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Title: | Grey-box Recursive Parameter Identification of a Nonlinear Dynamic Model for Mineral Flotation |
Authors: | Gonzalez, RA Quintanilla, P |
Keywords: | froth flotation;grey-box identification;mineral processing;online parameter estimation. |
Issue Date: | 7-May-2024 |
Publisher: | Cornell University |
Citation: | Gonzalez, R.A. and Quintanilla, P. (2024) 'Grey-box Recursive Parameter Identification of a Nonlinear Dynamic Model for Mineral Flotation', arXiv:2405.04275v1 [eess.SY], pp. 1 - 6. doi: 10.48550/arXiv.2405.04275. |
Abstract: | 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. |
URI: | https://bura.brunel.ac.uk/handle/2438/29214 |
DOI: | https://doi.org/10.48550/arXiv.2405.04275 |
Other Identifiers: | ORCiD: Paulina Quintanilla https://orcid.org/0000-0002-7717-0556 arXiv:2405.04275v1 [eess.SY] |
Appears in Collections: | Dept of Chemical Engineering Research Papers |
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Preprint.pdf | Copyright © 2024 The Author(s). This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/). | 1.74 MB | Adobe PDF | View/Open |
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