Digital twin with automatic disturbance detection for real-time optimization of a semi-autogenous grinding (SAG) mill
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910521159057408 |
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| author | Quintanilla, Paulina Fernández, Francisco Mancilla, Cristobal Rojas, Matías Estrada, Mauricio Navia, Daniel |
| author_facet | Quintanilla, Paulina Fernández, Francisco Mancilla, Cristobal Rojas, Matías Estrada, Mauricio Navia, Daniel |
| contents | This work describes the development and validation of a digital twin for a semi-autogenous grinding (SAG) mill controlled by an expert system. The digital twin consists of three modules emulating a closed-loop system: fuzzy logic for the expert control, a state-space model for regulatory control, and a recurrent neural network for the SAG mill process. The model was trained with 68 hours of data and validated with 8 hours of test data. It predicts the mill's behavior within a 2.5-minute horizon with a 30-second sampling time. The disturbance detection evaluates the need for retraining, and the digital twin shows promise for supervising the SAG mill with the expert control system. Future work will focus on integrating this digital twin into real-time optimization strategies with industrial validation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_06216 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Digital twin with automatic disturbance detection for real-time optimization of a semi-autogenous grinding (SAG) mill Quintanilla, Paulina Fernández, Francisco Mancilla, Cristobal Rojas, Matías Estrada, Mauricio Navia, Daniel Systems and Control Machine Learning Applications This work describes the development and validation of a digital twin for a semi-autogenous grinding (SAG) mill controlled by an expert system. The digital twin consists of three modules emulating a closed-loop system: fuzzy logic for the expert control, a state-space model for regulatory control, and a recurrent neural network for the SAG mill process. The model was trained with 68 hours of data and validated with 8 hours of test data. It predicts the mill's behavior within a 2.5-minute horizon with a 30-second sampling time. The disturbance detection evaluates the need for retraining, and the digital twin shows promise for supervising the SAG mill with the expert control system. Future work will focus on integrating this digital twin into real-time optimization strategies with industrial validation. |
| title | Digital twin with automatic disturbance detection for real-time optimization of a semi-autogenous grinding (SAG) mill |
| topic | Systems and Control Machine Learning Applications |
| url | https://arxiv.org/abs/2407.06216 |