Prediction and evaluation of fuel properties of hydrochar from waste solid biomass: Machine learning algorithm based on proposed PSO–NN model

Abstract

Hydrothermal carbonization is an effective and environmentally friendly biomass pretreatment technology, which converts high moisture biomass into homogeneous, carbon–rich, and high calorific value solid hydrochar. This study aimed to predict the fuel properties of the hydrochar based on hydrothermal conditions and biomass characteristics by machine learning (ML) models. Artificial neural network (ANN) combined with particle swarm optimization (PSO) algorithm was proposed and developed based on 296 data points collected from abundant previous studies, and the prediction capability is analyzed with ordinary ANN model. The results showed that particle swarm optimization–neural network (PSO–NN) model with optimal hyper–parameters can reduce iteration time, and improve the stability and accuracy of ANN model. Fuels properties of hydrochar were predicted by PSO–NN model with R2 greater than 0.85 and the convergence speed is increased by 26.8%. Feature importance and correlation were explored by the integration of PSO–NN model and model explainer based on SHAP methodology. The result showed that the carbon content in raw biomass was the significant feature impacting mass yield, and the mass yield of hydrochar mainly depended on elemental composition of feedstock. The HTC temperature of water is the most important factor affecting HHV of the hydrochar, so raising hydrothermal temperature is the best way to improve the HHV. N content was considered as the most important parameter for the N/C molar ratio among all the evaluated features. The O content of the raw biomass had obvious influence on the ASH content of hydrochar, and the influence of operating conditions for ash content changes only accounted for 14.3%, which indicated that the removing efficiency of ash from biomass was low only by changing the operating conditions. Both DHD and DCD of hydrochars were most affected by temperature, and the ash content played a significant role in the prediction of the DHD. Furthermore, we found that most of ash remained in feedstock negatively affected DHD of the hydrochar but had a positive effect on DCD. The PSO–NN model can be used for pre–experiment condition design, which is convenient for researchers to obtain ideal hydrothermal products.