Comparison kinetic analysis between Coats-Redfern and Criado’s master plot on pyrolysis of horse manure

As a commonly used approach in kinetics analysis, the model-fit method is typically employed to determine the feedstock’s reaction model undergoing thermal decomposition. There are several methods in the literature to obtaining the reaction model. However, very little research has been done to compa...

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Bibliographic Details
Main Authors: Li, Bo Yu, Tee, Meng Yang, Nge, Kie Seng, Ng, Andrew Kay Lup, Chong, William Woei Fong, Ng, Jo-Han, Mong, Guo Ren
Format: Article
Language:English
Published: Italian Association of Chemical Engineering - AIDIC 2023
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Online Access:http://eprints.utm.my/105965/1/WilliamChongWoei2023_ComparisonKineticAnalysisBetweenCoatsRedfern.pdf
http://eprints.utm.my/105965/
http://dx.doi.org/10.3303/CET23106213
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Institution: Universiti Teknologi Malaysia
Language: English
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Summary:As a commonly used approach in kinetics analysis, the model-fit method is typically employed to determine the feedstock’s reaction model undergoing thermal decomposition. There are several methods in the literature to obtaining the reaction model. However, very little research has been done to compare the discrepancies resulting from different model-fitting methods. In this study, two model-fitting methods, Coats-Redfern (CR) and Criado master plot were used to evaluate horse manure pyrolysis's kinetic reaction model. The feedstock was pyrolyzed in a thermogravimeter at a temperature ramp rate of 10 °C/min under N2 atmosphere. Both methods indicated that the 2-D Diffusion (Anti-Jander) reaction model was most suited to describe the pyrolysis reaction, illustrating that the reaction rate was limited by the diffusion of components through the product layer at the interface of feedstock. The other two parameters of kinetic triplets as determined from the CR method are activation energy, Ea at 68.3 kJ/mol and pre-exponential factor, A at 4.16×106 s-1. The Ea value in this work is much lower than that obtained through the model-free method.