镨/钕萃取过程组分含量多RBF模型预测

doi:10.11949/j.issn.0438-1157.20151950

化工学报 ›› 2016, Vol. 67 ›› Issue (3): 974-981.DOI: 10.11949/j.issn.0438-1157.20151950

镨/钕萃取过程组分含量多RBF模型预测

陆荣秀, 叶兆斌, 杨辉, 何峰

华东交通大学电气与电子工程学院, 江西南昌 330013;
江西省先进控制与优化重点实验室, 江西南昌 330013

收稿日期:2015-12-22 修回日期:2015-12-29 出版日期:2016-03-05 发布日期:2016-01-12
通讯作者: 杨辉
基金资助:
国家自然科学基金项目(61364013,51174091,61563015);国家重点基础研究发展计划项目前期研究专项(2014CB360502)。

Multi-RBF models based prediction of component content for Pr/Nd extraction process

LU Rongxiu, YE Zhaobin, YANG Hui, HE Feng

School of Electrical and Electronic Engineering, East China Jiaotong University, Nanchang 330013, Jiangxi, China;
Key Laboratory of Advanced Control & Optimization of Jiangxi Province, Nanchang 330013, Jiangxi, China

Received:2015-12-22 Revised:2015-12-29 Online:2016-03-05 Published:2016-01-12
Contact: 67
Supported by:
supported by the National Natural Science Foundation of China (61364013, 51174091, 61563015) and the Earlier Research Project of the National Basic Research and Development Program of China (2014CB360502).

摘要/Abstract

摘要：

针对具有特征颜色的镨/钕(Pr/Nd)萃取过程中元素组分含量难以快速准确检测的问题,提出了一种基于多RBF神经网络模型的组分含量建模及其自适应校正方法。通过选择Pr/Nd溶液图像特征H、S分量一阶矩为模型的输入变量,采用减法聚类对样本数据进行分类并建立相应的子模型;当萃取运行环境或对象特性发生变化导致模型精度不够时,根据模型参数调整策略自动调整各子模型的网络结构和参数,实现元素组分含量的准确预测。针对某Pr/Nd生产过程实际数据实验对比,结果表明本文方法能够满足稀土萃取过程元素组分含量检测的高准确度和快速性要求。

关键词: 萃取过程, 组分含量, 神经网络, 多模型, 自适应, 预测

Abstract:

It is difficult to rapidly and accurately detect the component content in the praseodymium/neodymium (Pr/Nd) extraction process. This paper proposes a multi-RBF model and its adaptive correction method of component content. Extracting the HSI image features of Pr/Nd mixed solution, the first moments of H and S components are selected as input variables. Using the subtractive clustering algorithm, the sample data are divided into several categories and the corresponding sub-models are obtained based on RBF neural network. To further realize the high-accuracy prediction of the element component content, a parameters adjustment strategy is designed to automatically adjust the network structure and parameters of sub-models when the change of operating environment or the object characteristics results in the accuracy of the prediction model doesn't meet control requirements. The comparison experiments on actual production data from Pr/Nd extraction process show that the proposed method can meet the high-accuracy and rapid requirements of element component content detection in rare earth extraction process.

Key words: extraction process, component content, neural network, multi-models, adaptive, prediction

中图分类号:

TP273

陆荣秀, 叶兆斌, 杨辉, 何峰. 镨/钕萃取过程组分含量多RBF模型预测[J]. 化工学报, 2016, 67(3): 974-981.

LU Rongxiu, YE Zhaobin, YANG Hui, HE Feng. Multi-RBF models based prediction of component content for Pr/Nd extraction process[J]. CIESC Journal, 2016, 67(3): 974-981.

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镨/钕萃取过程组分含量多RBF模型预测

Multi-RBF models based prediction of component content for Pr/Nd extraction process

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