ROBUST STATISTICS FOR SOFT SENSOR DEVELOPMENT IN CEMENT KILN?

摘要

This paper presents a systematic approach of developing data-driven soft sensor using robust statistical technique. Data preprocessing procedures are described in detail. First, a template defined with a key process variable is used to handle missing data. Second, a univariate, followed by a multivariate approach, principal component analysis (PCA), is used to detecting outlying observations. Then, regression technique is 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.