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 and a real industrial data set, we could obtain knowledge and information on appropriate adaptive models for each type of the degradation. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2339–2347, 2013
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