Data-driven analysis of sequencing batch reactors: Multivariate and qualitative data analysis of a wastewater treatment process - Couverture souple

Villez, Kris; A. Vanrolleghem, Peter; Rosen, Christian

 
9783838395456: Data-driven analysis of sequencing batch reactors: Multivariate and qualitative data analysis of a wastewater treatment process

Synopsis

This book deals with the development, application and validation of techniques for data analysis in view of supervisory control of cyclic systems, including and integrating aspects of monitoring, diagnosis and control. Two so far largely separated tools for data mining of process data are used as a basis for the presented developments. These are Principal Component Analysis (PCA) and Qualitative Representation of Trends (QRT). A pilot-scale sequencing batch reactor (SBR) for wastewater treatment is used as a case study for the major parts of the work presented. Another application is pursued regarding the analysis of flow measurement time series derived from an urban drinking water network.

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Présentation de l'éditeur

This book deals with the development, application and validation of techniques for data analysis in view of supervisory control of cyclic systems, including and integrating aspects of monitoring, diagnosis and control. Two so far largely separated tools for data mining of process data are used as a basis for the presented developments. These are Principal Component Analysis (PCA) and Qualitative Representation of Trends (QRT). A pilot-scale sequencing batch reactor (SBR) for wastewater treatment is used as a case study for the major parts of the work presented. Another application is pursued regarding the analysis of flow measurement time series derived from an urban drinking water network.

Biographie de l'auteur

Kris Villez obtained his PhD in bio-engineering at Ghent University, Ghent. He is currently a research associate at the Department of Chemical Engineering at Purdue University where he conducts research on resilient control. His research interests include the development of methods for statistical process inference, control and engineering.

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