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

doi:10.1016/j.cjche.2015.10.007

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

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

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/Abstract

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

Rongxiu Lu, Hui Yang. Soft measurement for component content based on adaptive model of Pr/Nd color features[J]. Chinese Journal of Chemical Engineering, 2015, 23(12): 1981-1986.

Rongxiu Lu, Hui Yang. Soft measurement for component content based on adaptive model of Pr/Nd color features[J]. Chin.J.Chem.Eng., 2015, 23(12): 1981-1986.

参考文献

[1] C.H. Yan, J.T. Jia, C.S. Liao, B.G. Li,W.L. Li, G.D'. Ascenzo, Application of 241Am EDXRF to the determination of rare earth samples of solvent extraction processes, J. Alloys Compd. 275-277 (1998) 940-943.

[2] B. Zhang, J.T. Jia, C.S. Liao, C.H. Yan, Determination of neodymium in rare earth mixture by spectrophotometry, Chin. J. Rare Metals 32 (4) (2008) 540-542.

[3] Z.G. Deng, T.H. Xu, The current status and development tendency of ionic rare earth extraction separation process, Nonferrous Metals Sci. Eng. 3 (4) (2012) 20-23.

[4] H. Yang, T.Y. Chai, Component content soft-sensor based on neural networks in rareearth countercurrent extraction process, Acta Autom. Sin. 32 (4) (2006) 489-495.

[5] H. Yang, M.H. Tan, T.Y. Chai, Neural networks based component content soft-sensor in countercurrent rare-earth extraction, J. Rare Earths 21 (6) (2003) 691-696.

[6] Z.R. Xiang, S.Q. Liu, Component content soft-sensor in rare earth extraction based on LS-SVM, J. Chin. Rare Earth Soc. 27 (01) (2009) 132-136.

[7] G.W. Hang, The research about the characteristics of rare earth ions in cascade extraction process control application, Jiangxi Metall. 16 (5) (1996) 26-27.

[8] B.F. Cao, Y.F. Xie,W.H. Gui, L.J.Wei, C.H. Yang, Integrated predictionmodel of bauxite concentrate grade based on distributed machine vision, Miner. Eng. 53 (2013) 31-38.

[9] Sameer H. Morar, Martin C. Harris, Dee J. Bradshaw, The use of machine vision to predict flotation performance, Miner. Eng. (2012) 31-36.

[10] W.T. Li, D.H. Wang, T.Y. Chai, Flame image-based burning state recognition for sintering process of rotary kiln using heterogeneous features and fuzzy integral, IEEE Trans. Ind. Inf. 8 (4) (2012) 780-790.

[11] H. Yang, Z.J. Gao, R.X. Lu, Measure method of component content based on rare earth ions color characteristic identification, J. Chin. Soc. Rare Earths 30 (1) (2012) 108-112.

[12] R.X. Lu, H. Yang, K.P. Zhang, Component content soft-sensor of SVM based on ions color characteristics, TELKOMNIKA 10 (6) (2012) 1445-1452.

[13] R.X. Lu, H. Yang, C.M. Ouyang, L.W. Zhu, Forecast of element component content in Pr\Nd extraction process based on PCA- LS_SVM, J. Nanchang Univ. (Nat. Sci.) 37 (6) (2013) 589-593.

[14] R.X. Lu, C.M. Ouyang, H. Yang, Application of HSI color model in Pr/Nd component content detection, Comput. Appl. Chem. 30 (10) (2013) 111-115.

[15] G.UmaMaheswari, K. Ramar, D.Manimegalai, V. Gomathi, An adaptive region based color texture segmentation using fuzzified distance metric, Appl. Soft Comput. 11 (2) (2011) 2916-2924.

[16] J.A.K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, J. Vandewalle, Least squares support vector machines, World Scientific, Singapore, 2002.

[17] G.C. Cawley, N.L.C. Talbot, Fast exact leave-one-out cross-validation of sparse leastsquares support vector machines, Neural Netw. 17 (10) (2004) 1467-1475.

[18] Jae H. Mina, Y.C. Lee, Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters, Expert Syst. Appl. 28 (4) (2005) 603-614.

