Kinetic and thermodynamic analysis of co-pyrolysis of rice straw and polystyrene

被引:2
|
作者
Anshu, Kumari [1 ]
Thengane, Sonal K. [1 ]
机构
[1] Indian Inst Technol Roorkee, Dept Hydro & Renewable Energy, Roorkee 247667, Uttarakhand, India
关键词
Co-pyrolysis; Rice straw; Polystyrene; Kinetic analysis; Thermodynamic analysis; Artificial neural network modeling; BIOFUEL PRODUCTION; WASTE POLYSTYRENE; BIOMASS PYROLYSIS; RECENT PROGRESS; POLYETHYLENE; PLASTICS; LIQUID; CONVERSION; MIXTURES; PRODUCTS;
D O I
10.1007/s13399-024-06047-6
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The present work investigates the co-pyrolysis of rice straw (RS) and polystyrene (PS) using a thermogravimetric analyzer to understand the kinetics and synergistic effect between the two feedstocks. Seven samples, namely, RS, PS, PS 0.05 (5 wt.% PS), PS 0.1 (10 wt.% PS), PS 0.2 (20 wt.% PS), PS 0.3 (30 wt.% PS), and PS 0.4 (40 wt.% PS) are used for the analysis. Two pyrolysis performance indices: devolatilization index (DI) and heat resistance index (HRI), are estimated to respectively analyze the volatiles release potential and thermal stability of the samples. Activation energy values are estimated using seven different iso-conversional models whereas the pre-exponential factor (A) is determined by the Kissinger equation and reaction order is determined using Avrami theory. The average apparent activation energy for different blends varies from 140.26 kJ/mol to 224.17 kJ/mol, with a minimum value obtained for PS 0.3 (135.71 kJ/mol) followed by PS 0.1 (139.95 kJ/mol) and PS 0.05 (140.27 kJ/mol). The reaction order concerning different temperatures and Criado master plot results reflect that RS, PS, and their respective blends followed a complex pyrolysis/co-pyrolysis reaction mechanism. The kinetic parameters gained via the most accurate Vyazovkin method are used to estimate Gibbs free energy (triangle G), enthalpy (triangle H), and entropy (triangle S) values. The estimated kinetic and thermodynamic parameters predicted PS 0.05, PS 0.1, and PS 0.3 as attractive blends for co-pyrolysis. Additionally, an artificial neural network (ANN) model is developed to predict the thermal decomposition of samples based on temperature, heating rate, and blending ratio. This study provides essential information for understanding the reaction mechanism and reactor design for RS and PS co-pyrolysis.
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页数:25
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