Citation: Liu, H.; He, M.; Cai, W.;
Zhou, G.; Wang, Y.; Li, L. Working
Condition Recognition of a Mineral
Flotation Process Using the
DSFF-DenseNet-DT. Appl. Sci. 2022,
12, 12223. https://doi.org/10.3390/
app122312223
Academic Editor: João M. F.
Rodrigues
Received: 30 October 2022
Accepted: 28 November 2022
Published: 29 November 2022
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Article
Working Condition Recognition of a Mineral Flotation Process
Using the DSFF-DenseNet-DT
Hongchang Liu
1
, Mingfang He
1,
*, Weiwei Cai
2
, Guoxiong Zhou
1
, Yanfeng Wang
3
and Liujun Li
4
1
School of Computer and Information Engineering, Central South University of Forestry and Technology,
Changsha 410004, China
2
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
3
National University of Defense Technology, Changsha 410015, China
4
Department of Soil and Water Systems, University of Idaho, Moscow, ID 83844, USA
* Correspondence: t20162306@csuft.edu.cn
Abstract:
The commonly used working condition recognition method in the mineral flotation process
is based on shallow features of flotation froth images. However, the shallow features of flotation
froth images frequently have an excessive amount of redundant and noisy information, which has an
impact on the recognition effect and prevents the flotation process from being effectively optimized.
Therefore, a working condition recognition method for the mineral flotation process based on a
deep and shallow feature fusion densely connected network decision tree (DSFF-DenseNet-DT) is
proposed in this paper. Firstly, the color texture distribution (CTD) and size distribution (SD) of a
flotation froth image obtained in advance are approximated by the nonparametric kernel density
estimation method, and a set of kernel function weights is obtained to represent the color texture
and size features, while the deep features of the flotation froth image are extracted through the
densely connected network (DenseNet). Secondly, a two-stage feature fusion method based on a
stacked autoencoder after Concat (Cat-SAE) is proposed to fuse and reduce the dimensionality of the
extracted shallow features and deep features so as to maximize the comprehensive description of
the features and eliminate redundant and noisy information. Finally, the feature vectors after fusion
dimensionality reduction are fed into the densely connected network decision tree (DenseNet-DT) for
working condition recognition. Multiple experiments employing self-built industrial datasets reveal
that the suggested method’s average recognition accuracy, precision, recall and F1 score reach 92.67%,
93.9%, 94.2% and 0.94, respectively. These results demonstrate the proposed method’s usefulness.
Keywords:
mineral flotation; feature fusion; kernel density estimation; densely connected network;
working condition recognition
1. Introduction
The aim of the mineral flotation process is to separate valuable minerals from useless
materials or other minerals so as to obtain upgraded minerals. The concentrate grade
is measured by calculating the percentage of the recovered useful element mass in the
total concentrate mass. As a key performance indicator to evaluate the flotation effect in
metallurgical enterprises, the concentrate grade directly reflects the quality of flotation
working conditions. When the grade of the concentrate is too low, the working conditions
of the flotation process will be in an abnormal state. The operator should adjust the flotation
operating variables such as the inlet air flow and pulp level, related to the flotation working
conditions in order to improve the concentrate grade. The better the flotation working
conditions, the higher the concentrate grade. Thus, the concentrate grade reflects the
flotation working conditions.
In the process of mineral flotation, the process working conditions are judged mainly
according to the color, size and other features of the flotation froth, and operating parameters
Appl. Sci. 2022, 12, 12223. https://doi.org/10.3390/app122312223 https://www.mdpi.com/journal/applsci
