Grey-box Recursive Parameter Identification of a Nonlinear Dynamic Model for Mineral Flotation
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917917043458048 |
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| author | González, Rodrigo A. Quintanilla, Paulina |
| author_facet | González, Rodrigo A. Quintanilla, Paulina |
| contents | This study presents a grey-box recursive identification technique to estimate key parameters in a mineral flotation process across two scenarios. The method is applied to a nonlinear physics-based dynamic model validated at a laboratory scale, allowing real-time updates of two model parameters, n and C, in response to changing conditions. The proposed approach effectively adapts to process variability and allows for continuous adjustments based on operational fluctuations, resulting in a significantly improved estimation of concentrate grade - one key performance indicator. In Scenario 1, parameters n and C achieved fit metrics of 97.99 and 96.86, respectively, with concentrate grade estimations improving from 75.1 to 98.69 using recursive identification. In Scenario 2, the fit metrics for n and C were 96.27 and 95.48, respectively, with the concentrate grade estimations increasing from 96.27 to 99.45 with recursive identification. The results demonstrate the effectiveness of the proposed grey-box recursive identification method in accurately estimating parameters and predicting concentrate grade in a mineral flotation process. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04275 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Grey-box Recursive Parameter Identification of a Nonlinear Dynamic Model for Mineral Flotation González, Rodrigo A. Quintanilla, Paulina Systems and Control This study presents a grey-box recursive identification technique to estimate key parameters in a mineral flotation process across two scenarios. The method is applied to a nonlinear physics-based dynamic model validated at a laboratory scale, allowing real-time updates of two model parameters, n and C, in response to changing conditions. The proposed approach effectively adapts to process variability and allows for continuous adjustments based on operational fluctuations, resulting in a significantly improved estimation of concentrate grade - one key performance indicator. In Scenario 1, parameters n and C achieved fit metrics of 97.99 and 96.86, respectively, with concentrate grade estimations improving from 75.1 to 98.69 using recursive identification. In Scenario 2, the fit metrics for n and C were 96.27 and 95.48, respectively, with the concentrate grade estimations increasing from 96.27 to 99.45 with recursive identification. The results demonstrate the effectiveness of the proposed grey-box recursive identification method in accurately estimating parameters and predicting concentrate grade in a mineral flotation process. |
| title | Grey-box Recursive Parameter Identification of a Nonlinear Dynamic Model for Mineral Flotation |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2405.04275 |