Kinetic modeling and neuro-fuzzy application in ethanol production

Emmanuel Zullo Godinho; Caetano Dartiere Zulian Fermino; Ricardo Marques Barreiros

doi:10.18011/bioeng.2025.v19.1264

Authors

Emmanuel Zullo Godinho Department of Exact Sciences, Sacred Heart University Center (UNISAGRADO), Bauru-SP, Brazil https://orcid.org/0000-0001-5281-6608
Caetano Dartiere Zulian Fermino Department of Exact Sciences, Sacred Heart University Center (UNISAGRADO), Bauru-SP, Brazil https://orcid.org/0000-0001-6762-6794
Ricardo Marques Barreiros Department of Forest Science, São Paulo State University (FCA UNESP), Botucatu-SP, Brazil

DOI:

https://doi.org/10.18011/bioeng.2025.v19.1264

Keywords:

Neuro-Fuzzy, Ethanol production, Kinetic modeling, Artificial neural networks, Fermentation optimization

Abstract

This study presents the application of kinetic modeling and Neuro-Fuzzy techniques in ethanol production. The research aims to optimize the fermentation process by employing the Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict ethanol yield under different conditions. Initially, sugarcane juice was used as a raw material and subjected to fractional distillation to obtain ethanol. The experimental data were analyzed using artificial neural networks and fuzzy logic to develop a predictive model. The ANFIS hybrid model demonstrated high accuracy in forecasting ethanol production, allowing for process optimization and cost reduction. Additionally, the kinetic analysis of fermentation provided insights into substrate consumption and ethanol yield efficiency. The results indicate that the Neuro-Fuzzy approach is a powerful tool for improving bioethanol production processes, enhancing both efficiency and sustainability.

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