Soft sensor of sodium aluminate solution concentration based on improved differential evolution algorithm and LSSVM

doi:10.3969/j.issn.0438-1157.2013.05.027

CIESC Journal ›› 2013, Vol. 64 ›› Issue (5): 1704-1709.DOI: 10.3969/j.issn.0438-1157.2013.05.027

Previous Articles Next Articles

Soft sensor of sodium aluminate solution concentration based on improved differential evolution algorithm and LSSVM

QIAN Xiaoshan^1,2, YANG Chunhua¹, XU Lisha³

1. School of Information Science & Engineering, Central South University, Changsha 410083, Hunan, China;
2. School of Physical Science and Technology, Yichun University, Yichun 336000, Jiangxi, China;
3. She Wai School, Central South University of Forestry and Technology, Changsha 410004, Hunan, China

Received:2012-08-16 Revised:2013-01-02 Online:2013-05-05 Published:2013-05-05
Supported by:
supported by High-tech Research and Development Program of China(2009AA04Z124, 2009AA04Z137), the National Natural Science Foundation of China(60874069)and the National Outstanding Youth Science Foundation Project(61025015).

基于改进差分进化和最小二乘支持向量机的铝酸钠溶液浓度软测量

钱晓山^1,2, 阳春华¹, 徐丽莎³

1. 中南大学信息科学与工程学院, 湖南长沙 410083;
2. 宜春学院物理科学与工程技术学院, 江西宜春 336000;
3. 中南林业科技大学涉外学院, 湖南长沙 410004

通讯作者: 钱晓山
作者简介:钱晓山(1980-),男,博士研究生,讲师。
基金资助:
国家高技术研究发展计划项目(2009AA04Z124, 2009AA04Z137);国家自然科学基金项目(60874069);国家杰出青年科学基金项目(61025015)。

Abstract

Abstract: Aiming at online testing of concentration of sodium aluminate solution in evaporation process of alumina production, a modeling method for concentration of sodium aluminate solution based on improved differential evolution algorithm and least squares support vector machine was proposed.The input variables of the soft sensor model were determined by analyzing process parameters based on grey relational analysis and kernel principal components analysis,and then the LSSVM model was established based on improved differential evolution algorithm and compared with DE-LSSVM soft sensor model.Finally, the experimental results of industrial production data of evaporation process showed that the new model had better learning ability and generalization performance and higher prediction accuracy, and could provide necessary guidance for the evaporation process operation optimization.

Key words: improved differential evolution, least squares support vector machine, concentration of sodium aluminate solution, soft sensor

摘要： 针对氧化铝蒸发过程铝酸钠溶液浓度难以在线检测问题,提出了改进差分进化和最小二乘支持向量机的铝酸钠溶液浓度软测量建模方法。首先基于灰色关联分析和核主成分分析确定模型的输入变量,再用改进差分进化算法的最小二乘支持向量机构建软测量模型。并与DE-LSSVM软测量模型进行比较;最后应用蒸发过程生产数据进行验证,结果表明,新模型具有更好的学习能力和泛化性能且预测精度更高,可为蒸发过程操作优化提供必要的指导。

关键词: 改进差分进化, 最小二乘支持向量机, 铝酸钠溶液浓度, 软测量

CLC Number:

TP274

QIAN Xiaoshan, YANG Chunhua, XU Lisha. Soft sensor of sodium aluminate solution concentration based on improved differential evolution algorithm and LSSVM[J]. CIESC Journal, 2013, 64(5): 1704-1709.

钱晓山, 阳春华, 徐丽莎. 基于改进差分进化和最小二乘支持向量机的铝酸钠溶液浓度软测量[J]. 化工学报, 2013, 64(5): 1704-1709.

References 18

[1]	Vapnik V N.The Nature of Statistical Learning Theory[M].New York:Springer-Verlag,1995
[2]	Vapnik V, Levin E, Le Cun Y.Measuring the VC dimension of learning machines[J].Neural Computation(S0899-7667), 1994(6):851-876
[3]	Smola A J,Scholkopf B.A tutorial on support vector regression.NeuroCOLT2 Technical Report. NC2-TR-1998-030.London:Royal Holloway College,University of London,1998
[4]	Sanchez A D.Advanced support vector machines and kernel methods[J].Neurocomputing,2003,55(1):5-20
[5]	Suykens Jak, Vandewall J.Least squares support vector machine classifiers[J].Neural Processing Letters,1999, 9(3):293-300
[6]	Pelckmans K, Suykens Jak, de Moor B.Building sparse representations and structure determination on LSSVM substrates[J].Neurocomputing(Special Issue), 2005, 64:137-159
[7]	Wu Jinglong(吴景龙),Yang Shuxia(杨淑霞),Liu Chengshui(刘承水).Parameter selection for support vector machines based on genetic algorithms to short-term power load forecasting[J].Journal of Central South University:Science and Technology(中南大学学报:自然科学版),2009,40(1):180-l84
[8]	Osowski S,Siroic R,Arkiewicz M, et al.Application of suppor tvector machine and genetic algorithm for improved blood cell recognition[J].Instrumentation and Measurement,2009,58(7):2159-2168
[9]	Liu Ruilan(刘瑞兰), Mou Shengjing(牟盛静), Chu Jian(褚健),et al.Modeling soft sensor based on support vector machine and particle swarm optimization algorithms[J].Control Theory and Applications(控制理论与应用),2006, 23(6):895-900
[10]	Li Wei(栗伟),Su Hongye(苏宏业),Liu Ruilan(刘瑞兰).Parameter estimation of catalytic cracking model using PSO algorithm[J].CIESC Journal(化工学报), 2010,61(8):1927-1932
[11]	Jiang Huaqin(蒋华琴),Liu Xinggao(刘兴高).Optimal melt index prediction based on ICPSO_WLSSVM algorithm for industrial propylene polymerization[J].CIESC Journal(化工学报), 2012, 63(3):866-872
[12]	Lin Bihua(林碧华), Gu Xingsheng(顾幸生).Soft sensor modeling based on DE-LSSVM[J].Journal of Chemical Industry and Engineering(China)(化工学报), 2008,59(7):1681-1685
[13]	Li Huajie(李华杰),Lü Wenxiang(吕文祥),Huang Dexian(黄德先).Application of DE-based multivariable predictive control in CDU[J].Chemical Industry and Engineering Progress(化工进展), 2009, 28(4):584-587
[14]	Lü Zhijun(吕志军), Yang Jianguo(杨建国), Xiang Qian(项前), et al.Research on support vector machines based predictive model for yarn quality[J].Control and Decision(控制与决策), 2007, 22(6):693-696
[15]	Storn R, Price K.Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J].Journal of Global Optimization, 1997, 11(4):341-359
[16]	Hooke R,Jeeves T A.Direct search solution of numerical and statistical problems[J].J. Ass. Coumput. Mach.,1961,8:212-229
[17]	Kuo Y,Yang T,Huang G W.The use of grey relational analysis in solving multiple attribute decision making problems[J].Computers & Industrial Engineering,2008, 55(1):80-93
[18]	Baudat G,Anouar F.Feature vector selection and projection using kernels[J].Neurocomputing,2003,55(12):21-38