[19] H. Jiang, Z. Yan, X. Liu, Melt index prediction using optimized least squares support vector machines based on hybrid particle swarm optimization algorithm, Neurocomputing 119 (2013) 469-477.

[20] S.K. Lahiri, K.C. Ghanta, Prediction of pressure drop of slurry flow in pipeline by hybrid support vector regression and genetic algorithm model, Chin. J. Chem. Eng. 16 (6) (2008) 841-848.

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

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

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	Denglong Ma, Ruitao Wu, Zekang Li, Kang Cen, Jianmin Gao, Zaoxiao Zhang. A new method to forecast multi-time scale load of natural gas based on augmentation data-machine learning model[J]. 中国化学工程学报, 2022, 48(8): 166-175.
[2]	Tong Qin, Zhenhao Xi, Ling Zhao, Weikang Yuan. Monte Carlo simulation of sequential structure control of AN-MA-IA aqueous copolymerization by different operation modes[J]. 中国化学工程学报, 2022, 46(6): 231-242.
[3]	Jun Zhang, Qin Wang, Weifeng Shen. Hyper-parameter optimization of multiple machine learning algorithms for molecular property prediction using hyperopt library[J]. 中国化学工程学报, 2022, 52(12): 115-125.
[4]	Jiale Mao, Jiazhi Miao, Yingying Lu, Zheming Tong. Machine learning of materials design and state prediction for lithium ion batteries[J]. 中国化学工程学报, 2021, 37(9): 1-11.
[5]	Zhiquan Wang, Liang Wang, Zhihong Yuan, Bingzhen Chen. Data-driven optimal operation of the industrial methanol to olefin process based on relevance vector machine[J]. 中国化学工程学报, 2021, 34(6): 106-115.
[6]	Jie Yang, Alejandro Gallegos, Cheng Lian, Shengwei Deng, Honglai Liu, Jianzhong Wu. Curvature effects on electric-double-layer capacitance[J]. 中国化学工程学报, 2021, 29(3): 145-152.
[7]	Jiaqi Ding, Nan Xu, Manh Tien Nguyen, Qi Qiao, Yao Shi, Yi He, Qing Shao. Machine learning for molecular thermodynamics[J]. 中国化学工程学报, 2021, 29(3): 227-239.
[8]	Huan Zhang, Peisong Yang, Duli Yu, Kunfeng Wang, Qingyuan Yang. Prediction of methane storage in covalent organic frameworks using big-data-mining approach[J]. 中国化学工程学报, 2021, 39(11): 286-296.
[9]	Weijun Zhu, Xingwang Liu, Xu Hou, Jiayao Hu, Zhenheng Diao. Application of machine learning to process simulation of n-pentane cracking to produce ethylene and propene[J]. 中国化学工程学报, 2020, 28(7): 1832-1839.
[10]	Kang Guan, Yong Gao, Qingfeng Zeng, Xingang Luan, Yi Zhang, Laifei Cheng, Jianqing Wu, Zhenya Lu. Numerical modeling of SiC by low-pressure chemical vapor deposition from methyltrichlorosilane[J]. 中国化学工程学报, 2020, 28(6): 1733-1743.
[11]	Kexin Bi, Tong Qiu. An intelligent SVM modeling process for crude oil properties prediction based on a hybrid GA-PSO method[J]. 中国化学工程学报, 2019, 27(8): 1888-1894.
[12]	Wei Gao, Xiao Feng. Synthesis of the fluid machinery network in a circulating water system[J]. Chinese Journal of Chemical Engineering, 2019, 27(3): 587-597.
[13]	Bing Wang, Feng Qian, Weimin Zhong. Wind field reconstruction for the dispersion modeling of accidental chemical spills on complex geometry[J]. 中国化学工程学报, 2019, 27(11): 2712-2724.
[14]	Mohammad Mesbah, Ebrahim Soroush, Mashallah Rezakazemi. Development of a least squares support vector machine model for prediction of natural gas hydrate formation temperature[J]. , 2017, 25(9): 1238-1248.
[15]	Liangliang Sun, Jianghua Wu, Haiqi Jia, Xuebin Liu. Research on fault detection method for heat pump air conditioning system under cold weather[J]. Chinese Journal of Chemical Engineering, 2017, 25(12): 1812-1819.