Comparative Analysis of a Family of Sliding Mode Observers under Real-Time Conditions for the Monitoring in the Bioethanol Production

Online monitoring of fermentation processes is a necessary task to determine concentrations of key biochemical compounds, diagnose faults in process operations, and implement feedback controllers. However, obtaining the signals of all-important variables in a real process is a task that may be diffi...

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Bibliographic Details
Published in:Fermentation (Basel) 2022-09, Vol.8 (9), p.446
Main Authors: Alvarado-Santos, Eduardo, Mata-Machuca, Juan L., López-Pérez, Pablo A., Garrido-Moctezuma, Rubén A., Pérez-Guevara, Fermín, Aguilar-López, Ricardo
Format: Article
Language:English
Subjects:
Algorithms
batch bioreactor
Biofuels
Biomass
Bioreactors
Carbon dioxide
Comparative analysis
Embedded systems
Ethanol
ethanol fermentation
Fermentation
Glucose
Methods
Optimization
real-time implementation
Sensors
Simulation
sliding modes
state observers
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Summary:Online monitoring of fermentation processes is a necessary task to determine concentrations of key biochemical compounds, diagnose faults in process operations, and implement feedback controllers. However, obtaining the signals of all-important variables in a real process is a task that may be difficult and expensive due to the lack of adequate sensors, or simply because some variables cannot be directly measured. From the above, a model-based approach such as state observers may be a viable alternative to solve the estimation problem. This work shows a comparative analysis of the real-time performance of a family of sliding-mode observers for reconstructing key variables in a batch bioreactor for fermentative ethanol production. These observers were selected for their robust performance under model uncertainties and finite-time estimation convergence. The selected sliding-mode observers were the first-order sliding mode observer, the proportional sliding mode observer, and the high-order sliding mode observer. For estimation purposes, a power law kinetic model for ethanol production by Saccharomyces cerevisiae was performed. A hybrid methodology allows the kinetic parameters to be adjusted, and an approach based on inference diagrams allows the observability of the model to be determined. The experimental results reported here show that the observers under analysis were robust to modeling errors and measurement noise. Moreover, the proportional sliding-mode observer was the algorithm that exhibited the best performance.
ISSN:2311-5637
2311-5637
DOI:10.3390/fermentation8090446