Ensemble‐based adaptive soft sensor for fault‐tolerant biomass monitoring. Issue 3 (8th January 2022)

• Record Type: Journal Article
• Authors: Siegl, Manuel; Brunner, Vincent; Geier, Dominik; Becker, Thomas
• Other Names: Beutel Sascha guestEditor.
• Is Part Of: Engineering in life sciences. Volume 22:Issue 3/4(2022)
• Page Start: 229
• Page End: 241
• Publication Date: 2022-01-08
• Subjects: adaptive modeling -- biomass prediction -- ensemble‐based method -- fault tolerance -- soft sensor; Bioengineering -- Periodicals; 660.605
• DOI: 10.1002/elsc.202100091
• Languages: English
• ISSNs: 1618-0240

Abstract

Abstract: The accuracy and precision of soft sensors depend strongly on the reliability of underlying model inputs. These inputs (particularly readings of hardware sensors) are frequently subject to faults. This study aims to develop an adaptive soft sensor capable of reliable and robust biomass concentration predictions in the presence of faulty model inputs for a Pichia pastoris bioprocess. Hence, three soft sensor submodels were developed based on three independent model inputs (base addition, CO2 production, and mid‐infrared spectrum). An ensemble‐based algorithm combined the submodels to form an ensemble model, that is, an adaptive soft sensor, to achieve fault‐tolerant prediction. The algorithm's basic steps are as follows: the initial determination of submodel reliability is followed by selecting appropriate submodels to generate a reliable prediction via variance‐based weighting of the submodels. The adaptive soft sensor demonstrated high robustness and accuracy in biomass prediction in the presence of multiple simulated sensor faults (RMSE = 0.43 g L −1 ) and multiple real sensor faults (RMSE = 0.70 g L −1 ).