Kinetic-compartmental modelling of potassium-containing cellulose feedstock gasification

作     者：Attila Egedy Livia Gyurik Tamas Varga Jun Zou Norbert Miskolczi Haiping Yang 

作者机构：Institute of Chemical and Process EngineeringUniversity of PannoniaVeszprem 8200Hungary State Key Laboratory of Coal CombustionHuazhong University of Science and TechnolgyWuhan 430074China 

出 版 物：《Frontiers of Chemical Science and Engineering》 (化学科学与工程前沿（英文版）)

年 卷 期：2018年第12卷第4期

页      面：708-717页

核心收录：

学科分类：0817[工学-化学工程与技术] 08[工学] 

基　　金：the Horizon 2020, Marie Curie Research and Innovation Staff Exchange (RISE) (MSCA-RISE-2014 (Flexi-pyrocat) supported by Smart Specialization Strategy (S3) –Comprehensive Institutional Development Program at the University of Pannonia to Promote Sensible Individual Education and Career Choices project 

主　　题：biomass pyrolysis kinetic parameter identification compartment modelling optimisation 

摘      要：Biomass is of growing interest as a secondary energy source and can be converted to fuels with higher energy density especially by pyrolysis or gasification. Understanding the mechanism and the kinetics of biomass pyrolysis (thermal decomposition) and gasification (conversion of organic material to gases) could be the key to the design of industrial devices capable of processing vast amounts of biomass feedstock. In our work real product components obtained in pyrolysis were took into consideration as well as char and oil as lumped components, and the kinetic constants for a biomass model compound (cellulose) pyrolysis and gasification were identified based on a proposed simplified reaction mechanism within a compartment model structure. A laboratory scale reactor was used for the physical experiments containing consecutive fast pyrolysis and gasification stages using alkali metal (K) containing feedstock, which has a significant effect on the cellulose pyrolysis and gasification. The detailed model was implemented in MATLAB/Simulink environment, and the unknown kinetic parameters were identified based on experimental data. The model was validated based on measurement data, and a good agreement was found. Based on the validated first principle model the optimal parameters were determined as 0.15 mL/min steam flow rate, and 4% K content.