Soft sensing has been widely used in chemical industry to build an online monitor of the variables
which are unmeasurable online or measurable online but with a high cost. One inherent difficulty is insufficiency
of the training samples because the labeled data are limited. Besides, the traditional soft-sensing structure has
no online correction mechanism. The forecasting result may be incorrect if the working condition is changed. In
this work, a semi-supervised learning (SSL) method is proposed to build the soft-sensing model by use of the
unlabeled data. Meanwhile, an online correction mechanism is proposed to establish a soft-sensing approach. The
mechanism estimates the input variables at each step 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 generalization ability than other approaches.
TANG Qi-feng (汤奇峰), LI De-wei* (李德伟), XI Yu-geng (席裕庚)
. Soft-Sensing Method with Online Correction Based on Semi-Supervised Learning[J]. Journal of Shanghai Jiaotong University(Science), 2015
, 20(2)
: 171
-176
.
DOI: 10.1007/s12204-015-1606-y
[1] Gonzaga J C B, Meleiro L A C, Kiang C, et al.ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process[J]. Computer Chemical Engineering, 2009, 34(1): 43-49.
[2] Topcu I B, Saridemir M. Prediction of rubberized mortar properties using artificial neural network and fuzzy logic [J]. Journal of Materials Processing Technology,2008, 199(1-3): 108-118.
[3] Vijayabaskar V, Gupta R, Chakrabarti P P, et al. Prediction of properties of rubber by using artificial neural networks [J]. Journal of Applied Polymer Science, 2006, 100(3): 2227-2237.
[4] Facco P, Doplicher F, Bezzo F, et al. Moving average PLS soft sensor for online product quality estimation in an industrial batch polymerization process[J]. Journal of Process Control, 2009, 19(3): 520-529.
[5] Liu X Q, Kruger U, Littler T, et al. Moving window kernel PCA for adaptive monitoring of nonlinear processes [J]. Chemometrics and Intelligent Laboratory System, 2009, 96(2): 132-143.
[6] Kim K, Lee J M, Lee I B. A novel multivariate regression approach based on kernel partial least squares with orthogonal signal correction [J]. Chemometrics and Intelligent Laboratory System, 2009, 79(1-2): 22-30.
[7] Su Z G, Wang P H, Shen J, et al. Multi-model strategy based evidential soft sensor model for predicting evaluation of variables with uncertainty [J]. Applied Soft Computing, 2011, 11(2): 2595-2610.
[8] Zhong W, Yu J S. MIMO soft sensors for estimating product quality with online correction [J]. Chemical Engineering Research and Design, 2000, 78(4): 612-620.
[9] Peng Xiao-qi, Sun Yuan, Tang Ying. Performance monitoring and assessment of a soft-sensor and its adaptive correction [J]. Journal of Chemical Industry and Engineering, 2012, 63(5): 1474-1483 (in Chinese).
[10] Tang Q F, Li D W, Xi Y G, et al. Soft-sensing design based on semiclosed-loop framework [J]. Chinese Journal of Chemical Engineering, 2012, 20(6): 1213-1218.
[11] Nigam K, Mccallum A K, Thrun S, et al. Text classification from labeled and unlabeled documents using EM [J]. Machine Learning, 2000, 39(2-3): 103-134.
[12] Lam H K, Ling S H, Tam P K S, et al. Tuning of the structure and parameters of neural networks using an improved genetic algorithm [J]. IEEE Transactions on Neural Network, 2003, 14(1): 79-88.
[13] Eriksson M, Golriz M R. Radiation heat transfer in circulating fluidized bed combustors [J]. International Journal of Thermal Sciences, 2005, 44(4): 399-409.
[14] Guedea I, Bolea I, Lupi′a?nez C, et al. Control system for an oxy-fuel combustion fluidized bed with flue gas recirculation [J]. Energy Procedia, 2011, 4: 972-979.
[15] Tang Q F, Zhao L, Qi R B, et al. Tuning the structure and parameters of a neural network by using cooperative quantum particle swarm algorithm [J]. Measuring Technology and Mechatronics Automation, 2011,48: 1328-1332.
[16] Tang Qing-feng. The cooperative quantum-particle swarm algorithm and its application in the energy utilization optimization of the steam network [D]. Shanghai:East China University of Science & Technology,2011 (in Chiense).
[17] Yeh T T, Espina P I, Osella S A. An intelligent ultrasonic flow meter for improved flow measurement and flow calibration facility [C]//Instrumentation and Measurement Technology Conference. Budapest, Hungary:IEEE, 2001: 1741-1746.
