Soft sensor of flotation froth grade classification based on hybrid deep neural network

Dingsen Zhang and Xianwen Gao

International Journal of Production Research, 2021, vol. 59, issue 16, 4794-4810

Abstract: In recent years, the technology of deep learning has made great achievements in the field of machine learning. In this study, with the help of the transfer learning method, a kind of soft sensor is designed for the classification of iron ore tailings grade. Firstly, a sample database of froth images of flotation tailings was established. Secondly, the three most reliable models are determined after comparing the accuracy of 13 deep neural network models applied in the flotation froth image. A more accurate hybrid deep neural network model is established, with an accuracy of 97%. Finally, a software system is designed and developed, which can operate stably in the flotation plant. The experimental results show the effectiveness of the proposed hybrid deep neural network in the field of iron ore froth flotation.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2021.1894366 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:59:y:2021:i:16:p:4794-4810

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2021.1894366

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().