Modeling for soft sensor systems and parameters updating online

Journal of Process Control - Tập 24 - Trang 975-990 - 2014
Pengfei Cao1, Xionglin Luo1
1Research Institute of Automation, China University of Petroleum, Beijing 102249, China

Tài liệu tham khảo

Zhong, 2000, MIMO soft sensors for estimating product quality with online correction, Trans. IChemE, 78, 612, 10.1205/026387600527554 Kadlec, 2009, Data-driven soft sensors in the process industry, Comput. Chem. Eng., 33, 795, 10.1016/j.compchemeng.2008.12.012 Hui, 2004, RBF-ARX model-based nonlinear system modeling and predictive control with application to a NOx decomposition process, Control Eng. Pract., 12, 191, 10.1016/S0967-0661(03)00050-9 Dae, 2006, Nonlinear dynamic partial least squares modeling of a full-scale biological wastewater treatment plant, Process Biochem., 41, 2050, 10.1016/j.procbio.2006.05.006 Cao, 2013, Modeling of soft sensor for chemical process, J. Chem. Ind. Eng., 64, 788 Hector, 2011, A reduced order soft sensor approach and its application to a continuous digester, J. Process Control, 21, 489, 10.1016/j.jprocont.2011.02.001 Ma, 2005, Discuss about dynamic soft-sensing modeling, J. Chem. Ind. Eng., 56, 1516 Ania, 2003, A nonlinear model predictive control system based on wiener piecewise linear models, J. Process Control, 13, 655, 10.1016/S0959-1524(02)00121-X Martin, 2008, Identification of wiener models using optimal local linear models, Simulat. Model. Pract. Theory, 16, 1055, 10.1016/j.simpat.2008.05.012 Boyd, 1985, Fading memory and the problem of approximating nonlinear operators with volterra series, IEEE Trans. Circ. Syst., 32, 1150, 10.1109/TCS.1985.1085649 Stefan, 2009, Support vector method for identification of wiener models, J. Process Control, 19, 1174, 10.1016/j.jprocont.2009.03.003 Ferreira, 2012, Local convergence analysis of inexact Gauss–Newton like methods under majorant condition, J. Comput. Appl. Math., 236, 2487, 10.1016/j.cam.2011.12.008 Richard, 1996, Gauss–Newton and M-estimation for ARMA processes with infinite variance, Stochast. Process. Appl., 63, 75, 10.1016/0304-4149(96)00063-4 Ding, 2005, Hierarchical gradient-based identification of multivariable discrete-time systems, Automatica, 41, 315, 10.1016/j.automatica.2004.10.010 Ding, 2006, Convergence analysis of estimation algorithms for dual-rate stochastic systems, Appl. Math. Comput., 176, 245, 10.1016/j.amc.2005.09.048 Eykhoff, 1974 Tsidu, 2005, On the accuracy of covariance matrix: Hessian versus Gauss–Newton methods in atmospheric remote sensing with infrared spectroscopy, J. Quant. Spectrosc. Radiat. Transfer, 96, 103, 10.1016/j.jqsrt.2004.11.014 Tarvainen, 2008, Gauss–Newton reconstruction method for optical tomography using the finite element solution of the radiative transfer equation, J. Quant. Spectrosc. Radiat. Transfer, 109, 2767, 10.1016/j.jqsrt.2008.08.006 Ljung, 1983 Solo, 1990, Stochastic adaptive control and martingale limit theory, IEEE Trans. Autom. Control, 35, 66, 10.1109/9.45146 Tian, 2010, Development of a novel soft sensor using a local model network with an adaptive subtractive clustering approach, Ind. Eng. Chem. Res., 49, 4738, 10.1021/ie901098w Dai, 2006, Assumed inherent sensor inversion based ANN dynamic soft-sensing method and its application in erythromycin fermentation process, Comput. Chem. Eng., 30, 1203, 10.1016/j.compchemeng.2006.02.001 Elom, 2011, A decoupled multiple model approach for soft sensors design, Control Eng. Pract., 19, 126, 10.1016/j.conengprac.2010.10.006 Strejc, 1980, Least squares parameter estimation, Automatica, 16, 535, 10.1016/0005-1098(80)90077-1 Qin, 1996 Principe, 2000 Shang, 2013, Novel Bayesian framework for dynamic soft sensor based on support vector machine with finite impulse response, IEEE Trans. Control Syst. Technol., 10.1109/TCST.2013.2278412 Wu, 2010, A novel calibration approach of soft sensor based on multirate data fusion technology, J. Process Control, 20, 1252, 10.1016/j.jprocont.2010.09.003 Fu, 2007, MIMO soft-sensor model of nutrient for compound fertilizer based on hybrid modeling technique, J. Chem. Ind. Eng., 15, 554 Luo, 2005 Ding, 1997, Martingale hyperconvergence theorem and the convergence of forgetting factor least squares algorithm, Control Theory Appl., 14, 90 Liu, 2013, Convergence properties of the least squares estimation algorithm for multivariable systems, Appl. Math. Model., 37, 476, 10.1016/j.apm.2012.03.007 Peng, 2002, Structured parameter optimization method for the radial basis function-based state-dependent autoregressive model, J. Syst. Sci., 33, 1087, 10.1080/0020772021000059753 Gomez, 2004, Wiener model identification and predictive control of a pH neutralization process, IEE Proc. Control Theory Appl., 151, 329, 10.1049/ip-cta:20040438 Mahmoodi, 2009, Nonlinear model predictive control of a pH neutralization process based on Wiener-Laguerre model, Chem. Eng. J., 146, 328, 10.1016/j.cej.2008.06.010