Challenges in the Development of Soft Sensors for Bioprocesses: A Critical Review

Vincent Brunner, Manuel Siegl, Dominik Geier, Thomas Becker

Research output: Contribution to journalReview articlepeer-review

46 Scopus citations

Abstract

Among the greatest challenges in soft sensor development for bioprocesses are variable process lengths, multiple process phases, and erroneous model inputs due to sensor faults. This review article describes these three challenges and critically discusses the corresponding solution approaches from a data scientist’s perspective. This main part of the article is preceded by an overview of the status quo in the development and application of soft sensors. The scope of this article is mainly the upstream part of bioprocesses, although the solution approaches are in most cases also applicable to the downstream part. Variable process lengths are accounted for by data synchronization techniques such as indicator variables, curve registration, and dynamic time warping. Multiple process phases are partitioned by trajectory or correlation-based phase detection, enabling phase-adaptive modeling. Sensor faults are detected by symptom signals, pattern recognition, or by changing contributions of the corresponding sensor to a process model. According to the current state of the literature, tolerance to sensor faults remains the greatest challenge in soft sensor development, especially in the presence of variable process lengths and multiple process phases.

Original languageEnglish
Article number722202
JournalFrontiers in Bioengineering and Biotechnology
Volume9
DOIs
StatePublished - 20 Aug 2021

Keywords

  • bioprocess
  • data synchronization
  • fault tolerance
  • multiphase process
  • online prediction
  • sensor fault
  • soft sensor

Fingerprint

Dive into the research topics of 'Challenges in the Development of Soft Sensors for Bioprocesses: A Critical Review'. Together they form a unique fingerprint.

Cite this