Kinetics, reaction mechanism and product distribution of lignocellulosic biomass pyrolysis using triple-parallel reaction model, combined kinetics, Py-GC/MS, and artificial neural networks

Abstract

Pyrolysis of biomass is a crucial process for the production of renewable energy and a sustainable alternative to fossil fuels. The present study analyzed the pyrolysis process and the composition of the products of six representative lignocellulosic biomasses using simultaneous thermal analyzer, pyrolysis - gas chromatography/mass spectrometry (Py-GC/MS), liquid-phase nuclear magnetic resonance (NMR) spectroscopy and X-ray photoelectron spectrometer (XPS). Furthermore, based on the TG results, the kinetics of the pyrolysis of three components of the six biomasses were explored using kinetic modelling, triple-parallel reaction model and Asym2sig deconvolution function. Subsequently, kinetic models of the six biomasses were developed using a combinatorial kinetic method. Finally, an artificial neural network (ANN) model was developed to predict the pyrolysis behaviors of the studied biomasses. For example, corn straw (CS) revealed three primary pyrolysis stages: below 400 K (volatilization of small molecules), 400–670 K (decomposition of major components), and above 670 K (charring of the residual components and secondary decomposition of intermediates). The optimum kinetics models for CS components were: f₁(α₁) = α₁^−0.80697 (1 − α₁)^1.99689 [−ln(1 − α₁)]^1.15425, f₂(α₂) = α₂^0.43522 (1 − α₂)^1.29066 [−ln(1 − α₂)]^0.32644, and f₃(α₃) = α₃^−2.82644 (1 − α₃)³ [–ln(1 − α₃)]^−1.87480. Moreover, ANN23 showed the highest R² value (0.99908). Therefore, ANN23 is the most suitable model for predicting the pyrolysis of CS. The present research provides valuable references for the pyrolysis of biomass.