Moving Horizon Estimation of Sulfur Concentrate Grade Based on Kinetic Models Under Multiple Working Conditions
This article proposed a moving horizon approach to predict sulfur concentrate grade in a sulfur flotation process. In this approach, the grade prediction problem is formulated as a moving horizon estimation problem. To build the nominal model for the estimation, the kinetic of sulfur flotation proce...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9183909/ |
| _version_ | 1818430569143336960 |
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| author | Mingfang He Bei Sun Guoxiong Zhou Junjie Zhu |
| author_facet | Mingfang He Bei Sun Guoxiong Zhou Junjie Zhu |
| author_sort | Mingfang He |
| collection | DOAJ |
| description | This article proposed a moving horizon approach to predict sulfur concentrate grade in a sulfur flotation process. In this approach, the grade prediction problem is formulated as a moving horizon estimation problem. To build the nominal model for the estimation, the kinetic of sulfur flotation process are firstly studied. The unknown variable in the kinetic model, i.e., the flux of overflow, is obtained using machine vision technology. Moreover, to account for the multi-modal characteristic of sulfur flotation process, the kinetic model parameters are identified for different working conditions. To eliminate the effect of, a robust parameter identification approach is adopted. Finally, the kinetic model is embedded in the moving horizon estimation framework, which reconstructs the concentrate grade using from online measured process variables. Experimental results demonstrate the feasibility and efficient of the proposed method. |
| first_indexed | 2024-12-14T15:35:29Z |
| format | Article |
| id | doaj.art-8df638a5377845008d1d0747198ae300 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T15:35:29Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8df638a5377845008d1d0747198ae3002022-12-21T22:55:43ZengIEEEIEEE Access2169-35362020-01-01815971815973110.1109/ACCESS.2020.30209169183909Moving Horizon Estimation of Sulfur Concentrate Grade Based on Kinetic Models Under Multiple Working ConditionsMingfang He0https://orcid.org/0000-0001-6969-626XBei Sun1https://orcid.org/0000-0003-3503-801XGuoxiong Zhou2https://orcid.org/0000-0002-8295-3862Junjie Zhu3https://orcid.org/0000-0002-4745-7396School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, ChinaSchool of Automation, Central South University, Changsha, ChinaSchool of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, ChinaSchool of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, ChinaThis article proposed a moving horizon approach to predict sulfur concentrate grade in a sulfur flotation process. In this approach, the grade prediction problem is formulated as a moving horizon estimation problem. To build the nominal model for the estimation, the kinetic of sulfur flotation process are firstly studied. The unknown variable in the kinetic model, i.e., the flux of overflow, is obtained using machine vision technology. Moreover, to account for the multi-modal characteristic of sulfur flotation process, the kinetic model parameters are identified for different working conditions. To eliminate the effect of, a robust parameter identification approach is adopted. Finally, the kinetic model is embedded in the moving horizon estimation framework, which reconstructs the concentrate grade using from online measured process variables. Experimental results demonstrate the feasibility and efficient of the proposed method.https://ieeexplore.ieee.org/document/9183909/Moving horizon estimationkinetic modelingparameter identificationmineral processing |
| spellingShingle | Mingfang He Bei Sun Guoxiong Zhou Junjie Zhu Moving Horizon Estimation of Sulfur Concentrate Grade Based on Kinetic Models Under Multiple Working Conditions IEEE Access Moving horizon estimation kinetic modeling parameter identification mineral processing |
| title | Moving Horizon Estimation of Sulfur Concentrate Grade Based on Kinetic Models Under Multiple Working Conditions |
| title_full | Moving Horizon Estimation of Sulfur Concentrate Grade Based on Kinetic Models Under Multiple Working Conditions |
| title_fullStr | Moving Horizon Estimation of Sulfur Concentrate Grade Based on Kinetic Models Under Multiple Working Conditions |
| title_full_unstemmed | Moving Horizon Estimation of Sulfur Concentrate Grade Based on Kinetic Models Under Multiple Working Conditions |
| title_short | Moving Horizon Estimation of Sulfur Concentrate Grade Based on Kinetic Models Under Multiple Working Conditions |
| title_sort | moving horizon estimation of sulfur concentrate grade based on kinetic models under multiple working conditions |
| topic | Moving horizon estimation kinetic modeling parameter identification mineral processing |
| url | https://ieeexplore.ieee.org/document/9183909/ |
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