Soft-sensing Design Based on Semiclosed-loop Framework*

Chinese Journal of Chemical Engineering ›› 2012, Vol. 20 ›› Issue (6): 1213-1218.

Soft-sensing Design Based on Semiclosed-loop Framework^*

汤奇峰¹, 李德伟¹, 席裕庚¹, 尹德斌²

1. Department of Automation, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai 200240, China;
2. Shanghai Xinhua Control Technology (Group) Co., Ltd, Shanghai 200241, China

收稿日期:2012-04-14 修回日期:2012-05-14 出版日期:2012-12-28 发布日期:2012-12-28
通讯作者: LI Dewei,E-mail:dwli@sjtu.edu.cn
基金资助:
Supported by the National Natural Science Foundation of China (60934007;61074060;61104078);the Research and Innovation Project of Shanghai Education Commission (11CXY08);the State Key Laboratory of Synthetical Automation for Process Industries.

Soft-sensing Design Based on Semiclosed-loop Framework^*

TANG Qifeng¹, LI Dewei¹, XI Yugeng¹, YIN Debin²

1. Department of Automation, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai 200240, China;
2. Shanghai Xinhua Control Technology (Group) Co., Ltd, Shanghai 200241, China

Received:2012-04-14 Revised:2012-05-14 Online:2012-12-28 Published:2012-12-28
Supported by:
Supported by the National Natural Science Foundation of China (60934007;61074060;61104078);the Research and Innovation Project of Shanghai Education Commission (11CXY08);the State Key Laboratory of Synthetical Automation for Process Industries.

摘要/Abstract

摘要： Soft-sensing is widely used in industrial applications.The traditional soft-sensing structure is open-loop without correction mechanism.If the working condition is changed or there is unknown disturbance,the forecast result of soft-sensing model may be incorrect.In order to obtain accurate values,it is necessary to carry out online correction.In this paper,a semiclosed-loop framework (SLF) is proposed to establish a soft-sensing approach,which estimates the input variables in the next moment by a prediction model and calibrates the output variables by a compensation model.The experimental results show that the proposed method has better prediction accuracy and robustness than other open-loop models.

关键词: soft-sensing, neural network, semiclosed-loop framework

Abstract: Soft-sensing is widely used in industrial applications.The traditional soft-sensing structure is open-loop without correction mechanism.If the working condition is changed or there is unknown disturbance,the forecast result of soft-sensing model may be incorrect.In order to obtain accurate values,it is necessary to carry out online correction.In this paper,a semiclosed-loop framework (SLF) is proposed to establish a soft-sensing approach,which estimates the input variables in the next moment by a prediction model and calibrates the output variables by a compensation model.The experimental results show that the proposed method has better prediction accuracy and robustness than other open-loop models.

Key words: soft-sensing, neural network, semiclosed-loop framework

汤奇峰, 李德伟, 席裕庚, 尹德斌. Soft-sensing Design Based on Semiclosed-loop Framework^*[J]. Chinese Journal of Chemical Engineering, 2012, 20(6): 1213-1218.

TANG Qifeng, LI Dewei, XI Yugeng, YIN Debin. Soft-sensing Design Based on Semiclosed-loop Framework^*[J]. Chin.J.Chem.Eng., 2012, 20(6): 1213-1218.

