Insight into nettle straw pyrolysis: Multicomponent kinetics, gas emissions and machine learning models

Abstract

This study aimed to comprehensively investigate the pyrolysis process of nettle straw by analyzing its multicomponent kinetics modeling, kinetics and thermodynamic parameters, pyrolysis performance, reaction mechanisms, gas emissions, correlation and statistical analysis, as well as feasibility for industrial application. The average apparent activation energies for pseudo-hemicellulose (PS-HEC), cellulose (PS-CEL) and lignin (PS-LIG) were estimated as 147.41, 182.66, and 197.12 kJ/mol, respectively, using four model-free methods: Ozawa-Flynn-Wall, Kissinger-Akahira-Sunose, Starink, and Tang. The pyrolysis reaction mechanisms were best described by diffusional and order-based models, with reaction mechanism functions of f(α) = [3/2(1-α)2/3]/[1-(1-α)1/3], f(α) = (1-α)1.5, and f(α) = (1-α)3 for PS-HEC, PS-CEL, and PS-LIG, respectively. TGA-FTIR analysis revealed that the gas emissions during nettle straw pyrolysis followed the order of CO > CO2 > C-O(H) > H2O > SO2 > CO > CH4 > NH3 > HCN. Best learning rate and artificial neural network (ANN) structure model were optimized. The optimal learning rate and ANN model structure were 0.0013 and ANN (5 *9 *1), respectively. NS has the potential to serve as a viable alternative to biofuel due to its certain energy, low price, abundant reserves, good pyrolysis performance, high volatile content, and low nitrogen and sulfur levels.