A Geometric Observer-Assisted Approach to Tailor State Estimation in a Bioreactor for Ethanol Production

Lisci, Silvia; Grosso, Massimiliano; Tronci, Stefania

doi:10.3390/pr8040480

Cited by 12 publications

(9 citation statements)

References 23 publications

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“…State estimators such as Kalman filters are often considered a more robust approach than RPE because they merge process data with the model predictions without altering model parameters. Numerous examples of different state estimating algorithms applied to fermentation processes are described in the literature [14,[25][26][27][28][29][30]. Successful implementation of state estimation at bench scale (15 L) was developed by Krämer et al [14,29] using an extended Kalman filter (EKF) [14] and a sigma point Kalman filter (SPKF) [29].…”

Section: Unidirectional Process Models In Fermentationmentioning

confidence: 99%

Digital Twin in biomanufacturing: challenges and opportunities towards its implementation

Udugama

López

Gargalo

et al. 2021

Syst Microbiol and Biomanuf

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The domain of industrial biomanufacturing is enthusiastically embracing the concept of Digital Twin, owing to its promises of increased process efficiency and resource utilisation. However, Digital Twin in biomanufacturing is not yet clearly defined and this sector of the industry is falling behind the others in terms of its implementation. On the other hand, some of the benefits of Digital Twin seem to overlap with the more established practices of process control and optimization, and the term is vaguely used in different scenarios. In an attempt to clarify this issue, we investigate this overlap for the specific case of fermentation operation, a central step in many biomanufacturing processes. Based on this investigation, a framework built upon a five-step pathway starting from a basic steady-state process model is proposed to develop a fully-fledged Digital Twin. For demonstration purposes, the framework is applied to a bench-scale second-generation ethanol fermentation process as a case study. It is proposed that the success or failure of a fully-fledged Digital Twin implementation is determined by key factors that comprise the role of modelling, human operator actions, and other propositions of economic value.

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Section: Unidirectional Process Models In Fermentationmentioning

confidence: 99%

Digital Twin in biomanufacturing: challenges and opportunities towards its implementation

Udugama

López

Gargalo

et al. 2021

Syst Microbiol and Biomanuf

View full text Add to dashboard Cite

show abstract

“…They mostly differ in the way the approximation of the prediction uncertainty is performed. Lisci and Tronci [4] applied an extended Kalman filter (EKF) to predict the state of a fed-batch cultivation of baker's yeast. The variables of interest were temperature, dissolved oxygen amount, and the substrate concentration.…”

Section: Introductionmentioning

confidence: 99%

Online State Prediction of S. cerevisiae Cultivation Purely Based on Ethanol Gas Sensors and an Observer

Emire¹,

Yousefi-Darani²,

Hitzmann³

2022

The 1st International Electronic Conference on Processes: Processes System Innovation

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“…To reduce the errors in measurements and to estimate non-measurable process variables, a Kalman filter can be applied. Although there are several applications of the Kalman filter for fermentation processes [14][15][16][17][18][19][20], its application is still rare in the food area. Recently, Pongsuttiyakorn et al [21] used weight sensors, which are inherently contaminated by noise, and complemented them with a Kalman filter to correct the real-time measured weight under various temperatures to determine the moisture content during the food drying process.…”

Section: Introductionmentioning

confidence: 99%

The Supervision of Dough Fermentation Using Image Analysis Complemented by a Continuous Discrete Extended Kalman Filter

et al. 2020

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Dough fermentation is an important step during the preparation of fermented baking goods. For the supervision of dough fermentation, a continuous-discrete extended Kalman filter was applied, which uses an image analysis system as the measurement. By estimation a fixed number of gas bubbles inside the dough, the radius of an average bubble was determined. A mathematical dough model was used by the extended Kalman filter to estimate the radius of the average bubble, the CO2 concentration of the non-gas dough phase and the number of CO2 molecules in the average bubble. During a fermentation of 50 min, the extended Kalman filter estimated that the average radius increased from 50 µm to 127 µm, the CO2 concentration in the non-gas dough increased to 23 mol/m³, and the CO2 amount in the bubble increased from 0.1 × 10−10 to 4 × 10−10 mol. Also, the specific CO2 production rate was estimated to be in the range from 1.5 × 10−3 to more than 4 × 10−3 mol·m³/kg/s. The advantages of an extended Kalman filter for the supervision of the dough fermentation process are discussed.

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A Geometric Observer-Assisted Approach to Tailor State Estimation in a Bioreactor for Ethanol Production

Cited by 12 publications

References 23 publications

Digital Twin in biomanufacturing: challenges and opportunities towards its implementation

Digital Twin in biomanufacturing: challenges and opportunities towards its implementation

Online State Prediction of S. cerevisiae Cultivation Purely Based on Ethanol Gas Sensors and an Observer

The Supervision of Dough Fermentation Using Image Analysis Complemented by a Continuous Discrete Extended Kalman Filter

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