Kinetic Mechanism Study Of Co-pyrolysis Of Biomass, Waste Cooking Oil And Gasification Tar And Artificial Neural Network Modeling

In order to investigate the co-pyrolysis behavior of biomass, waste cooking oiland tar derived from biomass gasification, this thesis focuses on the mechanism andkinetic analysis via thermal gravity （TG） analysis and differential scanningcalorimeter （DSC） analysis whilst extra co-pyrolysis behavior is predicted by buildingup the BP artificial neural network. In this study, three different heating rates arechosed and temperature increases from303K to973K by heating furnace inexperiment apparatus. Kinetic modeling of mass is acquired by different mathematicalmethodologies, so that the pyrolysis modeling and mechanisms are revealed underdifferent pyrolysis conditions, respectively. As a result, the optimal conditions,including heating rate, pyrolysis temperature, and the component ratio in feedstockcould be determined, which could be a both practical and accurate guide toindustrialized pyrolysis application. Finally, BP artificial neural network is built up topredict the thermal decomposition extent, activation energy and reaction order, etc.,according to the existed decomposition behavior and kinetic factors.There is a very strong synergistic relation happening during fast pyrolysis ofbiomass and main thermal decomposition period of biomass and waste cooking oil.However, the extent of thermal decomposition of biomass could reduce because ofinvolvement of waste cooking oil, and the synergistic extent could reach the negative40%maximally. By contrary, there is also an obvious synergistic effect during theco-pyrolysis of biomass and tar derived from biomass gasification. Since thedecomposition extent of tar is higher than that of biomass, and its temperature isrelatively lower, the decomposition extent of biomass increases after mixed with tar.Besides, synergistic ratio of co-pyrolysis fluctuates between positive40%and60%.Brifely, gasification tar promotes thermal decomposition rate of biomass.As for the co-pyrolysis behavior of biomass and waste cooking oil, the pyrolysismechanism is D₁（diffusion one-way transport）, D₁and R₁（one dimension limitingsurface reaction） pathway when heating rate is10K/min,30K/min and50K/min,respectively. As for co-pyrolysis behavior of biomass and gasification tar, thepyrolysis mechanism is D₃（diffusion three-way transport）, P-T₂（Prout-Tompkins, m=1） and P-T₁（Prout-Tompkins, m=0.5） pathway when heating rate is10K/min,30K/min and50K/min, respectively.The predicted values of co-pyrolysis behavior calculated via BP artificial neuralnetwork are remarkably closer to the experimental outcomes than simplesuperimposition outcomes, especially for the co-pyrolysis of biomass and gasificationtar, which could in turn, proves that there is a strong synergistic relationship betweenbiomass and gasification tar. Furthermore, there is little gap between the predictedvalue and experimental value of activation energy of co-pyrolysis. Specifically, themaximal relative error of predicted activation energy is7.5%while that of predictedreaction order is1%. Meanwhile, relative error of predicted reaction order is lowerthan that of activation energy.To sum up, according to the kinetic mechanism analysis, the co-pyrolysis ofbiomass and waste cooking oil, and gasification tar is very practical and useful in termof industrialized application, including technology, energy and cost aspects. Finally,the co-pyrolysis is of great significance in bio-energy field.