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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Coban R (2013) A context layered locally recurrent neural network for dynamic system identification. Eng Appl Artif Intell 60(9):35–54
Gholami M, Cai N, Brennan RW (2013) An artificial neural network approach to the problem of wireless sensors network localization. Robot Comput Integr Manuf 29(1):96–109
Pol HH, Bullmore E (2013) Neural networks in psychiatry. Eur Neuropsychopharmacol 23(1):1–6
Lai B-Q, Wang J-M, Duan J-J et al (2013) The integration of NSC-derived and host neural networks after rat spinal cord transection. Biomaterials 34(12):2888–2901
Aniceto JPS, Fernandes DLA et al (2013) Modeling ion exchange equilibrium of ternary systems using neural networks. Desalination 309(15):267–274
Rajković KM, Avramović JM, Milić PS et al (2013) Optimization of ultrasound-assisted base-catalyzed methanolysis of sunflower oil using response surface and artificial neural network methodologies. Chem Eng J 215–216(15):82–89
Irani R, Nasimi R (2011) Application of artificial bee colony-based neural network in bottom hole pressure prediction in underbalanced drilling. J Pet Sci Eng 78(1):6–12
Gil P, Henriques J, Cardoso A, Dourado A (2013) On affine state-space neural networks for system identification, Global stability conditions and complexity management. Control Eng Pract 21(4):518–529
Opdenbosch P, Sadegh N, Book W (2013) Intelligent controls for electro-hydraulic poppet valves. Control Eng Pract 21(6):789–796
Han H-G, Qiao J-F, Chen Q-L (2012) Model predictive control of dissolved oxygen concentration based on a self-organizing RBF neural network. Control Eng Pract 20(4):465–476
Wang HS, Wang YN, Wang YC (2013) Cost estimation of plastic injection molding parts through integration of PSO and BP neural network. Expert Syst Appl 40(2):418–428
Maric I (2013) Optimization of self-organizing polynomial neural networks. Expert Syst Appl 31:4528–4538
Peteiro-Barral D, Bolón-Canedo V, Alonso-Betanzos A et al (2013) Toward the scalability of neural networks through feature selection. Expert Syst Appl 40(8):2807–2816
Lu D (2009) Nonlinear model predictive control based on neural network. Master’s thesis, Central South University of China
Acknowledgements
This work was supported by the National Natural Science Foundation of China (51404081, 51504073, 51664010), the Research Program of the Education Department of Guizhou Province (KY [2015]433), and the Research Program of Talented Scholars of Guizhou Institute of Technology (XJG20141104).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 The Minerals, Metals & Materials Society
About this paper
Cite this paper
Wang, W., Wang, F. (2018). Neural Prediction Model for Extraction of Germanium from Zinc Oxide Dust by Microwave Alkaline Roasting-Water Leaching. In: Hwang, JY., et al. 9th International Symposium on High-Temperature Metallurgical Processing. TMS 2018. The Minerals, Metals & Materials Series. Springer, Cham. https://doi.org/10.1007/978-3-319-72138-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-72138-5_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-72137-8
Online ISBN: 978-3-319-72138-5
eBook Packages: Chemistry and Materials ScienceChemistry and Material Science (R0)