Optimized Flare Performance Analysis Through Multi-Modal Machine Learning and Temporal Standard Deviation Enhancements
Flaring is a routine practice in the upstream gas industry to dispose of waste gases, but its efficiency can drop significantly under non-ideal conditions such as crosswinds, over-aeration, or over-steaming. These inefficiencies lead to incomplete combustion, producing harmful substances like carbon...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10879335/ |
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| author | Said Boumaraf Pengfei Li Muaz Al Radi Fares Oussama Abdelhafez Abderaouf Behouch Khalid Yousef Al Awadhi Hamad Karki Sajid Javed Jorge Dias Naoufel Werghi |
| author_facet | Said Boumaraf Pengfei Li Muaz Al Radi Fares Oussama Abdelhafez Abderaouf Behouch Khalid Yousef Al Awadhi Hamad Karki Sajid Javed Jorge Dias Naoufel Werghi |
| author_sort | Said Boumaraf |
| collection | DOAJ |
| description | Flaring is a routine practice in the upstream gas industry to dispose of waste gases, but its efficiency can drop significantly under non-ideal conditions such as crosswinds, over-aeration, or over-steaming. These inefficiencies lead to incomplete combustion, producing harmful substances like carbon monoxide and unburned methane, which contribute significantly to global warming. Current solutions for monitoring flare efficiency are often complex or expensive, limiting their widespread adoption. This work introduces a novel framework for estimating flare combustion efficiency (CE) using a multi-modal machine learning architecture enhanced by a Temporal Standard Deviation (TSD) preprocessing technique. Our approach combines synchronized visual data with minimal field measurements (FM) for accurate efficiency estimation. We first extract sequential frames from an RGB video stream of flares and process them to extract TSD images, which essentially highlight the variability and dynamic changes in the combustion process. Next, we extract image feature representations from TSD images using the state-of-the-art Vision Transformer (ViT) and fuse them with experimentally selected FM data to create a comprehensive combustion dataset. A CatBoost regression model is then trained on this dataset to estimate the final CE. Our proposed framework is validated using real-world data from industrial flare upstream operations. The results demonstrate significant improvements in estimation accuracy and reliability compared to traditional methods, achieving an R-squared score of 94.77% with minimal FM data. This approach not only enhances the understanding of combustion dynamics but also offers a scalable, cost-effective solution for continuous flare monitoring and optimization. |
| format | Article |
| id | doaj-art-5d3adaea68fa4911b8a53e39bd5db80f |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5d3adaea68fa4911b8a53e39bd5db80f2025-08-20T02:57:59ZengIEEEIEEE Access2169-35362025-01-0113343623437710.1109/ACCESS.2025.354055810879335Optimized Flare Performance Analysis Through Multi-Modal Machine Learning and Temporal Standard Deviation EnhancementsSaid Boumaraf0https://orcid.org/0000-0001-8154-7195Pengfei Li1https://orcid.org/0009-0009-9458-8557Muaz Al Radi2Fares Oussama Abdelhafez3https://orcid.org/0009-0002-3420-7053Abderaouf Behouch4Khalid Yousef Al Awadhi5Hamad Karki6Sajid Javed7https://orcid.org/0000-0002-0036-2875Jorge Dias8https://orcid.org/0000-0002-2725-8867Naoufel Werghi9https://orcid.org/0000-0002-5542-448XC2PS, Department of Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab EmiratesC2PS, Department of Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab EmiratesC2PS, Department of Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab EmiratesDepartment of Mechanical and Nuclear Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab EmiratesDepartment of Physics, Khalifa University of Science and Technology, Abu Dhabi, United Arab EmiratesADNOC Technology, Abu Dhabi, United Arab EmiratesDepartment of Mechanical and Nuclear Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab EmiratesDepartment of Computer Science, KUCARS and C2PS, Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Computer Science, KUCARS and C2PS, Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Computer Science, KUCARS and C2PS, Khalifa University, Abu Dhabi, United Arab EmiratesFlaring is a routine practice in the upstream gas industry to dispose of waste gases, but its efficiency can drop significantly under non-ideal conditions such as crosswinds, over-aeration, or over-steaming. These inefficiencies lead to incomplete combustion, producing harmful substances like carbon monoxide and unburned methane, which contribute significantly to global warming. Current solutions for monitoring flare efficiency are often complex or expensive, limiting their widespread adoption. This work introduces a novel framework for estimating flare combustion efficiency (CE) using a multi-modal machine learning architecture enhanced by a Temporal Standard Deviation (TSD) preprocessing technique. Our approach combines synchronized visual data with minimal field measurements (FM) for accurate efficiency estimation. We first extract sequential frames from an RGB video stream of flares and process them to extract TSD images, which essentially highlight the variability and dynamic changes in the combustion process. Next, we extract image feature representations from TSD images using the state-of-the-art Vision Transformer (ViT) and fuse them with experimentally selected FM data to create a comprehensive combustion dataset. A CatBoost regression model is then trained on this dataset to estimate the final CE. Our proposed framework is validated using real-world data from industrial flare upstream operations. The results demonstrate significant improvements in estimation accuracy and reliability compared to traditional methods, achieving an R-squared score of 94.77% with minimal FM data. This approach not only enhances the understanding of combustion dynamics but also offers a scalable, cost-effective solution for continuous flare monitoring and optimization.https://ieeexplore.ieee.org/document/10879335/Flare stackscombustion efficiencytemporal standard deviationdeep learningCatBoost regression |
| spellingShingle | Said Boumaraf Pengfei Li Muaz Al Radi Fares Oussama Abdelhafez Abderaouf Behouch Khalid Yousef Al Awadhi Hamad Karki Sajid Javed Jorge Dias Naoufel Werghi Optimized Flare Performance Analysis Through Multi-Modal Machine Learning and Temporal Standard Deviation Enhancements IEEE Access Flare stacks combustion efficiency temporal standard deviation deep learning CatBoost regression |
| title | Optimized Flare Performance Analysis Through Multi-Modal Machine Learning and Temporal Standard Deviation Enhancements |
| title_full | Optimized Flare Performance Analysis Through Multi-Modal Machine Learning and Temporal Standard Deviation Enhancements |
| title_fullStr | Optimized Flare Performance Analysis Through Multi-Modal Machine Learning and Temporal Standard Deviation Enhancements |
| title_full_unstemmed | Optimized Flare Performance Analysis Through Multi-Modal Machine Learning and Temporal Standard Deviation Enhancements |
| title_short | Optimized Flare Performance Analysis Through Multi-Modal Machine Learning and Temporal Standard Deviation Enhancements |
| title_sort | optimized flare performance analysis through multi modal machine learning and temporal standard deviation enhancements |
| topic | Flare stacks combustion efficiency temporal standard deviation deep learning CatBoost regression |
| url | https://ieeexplore.ieee.org/document/10879335/ |
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