Title: An improved K-means algorithm based recognition method for working condition of flotation process

Authors: Yongheng Zhao; Tao Peng; Kang Li

Addresses: School of Information Science and Engineering, Central South University, Changsha, 65292, Hunan Province, China ' School of Information Science and Engineering, Central South University, Changsha, 65292, Hunan Province, China ' The Institute of Automation, Chinese Academy of Sciences (IACAS), Beijing, 100190, China

Abstract: In this paper, a recognition method based on an improved K-means algorithm with the priori knowledge of the process is proposed for the recognition of flotation working conditions. The proposed method consists of two major stages. In the offline classification stage, the bubble feature of images under different bubble status is first extracted to obtain the dataset. Then the obtained dataset is clustered by the improved K-means algorithm with the priori knowledge. At last, the working condition is classified and their root causes under different bubble status and ore grade are analysed. In the online recognition stage, the current bubble status is first determined. Then the current working condition is recognised by the classification algorithm with the current ore grade. Finally, the proposed method is verified by the real data from an antimony flotation processes.

Keywords: froth flotation; machine learning; K-means.

DOI: 10.1504/IJSCIP.2017.089809

International Journal of System Control and Information Processing, 2017 Vol.2 No.2, pp.113 - 126

Received: 18 Apr 2016
Accepted: 27 Feb 2017
Published online: 12 Feb 2018 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article