BATCH FERMENTATION PROCESS OF SORGHUM WORT MODELING BY ARTIFICIAL NEURAL NETWORK

  • Kouame Kan Benjamin University Jean Lorougnon Guede (UJLoG), Department of Biochemistry and Microbiology, Agroforestry Unit, Daloa, Côte d’Ivoire
  • Koko Anauma Casimir University Jean Lorougnon Guede (UJLoG), Department of Biochemistry and Microbiology, Agroforestry Unit, Daloa, Côte d’Ivoire
  • Diomande Masse University Jean Lorougnon Guede (UJLoG), Department of Biochemistry and Microbiology, Agroforestry Unit, Daloa, Côte d’Ivoire
  • Assidjo Nogbou Emmanuel Felix Houphouet-Boigny National Polytechnic Institute (INPHB), Laboratory of Industrial Process Synthesis and Environment, Côte d’Ivoire

Abstract

The production of tchapalo (traditional beer) remains uncontrolled and artisanal. For the improvement of the product quality, we need to know more about the traditional process and beer characteristics. The fermentation process is one of the most critical steps, which determines the quality of the beer. In this study, artificial neural network, precisely multi layer perceptron was used for modeling batch fermentation process of sorghum wort. The artificial neural network showed its ability to predict the ph, temperature, substrate, biomass, carbon dioxide (CO2) and alcohol (ethanol) evolution during batch fermentation of sorghum wort. All the correlation coefficients between the observed and predicted values for the artificial neural network were higher than 0.96. Thus, artificial neural network can be used to determine fermentation deviations during production of tchapalo and also to monitor and improve its quality.

Downloads

Download data is not yet available.
Published
2015-01-30
How to Cite
Benjamin, K. K., Casimir, K. A., Masse, D., & Emmanuel, A. N. (2015). BATCH FERMENTATION PROCESS OF SORGHUM WORT MODELING BY ARTIFICIAL NEURAL NETWORK. European Scientific Journal, ESJ, 11(3). Retrieved from https://eujournal.org/index.php/esj/article/view/4990