Biologically Fe super(2+) oxidizing fluidized bed reactor performance and controlling of Fe super(3+) recycle during heap bioleaching: an artificial neural network-based model
The performance of a biological Fe super(2+) oxidizing fluidized bed reactor (FBR) was modeled by a popular neural network-back-propagation algorithm over a period of 220days at 37C under different operational conditions. A method is proposed for modeling Fe super(3+) production in FBR and thereby m...
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Published in | Bioprocess and biosystems engineering Vol. 31; no. 2; pp. 111 - 117 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
01.02.2008
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Online Access | Get full text |
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Summary: | The performance of a biological Fe super(2+) oxidizing fluidized bed reactor (FBR) was modeled by a popular neural network-back-propagation algorithm over a period of 220days at 37C under different operational conditions. A method is proposed for modeling Fe super(3+) production in FBR and thereby managing the regeneration of Fe super(3+) for heap leaching application, based on an artificial neural network-back-propagation algorithm. Depending on output value, relevant control strategies and actions are activated, and Fe super(3+) production in FBR was considered as a critical output parameter. The modeling of effluent Fe super(3+) concentration was very successful, and an excellent match was obtained between the measured and the predicted concentrations. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 content type line 23 ObjectType-Feature-2 |
ISSN: | 1615-7591 1615-7605 |
DOI: | 10.1007/s00449-007-0153-9 |