A systematic approach for soft sensor development

This paper presents a systematic approach for development of a data-driven soft sensor using robust statistical technique. Data preprocessing procedures are described in detail. First, a template is defined based on a key process variable to handle missing data related to severe operation interruption. Second, a univariate, followed by a multivariate principal component analysis (PCA) approach, is used to detecting outlying observations. Then, robust regression techniques are employed to derive an inferential model. The proposed methodology is applied to a cement kiln system for realtime estimation of free lime, demonstrating improved performance over a standard multivariate approach.