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Pyrolytic characteristics of abutilon stalk waste using TG-FTIR, Py-GC/MS, and artificial neural networks: Kinetics, thermodynamics, and gaseous products distribution

Journal of Analytical and Applied Pyrolysis(2024)

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摘要
The physicochemical properties of abutilon stalk (ABUS) were investigated to assess its suitability for pyrolytic conversion into bioenergy and bio-based chemicals based on kinetic triplet state, thermodynamic parameters, decomposition mechanisms and product distributions. The pyrolysis behavior of ABUS was studied using a thermogravimetric analyzer at five heating rates in a nitrogen atmosphere. The Gaussian deconvolution function indicated that the pyrolysis process of ABUS can be successfully modeled as four parallel devolatilization events (R-2 > 0.99), divided into the devolatilization of pseudo drying (PO-D), hemicellulose (PO-H), cellulose (PO-C), and lignin (PO-L), respectively. The average apparent activation energies were 34.89 kJ/mol for PO-D, 163.02 kJ/mol for PO-H, 169.55 kJ/mol for PO-C, and 107.51 kJ/mol for PO-L using four model-free methods (Ozawa-Flynn-Wall, Kissinger-Akahira-Sunose, Starink, and Tang). The pyrolysis process was accurately predicted using diffusional, power law, geometrical contraction, and order-based reaction mechanism models. Thermal degradation process for each devolatilization event was successfully reconstructed using combined kinetics (R-2 > 0.96), providing a useful theoretical basis and mathematical model for predicting the pyrolysis behavior of ABUS. The volatile products produced by ABUS pyrolysis were characterized using an integrated TGA-FTIR and Py-GC/MS system, confirming the proportion of high-energy compounds (benzenes, phenols, ketones, alcohols, aldehydes, acids, esters, hydrocarbons and their derivatives, N-heterocyclic substances, and N-containing compounds) and gas emission levels (CO > CH4 > C-O > H2O > CO2 > CO > C-O(H) > HCN > NH3 > SO2). Artificial neural network (ANN) was used to predict the pyrolysis behaviors of ABUS, and the best ANN model was ANN (5 *11 *1) with high R-2 of 0.99998. Based on the kinetics, thermodynamics, and gaseous products distribution, ABUS has great application potential as raw material for bioenergy production.
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关键词
Pyrolysis,Kinetics,Artificial neural network,TG-FTIR,Py-GC/MS
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