参考文献

1 Leung,F.H.F.,Lam,H.K.,Ling,S.H.,Tam,P.K.S.,"Tuning of the structure and parameters of a neural network using an improved genetic algorithm",IEEE Transactions on Neural Network,14 (1),79-88 (2003).
2 Fu,Y.F.,Su,H.Y.,Zhang,Y.,Chu,J.,"Adaptive soft-sensor modeling algorithm based on FCMISVM and its application in PX adsorption separation process",Chin.J.Chem.Eng.,16 (5),746-751 (2008).
3 Wang,J.L.,Zhao,L.Q.,Yu,T.,"On-line estimation in fed-batch fermentation process using state space model and unscented Kalman filter",Chin.J.Chem.Eng..,18 (2),258-264 (2010).
4 Sun,L.F.,Wang,Y.C.,"Soft-sensing of oxygen content of flue gas based on mixed model",Energy Procedia.,12,221-226 (2012).
5 Dai,X.Z.,Wang,W.C.,Ding,Y.H.,Sun,Z.Y.,"Assumed inherent sensor inversion based ANN dynamic soft-sensing method and its application in erythromycin fermentation process",Computers & Chemical Engineering,30 (8),1203-1225 (2006).
6 Wu,M.,Lei,Q.,Cao,W.H.,She,J.H.,"Integrated soft sensing of coke-oven temperature",Control Engineering Practice,19 (10),1116-1125 (2011).
7 Yan,W.W.,Shao,H.H.,Wang,X.F.,"Soft sensing modeling based on support vector machine and Bayesian model selection",Computers & Chemical Engineering,28 (8),1489-1498 (2004).
8 Tsai,J.T.,Chou,J.H.,Liu,T.K.,"Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm",IEEE transaction on Neural network,17 (1),69-80 (2006).
9 Ma,J.,Zhang,G.Q.,Lu,J.,"A method for multiple periodic factor prediction problems using complex fuzzy sets",IEEE Transaction on Fuzzy System,20 (1),32-45 (2012).
10 Ismail,S.,Shabri,A.,Samsudin,R.,"A hybrid model of selforganizing maps (SOM) and least square support vector machine (LSSVM) for time-series forecasting",Expert Systems with Applications,38 (8),10574-10578 (2011).
11 Lu,J.F.,Wang,Q.M.,Li,Y.,Yue,G.X.,Yam,Y.,Baldur,E.,Shen,J.Z.,Yu,L.,"Unburned carbon loss in fly ash of CFB boilers burning hard coal",Tsinghua Science and Technology,8 (6),687-691 (2003).
12 Eriksson,M.,Golriz,M.R.,"Radiation heat transfer in circulating fluidized bed combustors",International Journal of Thermal Sciences,44 (4),399-409 (2005).
13 Guan,G.Q.,Fushimi,C.,Tsutsumi,A.,Ishizuka,M.,Matsuda,S.,Hatano,H.,Suzuki,Y.,"High-density circulating fluidized bed gasifier for advanced IGCC/IGFC—Advantages and challenges",Particuology,8 (6),602-606 (2010).
14 Belaissaoui,B.,Cabot,G.,Cabot,M.S.,Willson,D.,Favre,E.,"An energetic analysis of CO₂ capture on a gas turbine combining flue gas recirculation and membrane separation",Energy,38 (1),167-175 (2012).
15 Guedea,I.,Bolea,I.,Lupiá ez,C.,Cortés,N.,Teruel,E.,Pallarés,J.,Díez,L.I.,Romeo,L.M.,"Control system for an oxy-fuel combustion fluidized bed with flue gas recirculation",Energy Procedia,4,972-979 (2011).
16 Xie,M.Y.,Li,P.P.,Guo,H.F.,Gao,L.X.,Yu,J.,"Ternary system of Fe-based ionic liquid,ethanol and water for wet flue gas desulfurization",Chin.J.Chem.Eng.,20 (1),140-145 (2012).
17 Tang,Q.F.,Zhao,L.,Qi,R.B.,Cheng,H.,Qian,F.,"Tuning the structure and parameters of a neural network by using cooperative quantum particle swarm algorithm",Measuring Technology and Mechatronics Automation,48,1328-1332 (2011).
18 Tang,Q.F.,"The cooperative quantum-particle swarm algorithm and its application in the energy utilization optimization of the steam network",M.E.Thesis,East China University of Science & Technology,China (2011).(in Chinese)
19 Liu,G.H.,Zhou,D.W.,Xu,H.X.,Mei,C.L.,"Model optimization of SVM for a fermentation soft sensor",Expert Systems with Applications,37 (4),2708-2713 (2010).
20 Chi,M.M.,Feng,R.,Bruzzone,L.,"Classification of hyperspectral remote-sensing data with primal SVM for small-sized training dataset problem",Advances in Space Research,41 (11),1793-1799 (2008).

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Soft-sensing Design Based on Semiclosed-loop Framework^*

Soft-sensing Design Based on Semiclosed-loop Framework^*

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