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. (16th February 2021)

• Record Type: Journal Article
• Authors: Saidi, Majid; Yousefi, Masha; Minbashi, Mehran; Ameri, Fatemeh Arab
• Is Part Of: International journal of hydrogen energy. Volume 46:Number 12(2021)
• Page Start: 8411
• Page End: 8430
• Publication Date: 2021-02-16
• Subjects: Catalytic upgrading -- 4-Methylanisole -- Design of experiment -- Artificial neural network -- Response surface methodology -- Optimization; Hydrogen as fuel -- Periodicals; Hydrogène (Combustible) -- Périodiques; Hydrogen as fuel; Periodicals; 665.81
• DOI: 10.1016/j.ijhydene.2020.12.031
• Languages: English
• ISSNs: 0360-3199

Abstract

Abstract: Catalytic upgrading of 4-methylanisole as a representative of lignin-derived pyrolysis bio-oil was investigated over Pt/γ-Al2 O3 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. Highlights: Upgrading of 4-methylanisole as a representative of bio-oil has been analyzed. Impact of operating condition on 4-methylanisole conversion has been considered. A coupled model of ANN and DOE has been developed to optimize the process. AAN has been applied to determine the reactions network. Developed model provides a situation for design of large-scale production of fuels.