Future Definition and Extraction of the Blast Furnace 3D Burden Surface Based on Intelligent Algorithms
The accurate identification of the shape of the blast furnace (BF) burden surface is a crucial factor in the fault diagnosis of the BF condition and guides the charge operation. Based on the BF 3D point cloud data of phased array radar, this paper proposes a 3D burden surface feature definition syst...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/24/12860 |
| _version_ | 1797461550365147136 |
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| author | Shaolun Sun Zejun Yu Sen Zhang Wendong Xiao |
| author_facet | Shaolun Sun Zejun Yu Sen Zhang Wendong Xiao |
| author_sort | Shaolun Sun |
| collection | DOAJ |
| description | The accurate identification of the shape of the blast furnace (BF) burden surface is a crucial factor in the fault diagnosis of the BF condition and guides the charge operation. Based on the BF 3D point cloud data of phased array radar, this paper proposes a 3D burden surface feature definition system. Based on expert experience, the feature parameters of the burden surface are extracted. The voxel feature was extracted based on improved BNVGG. The optimized PointCNN extracts the point cloud features. The features of the burden surface were defined from three perspectives: the surface shape, voxel, and point cloud. The research of the 2D burden line is extended to the 3D burden surface, and the assumption of the symmetry of the BF is eliminated. Finally, the accuracy of the burden surface classification under each feature was evaluated, and the effectiveness of each feature extraction algorithm was verified. The experimental results show that the shape feature defined based on expert experience affects the recognition of the burden surface. However, it is defined from the data perspective and cannot accurately identify a similar burden surface shape. Therefore, the voxel features and point cloud features of the burden surface were extracted, improving the identification accuracy. |
| first_indexed | 2024-03-09T17:21:58Z |
| format | Article |
| id | doaj.art-4debe371d482496ea007ce976e9ee4ae |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:21:58Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4debe371d482496ea007ce976e9ee4ae2023-11-24T13:05:58ZengMDPI AGApplied Sciences2076-34172022-12-0112241286010.3390/app122412860Future Definition and Extraction of the Blast Furnace 3D Burden Surface Based on Intelligent AlgorithmsShaolun Sun0Zejun Yu1Sen Zhang2Wendong Xiao3Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaKey Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaKey Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaKey Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaThe accurate identification of the shape of the blast furnace (BF) burden surface is a crucial factor in the fault diagnosis of the BF condition and guides the charge operation. Based on the BF 3D point cloud data of phased array radar, this paper proposes a 3D burden surface feature definition system. Based on expert experience, the feature parameters of the burden surface are extracted. The voxel feature was extracted based on improved BNVGG. The optimized PointCNN extracts the point cloud features. The features of the burden surface were defined from three perspectives: the surface shape, voxel, and point cloud. The research of the 2D burden line is extended to the 3D burden surface, and the assumption of the symmetry of the BF is eliminated. Finally, the accuracy of the burden surface classification under each feature was evaluated, and the effectiveness of each feature extraction algorithm was verified. The experimental results show that the shape feature defined based on expert experience affects the recognition of the burden surface. However, it is defined from the data perspective and cannot accurately identify a similar burden surface shape. Therefore, the voxel features and point cloud features of the burden surface were extracted, improving the identification accuracy.https://www.mdpi.com/2076-3417/12/24/128603D burden surfaceBNVGGPointNetPointCNN |
| spellingShingle | Shaolun Sun Zejun Yu Sen Zhang Wendong Xiao Future Definition and Extraction of the Blast Furnace 3D Burden Surface Based on Intelligent Algorithms Applied Sciences 3D burden surface BNVGG PointNet PointCNN |
| title | Future Definition and Extraction of the Blast Furnace 3D Burden Surface Based on Intelligent Algorithms |
| title_full | Future Definition and Extraction of the Blast Furnace 3D Burden Surface Based on Intelligent Algorithms |
| title_fullStr | Future Definition and Extraction of the Blast Furnace 3D Burden Surface Based on Intelligent Algorithms |
| title_full_unstemmed | Future Definition and Extraction of the Blast Furnace 3D Burden Surface Based on Intelligent Algorithms |
| title_short | Future Definition and Extraction of the Blast Furnace 3D Burden Surface Based on Intelligent Algorithms |
| title_sort | future definition and extraction of the blast furnace 3d burden surface based on intelligent algorithms |
| topic | 3D burden surface BNVGG PointNet PointCNN |
| url | https://www.mdpi.com/2076-3417/12/24/12860 |
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