Kinetic parameters and reaction mechanism study of biomass pyrolysis by combined kinetics coupled with a heuristic optimization algorithm. (15th February 2023)

• Record Type: Journal Article
• Authors: Zhong, Yu; Ding, Yanming; Lu, Kaihua; Mao, Shaohua; Li, Changhai
• Is Part Of: Fuel. Volume 334(2023)Part 1
• Publication Date: 2023-02-15
• Subjects: Biomass pyrolysis -- Kinetics -- Combined kinetic method -- Particle Swarm Optimization -- Reaction mechanism; Fuel -- Periodicals; Coal -- Periodicals; Coal; Fuel; Periodicals; 662.6
• DOI: 10.1016/j.fuel.2022.126622
• Languages: English
• ISSNs: 0016-2361

Abstract

Highlights: The combined kinetic method is coupled with Particle Swarm Optimization. The three-parallel reaction model can describe beech pyrolysis well. Cellulose corresponds to Power law reaction mechanism. Both hemicellulose and lignin correspond to Order-based reaction mechanism. The application of the established mechanism is validated for wider heating rates. Abstract: Kinetic reaction mechanism plays a key role in accurately predicting biomass pyrolysis behavior. However, the order-based reaction mechanism is usually defined as the default in the existing kinetic studies. For the purpose of exploring the exact kinetic reaction mechanism of biomass pyrolysis, the combined kinetic method is tried here by coupling two common kinetic models (one-step global reaction and three-parallel reaction). The decomposition rate expressed by the method is related to three key parameters, whose values are corresponding to the establishment of reaction mechanism. The combined kinetic method is further coupled with a heuristic optimization algorithm to obtain more exact kinetic parameters in order to explore the reaction mechanism. The results show that it is hard to infer the reaction mechanism based on one-step global reaction, while based on three-parallel reaction, the respective reaction mechanism of each component can be established as Power law for cellulose, Order-based for hemicellulose and lignin by comparing with the thermogravimetric data. More importantly, the established mechanism and optimized parameters can be used to accurately predict the micro-scale pyrolysis behavior of biomass (beech) at a wider range of heating rates.