On-line identification of fermentation processes for ethanol production

A strategy for monitoring fermentation processes, specifically, simultaneous saccharification and fermentation (SSF) of corn mash, was developed. The strategy covered the development and use of first principles, semimechanistic and unstructured process model based on major kinetic phenomena, along w...

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Bibliographic Details
Published in:Bioprocess and biosystems engineering 2017-07, Vol.40 (7), p.989-1006
Main Authors: Câmara, M. M., Soares, R. M., Feital, T., Naomi, P., Oki, S., Thevelein, J. M., Amaral, M., Pinto, J. C.
Format: Article
Language:English
Subjects:
Bioengineering
Biomass
Biotechnology
Carbohydrates
Chemistry
Chemistry and Materials Science
Corn
Decision making
Energy
Energy balance
Environmental Engineering/Biotechnology
Ethanol
Fermentation
Food Science
Indicators
Industrial and Production Engineering
Industrial Chemistry/Chemical Engineering
Mathematical models
Monitoring
Optimization
Parameter estimation
Real time
Research Paper
Running
Saccharification
Saccharomyces cerevisiae
Sugar
Temperature effects
Zea mays
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Summary:A strategy for monitoring fermentation processes, specifically, simultaneous saccharification and fermentation (SSF) of corn mash, was developed. The strategy covered the development and use of first principles, semimechanistic and unstructured process model based on major kinetic phenomena, along with mass and energy balances. The model was then used as a reference model within an identification procedure capable of running on-line. The on-line identification procedure consists on updating the reference model through the estimation of corrective parameters for certain reaction rates using the most recent process measurements. The strategy makes use of standard laboratory measurements for sugars quantification and in situ temperature and liquid level data. The model, along with the on-line identification procedure, has been tested against real industrial data and have been able to accurately predict the main variables of operational interest, i.e., state variables and its dynamics, and key process indicators. The results demonstrate that the strategy is capable of monitoring, in real time, this complex industrial biomass fermentation. This new tool provides a great support for decision-making and opens a new range of opportunities for industrial optimization.
ISSN:1615-7591
1615-7605
DOI:10.1007/s00449-017-1762-6