Input estimation as a qualitative trend analysis problem

Christian M. Thürlimann, Kris Villez

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

The study of techniques for qualitative trend analysis (QTA) has been a popular approach to address challenges in fault diagnosis of engineered processes. Such challenges include the lack of reliable extrapolation of available models and lack of representative data describing previously unseen circumstances. Many of these challenges appear in biological systems even when normal operation can be assumed. It is for this reason that QTA techniques have also been proposed for the purpose of fault detection, automation, and dynamic modeling. In this work, we adopt a shape-constrained spline function method for the purpose of unknown input estimation. Thanks to data collected at laboratory-scale in a biological reactor for urine nitrification, this novel approach has been demonstrated successfully for the first time.

Original languageEnglish
Pages (from-to)333-342
Number of pages10
JournalComputers and Chemical Engineering
Volume107
DOIs
StatePublished - Dec 5 2017
Externally publishedYes

Funding

The authors thank Dan Finkel for his Matlab implementation of the DIRECT algorithm (Finkel, 2003) and the authors of Villez et al. (2013), Villez and Habermacher (2016), Derlon et al. (2017) for the SCS toolbox. This study was financed by the Swiss National Science Foundation (SNSF, Project No.: 157097). The authors thank Dan Finkel for his Matlab implementation of the DIRECT algorithm ( Finkel, 2003 ) and the authors of Villez et al. (2013) , Villez and Habermacher (2016) , Derlon et al. (2017) for the SCS toolbox. This study was financed by the Swiss National Science Foundation (SNSF, Project No.: 157097 ).

Keywords

  • Global optimization
  • Input estimation
  • Oxygen uptake rate
  • Qualitative trend analysis
  • Wastewater treatment

Fingerprint

Dive into the research topics of 'Input estimation as a qualitative trend analysis problem'. Together they form a unique fingerprint.

Cite this