A systematic approach for soft sensor development

This paper presents a systematic approach based on robust statistical techniques for development of a data-driven soft sensor, which is an important component of the process analytical technology (PAT) and is essential for effective quality control. The data quality is obviously of essential significance for a data-driven soft sensor. Therefore, preprocessing procedures for process measurements are described in detail. First, a template is defined based on one or more key process variables to handle missing data related to severe operation interruptions. Second, a univariate, followed by a multivariate principal component analysis (PCA) approach, is used to detect outlying observations. Then, robust regression techniques are employed to derive an inferential model. A dynamic partial least squares (DPLS) model is implemented to address the issue of auto-correlation in process data and thus to provide smoother estimation than using a static regression model. The proposed methodology i...

• Type: Journal
• Year: 2007
• Where: CCE
• Authors: Bao Lin, Bodil Recke, Jørgen K. H. Knudsen, Sten Bay Jørgensen
• Keywords: CCE 2007, Data-driven Soft Sensors, Process Analytical Technology, Robust Statistical Techniques, Software Engineering