期刊
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
卷 9, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fbioe.2021.722202
关键词
soft sensor; online prediction; bioprocess; multiphase process; data synchronization; sensor fault; fault tolerance
资金
- German Federal Ministry of Education and Research [031B0475E]
- German Research Foundation [BE 2245/17-1]
- Open Access Publishing Fund of the Technical University of Munich (TUM)
This article discusses the challenges in soft sensor development, including variable process lengths, multiple process phases, and sensor faults leading to erroneous model inputs. The corresponding solution approaches are presented, including data synchronization techniques, phase detection, and sensor fault detection.
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.
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