Ensemble-based adaptive soft sensor for fault-tolerant biomass monitoring.

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 bioprocess.

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

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.

Biomass soft sensor for a Pichia pastoris fed-batch process based on phase detection and hybrid modeling.

A common control strategy for the production of recombinant proteins in Pichia pastoris using the alcohol oxidase 1 (AOX1) promotor is to separate the bioprocess into two main phases: biomass generation on glycerol and protein production via methanol induction. This study reports the establishment of a soft sensor for the prediction of biomass concentration that adapts automatically to these distinct phases. A hybrid approach combining mechanistic (carbon balance) and data-driven modeling (multiple linear regression) is used for this purpose.

Online sensor validation in sensor networks for bioprocess monitoring using swarm intelligence.

Sensor faults can impede the functionality of monitoring and control systems for bioprocesses. Hence, suitable systems need to be developed to validate the sensor readings prior to their use in monitoring and control systems. This study presents a novel approach for online validation of sensor readings.

Biomass estimation in Pichia pastoris cultures by combined single-wavelength fluorescence measurements.

In this work, the evolution of different biogenic fluorophores involved in the metabolism of Pichia pastoris was determined at four different single-wavelength pairs (excitation/emission) during batch culture in microwell plates and used for effective and reliable biomass estimation by means of chemometric tools. The chemometric tools for biomass estimation were multiple linear regression (MLR), partial least squares regression (PLSR), and principal component regression (PCR). Variable importance in the projection (VIP) scores were used to rate the importance of model input variables, indicating tryptophan as the most important variable for biomass estimation.