508124000784 10.1016/j.dche.2024.100216 |
| spellingShingle | Lang, Yiming Ng, Michelle Xin Yi Yu, Kai Xiang Chen, Binghui Tan, Peng Chee Tan, Khang Wei Lam, Weng Hoong Siwayanan, Parthiban Kim, Kek Seong Choong, Thomas Shean Yaw Ten, Joon Yoon Ban, Zhen Hong A novel CFD-MILP-ANN approach for optimizing sensor placement, number, and source localization in large-scale gas dispersion from unknown locations |
| 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 |
| url | http://psasir.upm.edu.my/id/eprint/118650/ http://psasir.upm.edu.my/id/eprint/118650/ http://psasir.upm.edu.my/id/eprint/118650/ http://psasir.upm.edu.my/id/eprint/118650/1/118650.pdf |