Neural prediction model for microwave calcination-sulphuric acid leaching of germanium from zinc oxide dust

DOI

10.2991/ifeesm-17.2018.233How to use a DOI?

Keywords

zinc oxide dust; germanium; microwave calcinations; artificial neural network.

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 calcination-sulphuric acid leaching. Microwave heating temperature, liquid-solid ratio, leaching time, initial concentration of sulphuric acid and leaching temperature were the significant factors for the process. The results indicated that the neural network prediction model was reliable, the forecast and actual values fitted well. The model could be used to predict the regeneration experiments with high credibility and practical significance. The accuracy of convergence of the model has reached 10-5.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Wankun Wang
AU  - Fuchun Wang
AU  - Fanghai Lu
PY  - 2018/02
DA  - 2018/02
TI  - Neural prediction model for microwave calcination-sulphuric acid leaching of germanium from zinc oxide dust
BT  - Proceedings of the 2017 3rd International Forum on Energy, Environment Science and Materials (IFEESM 2017)
PB  - Atlantis Press
SP  - 1271
EP  - 1275
SN  - 2352-5401
UR  - https://doi.org/10.2991/ifeesm-17.2018.233
DO  - 10.2991/ifeesm-17.2018.233
ID  - Wang2018/02
ER  -