Predicting co-pyrolysis of coal and biomass using machine learning approaches

Coal and biomass co-thermochemical conversion has caught significant attentions, in which the co-pyrolysis is always the primary process. The traditional pyrolysis kinetic models are developed individually for coal and biomass, in which the synergistic effect wasn't comprehensively considered. In the present study, we innovatively explored a new method to accurately model this process using machine learning approaches, specifically the random forest algorithm based on classification and regression trees and extremely trees. First, a co-pyrolysis database is constructed from experimental data in published literatures, then divided into several sub-sets for training, application, and optimization, respectively. The machine learning models are trained on the training data-set, tested on the test data-set, and applicated on the new data-set. The training and test results demonstrate both models are able to well predict the co-pyrolysis (R2 > 0.999), and the application results demonstrate models also perform well at outside data (R2 > 0.873), with model based on extremely trees performs better owing to its better accuracy, generalization and less overfitting. It also demonstrates the known of biomass pyrolysis will be better than known of coal pyrolysis. In addition, the suggestion of input feature groups is given through parametric study, and variable importance measurement are explored.

Automated summary

The study developed machine learning models using the random forest algorithm to accurately predict and applied co-thermochemical conversion, with extremely trees model showing better performance in prediction and generalization. The results suggest that the known of biomass pyrolysis is better than known of coal pyrolysis, and recommendations for input feature groups and variable importance measurements are provided.