Neural prediction model for microwave calcination-sulphuric acid leaching of germanium from zinc oxide dust
Wankun Wang, Fuchun Wang, Fanghai Lu
Available Online February 2018.
- 10.2991/ifeesm-17.2018.233How to use a DOI?
- zinc oxide dust; germanium; microwave calcinations; artificial neural network.
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.
- © 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/).
Cite this article
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 -