SCI和EI收录∣中国化工学会会刊

Chinese Journal of Chemical Engineering ›› 2015, Vol. 23 ›› Issue (12): 1981-1986.DOI: 10.1016/j.cjche.2015.10.007

• 第25届中国过程控制会议专栏 • 上一篇    下一篇

Soft measurement for component content based on adaptive model of Pr/Nd color features

Rongxiu Lu, Hui Yang   

  1. School of Mechanical & Electrical Engineering, Nanchang University, Nanchang 330031, China School of Electrical and Electronic Engineering, East China Jiaotong University, Nanchang 330013, China Key Laboratory of Advanced Control & Optimization of Jiangxi Province, Nanchang 330013, China
  • 收稿日期:2015-05-20 修回日期:2015-08-09 出版日期:2015-12-28 发布日期:2016-01-19
  • 通讯作者: Hui Yang
  • 基金资助:

    Supported by the National Natural Science Foundation of China (51174091, 61364013, 61164013) and Earlier Research Project of the State Key Development Program for Basic Research of China (2014CB360502).

Soft measurement for component content based on adaptive model of Pr/Nd color features

Rongxiu Lu, Hui Yang   

  1. School of Mechanical & Electrical Engineering, Nanchang University, Nanchang 330031, China School of Electrical and Electronic Engineering, East China Jiaotong University, Nanchang 330013, China Key Laboratory of Advanced Control & Optimization of Jiangxi Province, Nanchang 330013, China
  • Received:2015-05-20 Revised:2015-08-09 Online:2015-12-28 Published:2016-01-19
  • Contact: Hui Yang
  • Supported by:

    Supported by the National Natural Science Foundation of China (51174091, 61364013, 61164013) and Earlier Research Project of the State Key Development Program for Basic Research of China (2014CB360502).

摘要: Formeasurement of component content in the extraction and separation process of praseodymium/neodymium (Pr/Nd), a softmeasurement method was proposed based on modeling of ion color features,which is suitable for fast estimation of component content in production field. Feature analysis on images of the solution is conducted, which are captured from Pr/Nd extraction/separation field. H/S components in the HSI color space are selected as model inputs, so as to establish the least squares support vector machine (LSSVM) model for Nd (Pr) content, while the model parameters are determined with the GA algorithm. To improve the adaptability of the model, the adaptive iteration algorithmis used to correct parameters of the LSSVMmodel, on the basis of model correction strategy and new sample data. Using the field data collected from rare earth extraction production, predictive methods for component content and comparisons are given. The results indicate that the proposed method presents good adaptability and high prediction precision, so it is applicable to the fast detection of element content in the rare earth extraction.

关键词: Pr/Nd extraction, Color feature, Component content, Adaptive iterative least squares support vector, machine, Real-time correction

Abstract: Formeasurement of component content in the extraction and separation process of praseodymium/neodymium (Pr/Nd), a softmeasurement method was proposed based on modeling of ion color features,which is suitable for fast estimation of component content in production field. Feature analysis on images of the solution is conducted, which are captured from Pr/Nd extraction/separation field. H/S components in the HSI color space are selected as model inputs, so as to establish the least squares support vector machine (LSSVM) model for Nd (Pr) content, while the model parameters are determined with the GA algorithm. To improve the adaptability of the model, the adaptive iteration algorithmis used to correct parameters of the LSSVMmodel, on the basis of model correction strategy and new sample data. Using the field data collected from rare earth extraction production, predictive methods for component content and comparisons are given. The results indicate that the proposed method presents good adaptability and high prediction precision, so it is applicable to the fast detection of element content in the rare earth extraction.

Key words: Pr/Nd extraction, Color feature, Component content, Adaptive iterative least squares support vector, machine, Real-time correction