Soft sensor of sodium aluminate solution concentration based on improved differential evolution algorithm and LSSVM

基于改进差分进化和最小二乘支持向量机的铝酸钠溶液浓度软测量

PDF (PC)

Knowledge

Cited

Abstract

Cite this article

share this article

References 18

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Linqi YAN, Zhenlei WANG. Multi-step predictive soft sensor modeling based on STA-BiLSTM-LightGBM combined model [J]. CIESC Journal, 2023, 74(8): 3407-3418.
[2]	Weiming SHAO, Wenxue HAN, Wei SONG, Yong YANG, Can CHEN, Dongya ZHAO. Dynamic soft sensor modeling method based on distributed Bayesian hidden Markov regression [J]. CIESC Journal, 2023, 74(6): 2495-2502.
[3]	LIU Cong, XIE Li, YANG Huizhong. Multi-model soft sensor development for penicillin fermentation process based on improved density peak clustering [J]. CIESC Journal, 2021, 72(3): 1606-1615.
[4]	Yuhao DU, Gaowei YAN, Rong LI, Fang WANG. Multiple working conditions soft sensor modeling method of geodesic flow kernel based on locally linear embedding [J]. CIESC Journal, 2020, 71(3): 1278-1287.
[5]	YANG Yijun,WANG Zhenlei,WANG Xin. Soft sensor modeling method based on hybrid model of nearest neighbor and neural network [J]. CIESC Journal, 2020, 71(12): 5696-5705.
[6]	Xuezhi DAI,Weili XIONG. A fast active learning method based on kernel extreme learning machine and its application for soft sensing [J]. CIESC Journal, 2020, 71(11): 5226-5236.
[7]	Xiaoqin LIAN, Liwei WANG, Sa AN, Wei WEI, Zaiwen LIU. On soft sensor of chemical oxygen demand by SOM-RBF neural network [J]. CIESC Journal, 2019, 70(9): 3465-3472.
[8]	Jing WU, Yiqi LIU, Jian LIU, Daoping HUANG, Yu QIU, Guangping YU. Study on the soft sensor of multi-kernel relevance vector machine based on time difference [J]. CIESC Journal, 2019, 70(4): 1472-1484.
[9]	Wenpeng JI, Huizhong YANG. Multi-manifold soft sensor based on modified expanding search clustering algorithm [J]. CIESC Journal, 2019, 70(2): 723-729.
[10]	Bao ZHU, Junfei QIAO. Features extracted from auto-encoder based echo state network and its applications to process modeling [J]. CIESC Journal, 2019, 70(12): 4770-4776.
[11]	Rongrong ZHAO, Zhonggai ZHAO, Fei LIU. Gaussian process regression modeling of fermentation process based on k-nearest neighbor mutual information [J]. CIESC Journal, 2019, 70(12): 4741-4748.
[12]	QIU Yu, LIU Yiqi, WU Jing, HUANG Daoping. A self-adaptive multi-output soft sensor modeling based on deep neural network [J]. CIESC Journal, 2018, 69(7): 3101-3113.
[13]	ZHANG Lei, ZHANG Xiaogang, CHEN Hua. Soft sensors for multi-stage batch processes based on Gath-Geva algorithm and kernel extreme learning machine [J]. CIESC Journal, 2018, 69(6): 2576-2585.
[14]	SHAO Weiming, TIAN Xuemin, SONG Zhihuan. Ensemble learning-based soft sensor method for multi-product chemical processes [J]. CIESC Journal, 2018, 69(6): 2551-2559.
[15]	MA Jian, DENG Xiaogang, WANG Lei. Industrial process soft sensor method based on deep learning ensemble support vector machine [J]. CIESC Journal, 2018, 69(3): 1121-1128.