Co–pyrolysis of de–alkalized lignin and coconut shell via TG/DTG–FTIR and machine learning methods: pyrolysis characteristics, gas products, and thermo–kinetics

Abstract

Co–pyrolysis characteristics and thermo–kinetics of de–alkalized lignin (DL) and coconut shell (CS) were investigated by using TG–FTIR and machine learning methods. The DL–CS samples mixed in different proportions were heated from ambient temperature to 1173.15 K at 10, 15, and 20 K·min−1. The gas functional groups in the pyrolysis process of the experimental samples were detected by TG–FTIR. The apparent activation energy (E) was estimated by Flynne–Walle–Ozawa (FWO), Kissinger–Akahira–Sunose (KAS), and Starink methods, and the R2 values of these three methods are greater than 0.97032, which means that the activation energy solved by these methods is feasible. An BP–NN model of 9×3×1 architecture was employed to predict the residual weight of DL–CS co–pyrolysis. The experimental values were in good agreement with the predicted values by BP–NN model (RMSE = 0.8606, R2 = 0.99888). A Stacked XGB–LGBM–MLP model was employed to improve the overall prediction performance of DL–CS co–pyrolysis, and this model succeeds to preform best (RMSE = 0.1888, R2 = 0.99995) of all individual models from test set result parameters. Our research results contribute to the optimal operating conditions for energy utilization, pollution control, and thermochemical conversion of lignin waste industry.