Soft-sensing method with online correction based on semi-supervised learning

Journal of Shanghai Jiaotong University (Science) - Tập 20 - Trang 171-176 - 2015
Qi-feng Tang1, De-wei Li1, Yu-geng Xi1
1Department of Automation; Key Laboratory of System Control and Information Processing of Ministry of Education, Shanghai Jiaotong University, Shanghai, China

Tóm tắt

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.

Tài liệu tham khảo

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. 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. 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. 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. 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. 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. 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. 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. 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). 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. 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. 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. Eriksson M, Golriz M R. Radiation heat transfer in circulating fluidized bed combustors [J]. International Journal of Thermal Sciences, 2005, 44(4): 399–409. Guedea I, Bolea I, Lupiáñez C, et al. Control system for an oxy-fuel combustion fluidized bed with flue gas recirculation [J]. Energy Procedia, 2011, 4: 972–979. 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. 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). 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.