Abstract
Soft sensors have been widely used in chemical processes to predict values of difficult-to-measure process variables online. If the relationship between explanatory variables X and an objective variable y is changed by catalyst deterioration, change of product and so on, prediction accuracy of a soft sensor is reduced. This problem is called degradation of a soft sensor model. To overcome the degradation, many adaptive soft sensors have been proposed. In this paper, we aim to improve prediction accuracy of just-in-time (JIT) models. JIT models are constructed with only data close to a query or with all data having weights according to similarity with a query. If the type of degradation is shift of y-value, prediction accuracy of JIT models is reduced since data with similar X-values but different y-values are mixed in database and the relationship between X and y is not consistent. To resolve this problem, we propose a management method of database, called JIT database. JIT database were constructed only the latest data in all areas of X. By constructing JIT model from JIT database, improvement of prediction accuracy was achieved for simulation data.
