Biofuel Production in Oleic Acid Hydrodeoxygenation Utilizing a Ni/Tire Rubber Carbon Catalyst and Predicting of n-Alkanes with Box–Behnken and Artificial Neural Networks

Luis A. Sánchez-Olmos, Manuel Sánchez-Cárdenas (), Fernando Trejo, Martín Montes Rivera (), Ernesto Olvera-Gonzalez and Benito Alexis Hernández Guerrero
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Luis A. Sánchez-Olmos: CICATA-Legaria, Instituto Politécnico Nacional, Legaria 694, Col. Irrigación, Ciudad de México 11500, Mexico
Manuel Sánchez-Cárdenas: CICATA-Legaria, Instituto Politécnico Nacional, Legaria 694, Col. Irrigación, Ciudad de México 11500, Mexico
Fernando Trejo: CICATA-Legaria, Instituto Politécnico Nacional, Legaria 694, Col. Irrigación, Ciudad de México 11500, Mexico
Martín Montes Rivera: Dirección de Posgrados e Investigación, Universidad Politécnica de Aguascalientes, Calle Paseo San Gerardo 207, Aguascalientes 20342, Mexico
Ernesto Olvera-Gonzalez: Laboratorio de Iluminación Artificial, Tecnológico Nacional de México, IT de Pabellón de Arteaga, Carretera a la Estación de Rincón Km. 1, Aguascalientes 20670, Mexico
Benito Alexis Hernández Guerrero: CICATA-Legaria, Instituto Politécnico Nacional, Legaria 694, Col. Irrigación, Ciudad de México 11500, Mexico

Energies, 2024, vol. 17, issue 22, 1-27

Abstract: Oleic acid is a valuable molecule for biofuel production, as it is found in high proportions in vegetable oils. When used, oleic acid undergoes hydrodeoxygenation reactions and produces alkanes within the diesel range. These alkanes are free of oxygenated compounds and have molecular structures similar to petrodiesel. Our research introduces a novel approach incorporating oleic acid into the hydrodeoxygenation process of Ni/Tire Rubber Carbon (Ni/C TR ) catalysts. These catalysts produced renewable biofuels with properties similar to diesel, particularly a high concentration of n-C 17 alkanes. Moreover, our Ni/C TR catalyst produces n-C 18 alkanes, but the generation of n-C 18 alkanes typically requires more complex catalysts. Our procedure achieved 74.74% of n-C 17 alkanes and 2.28% of n-C 18 alkanes. We used Box–Behnken and artificial neural networks (ANNs) to find the optimal configuration based on the predicted data. We developed a dataset with pressure, temperature, metal content, reaction time, and catalyst composition variables as inputs. The output variables are the n-C 17 and n-C 18 alkanes obtained. ANN602020 was our best model for obtaining the peak response; it accurately forecasted the n-C 17 and n-C 18 generation with R2 scores of 0.9903 and 0.9525, respectively, resulting in an MSE of 0.0014, MAE of 0.02773, and MAPE of 2.03979%. The combined R 2 score for both alkanes was 0.97139.

Keywords: renewable biofuels; Ni/Tire Rubber Carbon; hydrodeoxygenation; artificial neural networks; Box–Behnken (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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