Slurry-ability mathematical modeling of microwave-modified lignite: A comparative analysis of multivariate non-linear regression model and XGBoost algorithm model

Abstract

In the preparation of microwave-modified lignite (ML) water slurry (MLWS), its specific surface area, oxygen functional groups and oxygen-carbon atomic ratio are important factors affecting the slurry ability. Based on the relationship between the physicochemical properties of 23 ML samples and their maximum solid concentrations, a multivariate nonlinear regression (MNR) model and a machine learning model based on XGBoost algorithm were established to predict the slurry ability. The determination coefficient (R2), the mean average error (MAE) and the mean squared errors (MSE) for MNR mode is 0.928, 0.507 and 0.391, respectively. The R2, MAE and MSE for XGBoost model is 0.582, 0.619 and 0.667, respectively. Hence, the MNR model with less error percentage has a higher accuracy than the XGBoost model, but the XGBoost model is highly efficient, flexible and portable with strong generalization ability to unknown data. Overall, the proposed models in this study have a good effect on the prediction of the slurry ability of ML, and serve as a technical reference for future predicting the slurry ability of other types of coal.