Model-based calibration of a gas sensor array for on-line monitoring of ethanol concentration in Saccharomyces cerevisiae batch cultivation

Yousefi-Darani, Abdolrahim; Babor, Majharulislam; Emire, Shimelis Admassu; Hitzmann, Bernd

doi:10.1016/j.biosystemseng.2020.08.004

Cited by 22 publications

(11 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering similar chamber designs [16] , [17] , [18] , the following design was developed to ensure that the CO 2 readings from the exhaust line accurately reflect conditions inside the bioreactor. The Arduino controller turns on the ventilation once an hour, adding about 120 ml of air (which is about 4x the headspace volume) to the headspace of the bioreactor.…”

Section: Methodsmentioning

confidence: 99%

Using CO2 level monitoring to adjust the stress conditions of morbidostats

Bora

2024

Biotechnology Reports

View full text Add to dashboard Cite

Section: Methodsmentioning

confidence: 99%

Using CO2 level monitoring to adjust the stress conditions of morbidostats

Bora

2024

Biotechnology Reports

View full text Add to dashboard Cite

“…In this contribution, the yield coefficients as well as the Monod equation's constants ( K E and K G ) have been fixed to values which were chosen from literature [1, 20].…”

Section: Methodsmentioning

confidence: 99%

“…By preforming these steps, a peak shaped measurment signal is obtained. Ethanol concnetration is obtained from the raw signals by implementing signal processing methods and a chemometric model, which is described in detail in the literature [1]. Using the ethanol measurement in the gas phase, the ethanol concentration in the liquid phase of the cultivation broth is determined.…”

Section: Methodsmentioning

confidence: 99%

Parameter and state estimation of backers yeast cultivation with a gas sensor array and unscented Kalman filter

Yousefi-Darani

Emire

Hinrichs

et al. 2020

Engineering in Life Sciences

Self Cite

View full text Add to dashboard Cite

Real‐time information about the concentrations of substrates and biomass is the key to accurate monitoring and control of bioprocess. However, on‐line measurement of these variables is a challenging task and new measurement systems are still required. An alternative are software sensors, which can be used for state and parameter estimation in bioprocesses. The software sensors predict the state of the process by using mathematical models as well as data from measured variables. The Kalman filter is a type of such sensors. In this paper, we have used the Unscented Kalman Filter (UKF) which is a nonlinear extension of the Kalman filter for on‐line estimation of biomass, glucose and ethanol concentration as well as for estimating the growth rate parameters in S. cerevisiae batch cultivation, based on infrequent ethanol measurements. The UKF algorithm was validated on three different cultivations with variability of the substrate concentrations and the estimated values were compared to the off‐line values. The results obtained showed that the UKF algorithm provides satisfactory results with respect to estimation of concentrations of substrates and biomass as well as the growth rate parameters during the batch cultivation.

show abstract

“…Model calibration: it is the most practiced field in food processing optimization. Here, the parameters need to be searched for a nonlinear dynamic model that provide the best fit to the dataset derived experimentally ( Shoaib et al, 2021 ; Yousefi-Darani et al, 2020 ).…”

Section: Mathematical Models Used In the Food Industrymentioning

confidence: 99%