Pipe Resistance Loss Calculation in Industry 4.0: An Innovative Framework Based on TransKAN and Generative AI
As the demand for deep mineral resource extraction intensifies, optimizing pipeline transportation systems in backfill mining has become increasingly critical. Thus, reducing energy loss while ensuring the filling effect becomes crucial for improving process efficiency. Owing to variations among min...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/12/3803 |
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| author | Qinyu Zhang Huiying Liu Zhike Liu Yongkang Liu Yuhan Gong Chonghao Wang |
| author_facet | Qinyu Zhang Huiying Liu Zhike Liu Yongkang Liu Yuhan Gong Chonghao Wang |
| author_sort | Qinyu Zhang |
| collection | DOAJ |
| description | As the demand for deep mineral resource extraction intensifies, optimizing pipeline transportation systems in backfill mining has become increasingly critical. Thus, reducing energy loss while ensuring the filling effect becomes crucial for improving process efficiency. Owing to variations among mines, accurately calculating pipeline resistance loss remains challenging, resulting in significant inaccuracies. The rapid development of Industry 4.0 provides intelligent and data-driven optimization ideas for this challenge. This study introduces a novel pipeline resistance loss prediction framework integrating generative artificial intelligence with a TransKAN model. This study employs generative artificial intelligence to produce physically constrained augmented data, integrates the KAN network’s B-spline basis functions for nonlinear feature extraction, and incorporates the Transformer architecture to capture spatio-temporal correlations in pipeline pressure sequences, enabling precise resistance loss calculation. The experimental data collected from pipeline pressure sensors provides empirical validation for the model. Compared with traditional mathematical formulas, BP neural networks, SVMs, and random forests, the proposed model demonstrates superior performance, achieving an R<sup>2</sup> value of 0.9644, an RMSE of 0.7126, and an MAE of 0.4703. |
| format | Article |
| id | doaj-art-88d98e6f6f8d49848ebeb7c16f67d024 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-88d98e6f6f8d49848ebeb7c16f67d0242025-08-20T03:26:51ZengMDPI AGSensors1424-82202025-06-012512380310.3390/s25123803Pipe Resistance Loss Calculation in Industry 4.0: An Innovative Framework Based on TransKAN and Generative AIQinyu Zhang0Huiying Liu1Zhike Liu2Yongkang Liu3Yuhan Gong4Chonghao Wang5College of Science, North China University of Science and Technology, Tangshan 063210, ChinaCollege of Science, North China University of Science and Technology, Tangshan 063210, ChinaCollege of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, ChinaCollege of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, ChinaCollege of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, ChinaCollege of Mining Engineering, North China University of Science and Technology, Tangshan 063210, ChinaAs the demand for deep mineral resource extraction intensifies, optimizing pipeline transportation systems in backfill mining has become increasingly critical. Thus, reducing energy loss while ensuring the filling effect becomes crucial for improving process efficiency. Owing to variations among mines, accurately calculating pipeline resistance loss remains challenging, resulting in significant inaccuracies. The rapid development of Industry 4.0 provides intelligent and data-driven optimization ideas for this challenge. This study introduces a novel pipeline resistance loss prediction framework integrating generative artificial intelligence with a TransKAN model. This study employs generative artificial intelligence to produce physically constrained augmented data, integrates the KAN network’s B-spline basis functions for nonlinear feature extraction, and incorporates the Transformer architecture to capture spatio-temporal correlations in pipeline pressure sequences, enabling precise resistance loss calculation. The experimental data collected from pipeline pressure sensors provides empirical validation for the model. Compared with traditional mathematical formulas, BP neural networks, SVMs, and random forests, the proposed model demonstrates superior performance, achieving an R<sup>2</sup> value of 0.9644, an RMSE of 0.7126, and an MAE of 0.4703.https://www.mdpi.com/1424-8220/25/12/3803pipeline resistance lossattention fusiongenerative artificial intelligenceKAN network |
| spellingShingle | Qinyu Zhang Huiying Liu Zhike Liu Yongkang Liu Yuhan Gong Chonghao Wang Pipe Resistance Loss Calculation in Industry 4.0: An Innovative Framework Based on TransKAN and Generative AI Sensors pipeline resistance loss attention fusion generative artificial intelligence KAN network |
| title | Pipe Resistance Loss Calculation in Industry 4.0: An Innovative Framework Based on TransKAN and Generative AI |
| title_full | Pipe Resistance Loss Calculation in Industry 4.0: An Innovative Framework Based on TransKAN and Generative AI |
| title_fullStr | Pipe Resistance Loss Calculation in Industry 4.0: An Innovative Framework Based on TransKAN and Generative AI |
| title_full_unstemmed | Pipe Resistance Loss Calculation in Industry 4.0: An Innovative Framework Based on TransKAN and Generative AI |
| title_short | Pipe Resistance Loss Calculation in Industry 4.0: An Innovative Framework Based on TransKAN and Generative AI |
| title_sort | pipe resistance loss calculation in industry 4 0 an innovative framework based on transkan and generative ai |
| topic | pipeline resistance loss attention fusion generative artificial intelligence KAN network |
| url | https://www.mdpi.com/1424-8220/25/12/3803 |
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