Discussion on Time Difference Models and Intervals of Time Difference for Application of Soft Sensors

In chemical plants, soft sensors are widely used to estimate process variables that are difficult to measure online. The predictive accuracy of soft sensors decreases over time because of changes in the state of chemical plants, and soft sensor models based on time difference (TD) have been constructed. However, many details of models based on TD remain to be clarified. In this study, TD models are discussed in terms of noise in data, autocorrelation in process variables, predictive accuracy, and so on. We theoretically clarify and formulate the differences of predictive accuracy between normal models and TD models and the effects of noise, autocorrelation, TD intervals, and so on on the predictive accuracy. The relationships and the formulas were verified by analyzing simulation data. Furthermore, we analyzed dynamic simulation data and real industrial data and confirmed that the predictive accuracy of TD models increased when TD intervals were optimized.

[1]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[2]  Hiromasa Kaneko,et al.  A new process variable and dynamics selection method based on a genetic algorithm‐based wavelength selection method , 2012 .

[3]  Daoping Huang,et al.  Development of Interval Soft Sensors Using Enhanced Just-in-Time Learning and Inductive Confidence Predictor , 2012 .

[4]  Hiromasa Kaneko,et al.  A soft sensor method based on values predicted from multiple intervals of time difference for improvement and estimation of prediction accuracy , 2011 .

[5]  Hiromasa Kaneko,et al.  Development of Soft Sensor Models Based on Time Difference of Process Variables with Accounting for Nonlinear Relationship , 2011 .

[6]  Hiromasa Kaneko,et al.  Maintenance-free soft sensor models with time difference of process variables , 2011 .

[7]  Hiromasa Kaneko,et al.  Applicability domains and accuracy of prediction of soft sensor models , 2011 .

[8]  Bogdan Gabrys,et al.  Local learning‐based adaptive soft sensor for catalyst activation prediction , 2011 .

[9]  Bogdan Gabrys,et al.  Review of adaptation mechanisms for data-driven soft sensors , 2011, Comput. Chem. Eng..

[10]  Manabu Kano,et al.  Soft‐sensor development using correlation‐based just‐in‐time modeling , 2009 .

[11]  Bogdan Gabrys,et al.  Data-driven Soft Sensors in the process industry , 2009, Comput. Chem. Eng..

[12]  Hiromasa Kaneko,et al.  Development of a new soft sensor method using independent component analysis and partial least squares , 2009 .

[13]  Manabu Kano,et al.  Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry , 2008, Comput. Chem. Eng..

[14]  M. Chiu,et al.  A new data-based methodology for nonlinear process modeling , 2004 .

[15]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[16]  S. Qin Recursive PLS algorithms for adaptive data modeling , 1998 .

[17]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[18]  J. D. Winefordner,et al.  A review and tutorial discussion of noise and signal-to-noise ratios in analytical spectrometry—I. Fundamental principles of signal-to-noise ratios , 1978 .

[19]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .