Bibliographic citations
Basauri, G., Velarde, M. (2024). Machine learning techniques applied to the production of bioethanol from the characterization of lignocellulosic biomass (Theobroma cacao l.) [Universidad de Lima]. https://hdl.handle.net/20.500.12724/20743
Basauri, G., Velarde, M. Machine learning techniques applied to the production of bioethanol from the characterization of lignocellulosic biomass (Theobroma cacao l.) []. PE: Universidad de Lima; 2024. https://hdl.handle.net/20.500.12724/20743
@misc{renati/1434964,
title = "Machine learning techniques applied to the production of bioethanol from the characterization of lignocellulosic biomass (Theobroma cacao l.)",
author = "Velarde Herrera, Melanie Evelyn",
publisher = "Universidad de Lima",
year = "2024"
}
In recent years, the demand for fossil fuels has been increasing and this has generated a shortage in world reserves, which limits economic growth; An example of this is Tocache, one of the most isolated and poorest provinces in Peru. This study deals with the use of lignocellulosic residues such as cocoa husks to generate biofuel, whose objective is to compare the experimental results with those obtained from the simulation. Furthermore, the pretreatment, enzymatic hydrolysis and fermentation processes were carried out at the Technical University of Machala. The Machine Learning was carried out with the orange software, which was based on the data and the experimental variables previously found. The best result corresponds to the Random Forest method, with which a precision with R2 (0.83) was obtained. Therefore, the predicted glucose was 1.04 g/L, and the optimal amount of ethyl alcohol was 5.34 g/L. The results show that the simulated ethyl alcohol is close to that found experimentally (7.1 g/L) and to previous studies. Finally, the use of Machine Learning is less expensive, and the results can be obtained in the shortest possible time compared to experimental procedures.
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