An integrated predictive model with an on-line updating strategy for iron precipitation in zinc hydrometallurgy
Iron precipitation by goethite plays an important role in zinc hydrometallurgy. The ferrous ion concentration, which is a key index for assessing the iron removal rate and process control results, cannot be measured on-line. In this study, an integrated predictive model of the ferrous ion concentrat...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Published: |
Elsevier
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/12909 |
| _version_ | 1848748207142600704 |
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| author | Xie, Y. Xie, S. Chen, X. Gui, W. Yang, C. Caccetta, Louis |
| author_facet | Xie, Y. Xie, S. Chen, X. Gui, W. Yang, C. Caccetta, Louis |
| author_sort | Xie, Y. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Iron precipitation by goethite plays an important role in zinc hydrometallurgy. The ferrous ion concentration, which is a key index for assessing the iron removal rate and process control results, cannot be measured on-line. In this study, an integrated predictive model of the ferrous ion concentration is established by integrating the mechanism model and error compensation model, which is based on data identification. The mechanism model is proposed based on an analysis of the process reaction and considering the reaction unit as a continuous stirred tank reactor model. For unknown parameters in the mechanism model, a double-particle swarm optimization algorithm based on information exchange and dynamic adjustment of the feasible region is developed for optimal selection. To improve the adaptive capability of the integrated model, we propose a model-updating strategy and parameter calibration method based on a sensitivity analysis to accomplish on-line adaptive updating of the predictive model. The simulation results demonstrate that the proposed model can effectively track the variation tendency of the ferrous ion concentration and successfully improve the adaptability of the integrated model. |
| first_indexed | 2025-11-14T07:01:22Z |
| format | Journal Article |
| id | curtin-20.500.11937-12909 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:01:22Z |
| publishDate | 2015 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-129092017-09-13T16:06:24Z An integrated predictive model with an on-line updating strategy for iron precipitation in zinc hydrometallurgy Xie, Y. Xie, S. Chen, X. Gui, W. Yang, C. Caccetta, Louis Iron precipitation by goethite plays an important role in zinc hydrometallurgy. The ferrous ion concentration, which is a key index for assessing the iron removal rate and process control results, cannot be measured on-line. In this study, an integrated predictive model of the ferrous ion concentration is established by integrating the mechanism model and error compensation model, which is based on data identification. The mechanism model is proposed based on an analysis of the process reaction and considering the reaction unit as a continuous stirred tank reactor model. For unknown parameters in the mechanism model, a double-particle swarm optimization algorithm based on information exchange and dynamic adjustment of the feasible region is developed for optimal selection. To improve the adaptive capability of the integrated model, we propose a model-updating strategy and parameter calibration method based on a sensitivity analysis to accomplish on-line adaptive updating of the predictive model. The simulation results demonstrate that the proposed model can effectively track the variation tendency of the ferrous ion concentration and successfully improve the adaptability of the integrated model. 2015 Journal Article http://hdl.handle.net/20.500.11937/12909 10.1016/j.hydromet.2014.11.004 Elsevier restricted |
| spellingShingle | Xie, Y. Xie, S. Chen, X. Gui, W. Yang, C. Caccetta, Louis An integrated predictive model with an on-line updating strategy for iron precipitation in zinc hydrometallurgy |
| title | An integrated predictive model with an on-line updating strategy for iron precipitation in zinc hydrometallurgy |
| title_full | An integrated predictive model with an on-line updating strategy for iron precipitation in zinc hydrometallurgy |
| title_fullStr | An integrated predictive model with an on-line updating strategy for iron precipitation in zinc hydrometallurgy |
| title_full_unstemmed | An integrated predictive model with an on-line updating strategy for iron precipitation in zinc hydrometallurgy |
| title_short | An integrated predictive model with an on-line updating strategy for iron precipitation in zinc hydrometallurgy |
| title_sort | integrated predictive model with an on-line updating strategy for iron precipitation in zinc hydrometallurgy |
| url | http://hdl.handle.net/20.500.11937/12909 |