Sensitive Feature Selection for Industrial Flotation Process Soft Sensor Based on Multiswarm PSO With Collaborative Search

被引:2
作者
Xie, Shiwen [1 ]
Yu, Yongjia [1 ]
Xie, Yongfang [1 ]
Tang, Zhaohui [1 ]
机构
[1] Cent South Univ, Sch Automat, Changsha 410083, Peoples R China
关键词
Feature extraction; Soft sensors; Zinc; Correlation; Mutual information; Image color analysis; Collaboration; Collaborative search (CS); feature selection (FS); flotation process; sensitivity coefficient; soft sensor; PARTICLE SWARM OPTIMIZER; MUTUAL INFORMATION; NETWORK;
D O I
10.1109/JSEN.2024.3381837
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Concentrate grade and recovery are key production indexes (KPIs) for industrial flotation process. To establish the soft sensor model of the concentrate grade and recovery, a lot of froth image features are extracted as the input variables. However, these image features contain some redundant and irrelevant features. To improve the efficiency without degrading the performance of the soft sensor model, a sensitive feature selection (FS) method is proposed in this article. Sensitivity coefficient is defined to weigh the attribute significance of features to label, which is calculated by gray correlation analysis. Then, the criterion of sensitive FS based on minimal-redundancy-maximal-relevance (mRMR) is proposed. To solve the FS problem, a multiswarm particle swarm optimization (PSO) with collaborative search PSO (CS-PSO) is developed. Information exchange mechanism among three particle swarms in CS is proposed to improve the search effect and search accuracy. Self-adjusting structure RBFNN (SA-RBFNN) is employed to establish the soft sensor model to predict the concentrate grade based on the selected froth image features. The effectiveness of the proposed method is validated by the industrial flotation process data by comparing with other methods.
引用
收藏
页码:17159 / 17168
页数:10
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