文献类型：期刊文献

英文题名：Neural prediction model for extraction of germanium from zinc oxide dust by microwave alkaline roasting-water leaching

作者：Wang, Wankun Wang, Fuchun

第一作者：Wang, Wankun;王万坤

通信作者：Wang, Fuchun|[14440dd2a745fef007a6a]王福春;[1444088c999811cc55819]王福春;

机构：[1] Key Laboratory of Light Metal Materials Processing Technology of Guizhou Provinces, Guiyang, 550003, China; [2] School of Materials and Metallurgical Engineering, Guizhou Institute of Technology, Guiyang, 550003, China

第一机构：Key Laboratory of Light Metal Materials Processing Technology of Guizhou Provinces, Guiyang, 550003, China

通信机构：|贵州理工学院材料与冶金工程学院;贵州理工学院材料与能源工程学院

年份：2018

起止页码：61-67

外文期刊名：Minerals, Metals and Materials Series

收录：EI(收录号：20180904830862)

语种：英文

外文关键词：Dust - Neural networks - Zinc oxide - Leaching - Microwave heating - Calcination - II-VI semiconductors - Extraction - Forecasting

摘要：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. ? The Minerals, Metals & Materials Society 2018.