Catalytic upgrading of 4-methylaniosle as a representative of lignin-derived pyrolysis bio-oil: Process evaluation and optimization via coupled application of design of experiment and artificial neural networks

Catalytic upgrading of 4-methylanisole as a representative of lignin-derived pyrolysis biooil was investigated over Pt/g-Al2O3 catalyst. The catalytic upgrading process was conducted at different operating condition to determine the detailed reactions network. Additionally, artificial neural network and design of experiment were applied by feeding the reaction temperature, operating pressure and space velocity to predict 4-methylanisole conversion, main products selectivity, reactions rate and reactions network. The main products of 4-methylanisole upgrading were toluene, phenol derivatives, cyclohexanone, 4-methylcyclohexanone, and 2-tert-butyl-4-methylphenol. The major classes of reactions during the upgrading process were hydrogenolysis, hydrodeoxygenation, alkylation, and hydrogenation. For optimization of experimental data obtained at suggested conditions by design of experiment, the response surface methodology was applied. Artificial neural network model was used to investigate the kinetics behavior of the system due to the complex nature of system. A combination of the response surface methodology, artificial neural network, and design of experiment has revealed its ability to solve a quadratic polynomial model. The coefficients of determination were close to 1, and the mean square error of the artificial neural network model was close to 0 which showed the high accuracy of model predictions. It was inferred that during the upgrading process of 4-methylanisole, increasing temperature and pressure and setting space velocity at the minimum value are the reasons to come close to the optimum reaction rate. The comparison of experimental results with simulated data from the artificial neural network and the response surface methodology models illustrated that the developed model can create an applicable situation for practical design of large-scale production of valuable fuels from renewable resources. (C) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Automated summary

Catalytic upgrading of 4-methylanisole using Pt/g-Al2O3 catalyst was studied, showing major products like toluene, phenol derivatives, and cyclohexanone; artificial neural network and design of experiment were utilized to predict conversion rate, product selectivity, and reactions network; optimization of experimental data through response surface methodology and artificial neural network model demonstrated high prediction accuracy and potential for large-scale production of fuels from renewable resources.