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

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/CTR) catalysts. These catalysts produced renewable biofuels with properties similar to diesel, particularly a high concentration of n-C17 alkanes. Moreover, our Ni/CTR catalyst produces n-C18 alkanes, but the generation of n-C18 alkanes typically requires more complex catalysts. Our procedure achieved 74.74% of n-C17 alkanes and 2.28% of n-C18 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-C17 and n-C18 alkanes obtained. ANN602020 was our best model for obtaining the peak response; it accurately forecasted the n-C17 and n-C18 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 R2 score for both alkanes was 0.97139.

Keywords

• renewable biofuels
• Ni/Tire Rubber Carbon
• hydrodeoxygenation
• artificial neural networks
• Box–Behnken