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Erschienen in:

2018 | OriginalPaper | Buchkapitel

Neural Prediction Model for Extraction of Germanium from Zinc Oxide Dust by Microwave Alkaline Roasting-Water Leaching

verfasst von : Wankun Wang, Fuchun Wang

Erschienen in: 9th International Symposium on High-Temperature Metallurgical Processing

Verlag: Springer International Publishing

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Abstract

Based on the study of artificial neural network, the neural model was established for the prediction of germanium extraction from zinc oxide dust by microwave alkaline roasting-water leaching. Alkali-material mass ratio, microwave heating temperature, liquid-solid ratio, aging time, leaching time and leaching temperature were the significant factors for the process. The results indicated that the neural network prediction model was reliable, and the forecast values fitted well with the actual experimental values. The model could be used to predict the regeneration experiments with high credibility and practical significance. The accuracy of convergence of the model reached 10⁻⁵.

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Titel: Neural Prediction Model for Extraction of Germanium from Zinc Oxide Dust by Microwave Alkaline Roasting-Water Leaching
verfasst von: Wankun Wang
Fuchun Wang
Verlag: Springer International Publishing
Buch: 9th International Symposium on High-Temperature Metallurgical Processing
Print ISBN: 978-3-319-72137-8

Electronic ISBN: 978-3-319-72138-5

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-72138-5_7

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