Classification of the Degradation of Soft Sensor Models and Discussion on Adaptive Models

Soft sensors are used widely to estimate a process variable which is difficult to measure online. One of the crucial difficulties of soft sensors is that predictive accuracy drops due to changes of state of chemical plants. It is called as the degradation of soft sensor models. In this study, we attempted to classify this degradation of models in terms of changes in an explanatory variable and an objective variable, and the rapidity of the changes. Moreover, we discussed characteristics of adaptive soft sensor models, based on the classification results. By analyzing simulated data sets, we could obtain knowledge and information on appropriate adaptive models for each type of the degradation. Keyword: Process control, Soft sensor, Degradation, Adaptive model, Predictive ability

[1]  Thomas F. Edgar,et al.  Identification of faulty sensors using principal component analysis , 1996 .

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

[3]  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 .

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

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

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

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

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

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

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

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

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

[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..