基于溶液图像时序特征的元素组分含量动态监测系统

doi:10.11772/j.issn.1001-9081.2020101682

计算机应用 ›› 2021, Vol. 41 ›› Issue (10): 3075-3081.DOI: 10.11772/j.issn.1001-9081.2020101682

所属专题：前沿与综合应用

基于溶液图像时序特征的元素组分含量动态监测系统

陆荣秀^1,2, 陈明明^1,2, 杨辉^1,2, 朱建勇^1,2

1. 华东交通大学电气与自动化工程学院, 南昌 330013;
2. 江西省先进控制与优化重点实验室(华东交通大学), 南昌 330013

收稿日期:2020-10-30 修回日期:2021-01-26 出版日期:2021-10-10 发布日期:2021-03-04
通讯作者: 陆荣秀
作者简介:陆荣秀(1976-),女,广西灌阳人,副教授,博士,主要研究方向:复杂工业过程建模、控制与优化、智能检测;陈明明(1997-),男,河南开封人,硕士研究生,主要研究方向:复杂工业过程建模、智能检测;杨辉(1965-),男,江西高安人,教授,博士,主要研究方向:复杂工业过程建模、控制与优化、光机电一体化在线检测;朱建勇(1977-),男,江西吉安人,副教授,博士,主要研究方向:复杂工业过程建模、控制与优化、机器学习。
基金资助:
国家自然科学基金资助项目（61863014，61733005，61963015）；国家重点研发计划项目（2020YFB1713700，2020YFB1713703）。

Element component content dynamic monitoring system based on time sequence characteristics of solution images

LU Rongxiu^1,2, CHEN Mingming^1,2, YANG Hui^1,2, ZHU Jianyong^1,2

1. School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang Jiangxi 330013, China;
2. Key Laboratory of Advanced Control and Optimization of Jiangxi Province(East China Jiaotong University), Nanchang Jiangxi 330013, China

Received:2020-10-30 Revised:2021-01-26 Online:2021-10-10 Published:2021-03-04
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61863014, 61733005, 61963015), the National Key Research and Development Program of China (2020YFB1713700, 2020YFB1713703).

摘要/Abstract

摘要： 针对稀土萃取过程中组分含量难以实时监测以及现有组分含量检测方法耗时、耗内存的现状，设计了一种基于溶液图像时序特征的元素组分含量动态监测系统。首先使用图像采集装置获取萃取槽体溶液的时序图像，考虑萃取液颜色特性和单一颜色空间的不全面性，采用主成分分析（PCA）方法在HSI和YUV融合的颜色空间提取图像的时序特征，并结合生产指标构造基于鲸鱼优化算法（WOA）的最小二乘支持向量机（LSSVM）分类器来对工况状态进行判断。然后当工况处于非最佳状态时，在HSV颜色空间对图像提取颜色直方图和颜色矩特征，并开发以溶液图像间的混合特征差值的线性加权值为相似度度量的图像检索系统，从而获取组分含量值。最后进行镨/钕萃取槽体混合溶液测试，结果表明该系统能够实现元素组分含量的动态监测。

关键词: 稀土萃取, 时序特征, 主成分分析, 鲸鱼优化算法, 最小二乘支持向量机, 组分含量, 图像检索

Abstract: In view of the difficulties in real-time monitoring of component contents in rare earth extraction process and the high time consumption and memory consumption of existing component content detection methods, a dynamic monitoring system for element component content based on time sequence characteristics of solution images was designed. Firstly, the image acquisition device was used to obtain the time sequence image of the extraction tank solution. Considering the color characteristics of the extracted liquid and the incompleteness of single color space, the time sequence characteristics of the image were extracted in the color space of the fusion of HSI (Hue, Saturation, Intensity) and YUV (Luminance-Bandwidth-Chrominance) by using Principal Component Analysis (PCA) method, and combined with the production index, the Whale Optimization Algorithm (WOA) based Least Squares Support Vector Machine (LSSVM) classifier was constructed to judge the status of the working condition. Secondly, when the working condition was not optimal, the color histogram and color moment features of the image were extracted in HSV (Hue, Saturation, Value) color space, and an image retrieval system was developed with the linear weighted value of the mixed feature difference between solution images as the similarity measurement to obtain the value of component content. Finally, the test of the mixed solution of the praseodymium/neodymium extraction tank was carried out, and the results show that this system can realize the dynamic monitoring of element component content.

Key words: rare earth extraction, time sequence characteristic, Principal Component Analysis (PCA), Whale Optimization Algorithm (WOA), Least Squares Support Vector Machine (LSSVM), component content, image retrieval

中图分类号:

TP274

陆荣秀, 陈明明, 杨辉, 朱建勇. 基于溶液图像时序特征的元素组分含量动态监测系统[J]. 计算机应用, 2021, 41(10): 3075-3081.

LU Rongxiu, CHEN Mingming, YANG Hui, ZHU Jianyong. Element component content dynamic monitoring system based on time sequence characteristics of solution images[J]. Journal of Computer Applications, 2021, 41(10): 3075-3081.

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基于溶液图像时序特征的元素组分含量动态监测系统

Element component content dynamic monitoring system based on time sequence characteristics of solution images

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