A more accurate modeling for fed-batch ethanol fermentation with high cell density

Author(s)

Veloso, Ivan I. K. ; Rodrigues, Kaio C. S. ; Esperanca, Mateus N. ; Batista, Gustavo ; Cruz, Antonio J. G. ; Badino, Alberto C.

• Document type: Journal article
• Source: Biochemical Engineering Journal; v. 193, p. 9-pg., 2023-02-21.

Abstract

In fermentations with high cell density, the high volume occupied by the cells can impair the robustness of models. In this work, the cell volume was considered in the development of mass balances for fed-batch ethanol fermentations, which can be useful for industrial applications. Batch fermentation was modeled using a classical model, which ignores the volume occupied by the cells, and considering the volume occupied by the cells, with no significant difference observed between the estimated kinetic parameters. High cell density fed-batch fer-mentations were simulated and experimentally validated for different must substrate concentrations of 300 g.L- 1 and 375 g.L-1 (very high gravity fermentation) in bench-scale assays. Comparison using different statistical indexes showed that the models had equal performance in predicting the profiles of cells and ethanol. However, for prediction of the substrate concentration, the proposed model presented better indexes, indicating that the cell volume should be considered in the development of fed-batch ethanol fermentation modeling. This approach could be used as an important tool for the improvement of optimization routines and process control, especially for the application of very high gravity fed-batch ethanol fermentations on an industrial scale, which allows an increase of ethanol production around 20% compared to the conventional process. Besides, it can be used in techno-economic assessments providing more accurate results thereby bringing economic gains. (AU)

• FAPESP's process: 18/11405-5 - Extractive Ethanol Fermentation in Pneumatic Bioreactors with Removal by CO2 Stripping Integrated to Recovery Units
• Grantee: Alberto Colli Badino Junior
• Support Opportunities: Regular Research Grants