文献类型：会议论文

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

通信作者：Wang, FC[1]|[144403049e06275085f50]王福春;

机构：[1]Key Lab Light Met Mat Proc Technol Guizhou Prov, Guiyang 550003, Guizhou, Peoples R China;[2]Guizhou Inst Technol, Sch Mat & Met Engn, Guiyang 550003, Guizhou, Peoples R China

第一机构：Key Lab Light Met Mat Proc Technol Guizhou Prov, Guiyang 550003, Guizhou, Peoples R China

通信机构：corresponding author), Key Lab Light Met Mat Proc Technol Guizhou Prov, Guiyang 550003, Guizhou, Peoples R China.|贵州理工学院材料与冶金工程学院

会议论文集：9th International Symposium on High-Temperature Metallurgical Processing

会议日期：2018

会议地点：Phoenix, AZ

语种：英文

外文关键词：Zinc oxide dust; Germanium; Microwave alkaline roasting; Water leaching; Artificial neural network

年份：2018

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).