A Systematic Approach for Soft Sensor Development

Bao Lin, Bodil Recke, Philippe Renaudat, Jørgen Knudsen, Sten Bay Jørgensen

Research output: Contribution to journalConference articleResearchpeer-review


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 is illustrated through applications to a cement kiln system for estimation of variables related to product quality, i.e., free lime, and to emission quality, i.e., nitrogen oxides (NOx) emission. The case studies reveal the effectiveness of the systematic framework in deriving data-driven soft sensors that provide reasonably reliable one-step-ahead predictions.
Original languageEnglish
JournalComputers and Chemical Engineering
Issue number5
Pages (from-to)419-425
Publication statusPublished - 2007
Event15th European Symposium on Computer Aided Process Engineering - Barcelona, Spain
Duration: 29 May 20051 Jun 2005
Conference number: 15


Conference15th European Symposium on Computer Aided Process Engineering
Internet address


  • multivariate regression analysis
  • soft sensing
  • robust statistics


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