Abstract
Research findings on Machine Learning are discussed in a new report. According to news reporting originating in Anhui, People's Republic of China, by NewsRx journalists, research stated, "Understanding the sulfur migration during pyrolysis of coals especially high -sulfur coals is important. However, structural complexity and diversity of coals make it face huge challenge." Financial supporters for this research include National Natural Science Foundation of China (NSFC), Key Project of Scien- tific Research Plan of Anhui Province, Provincial Innova- tive Group for Processing & Clean Utilization of Coal Resource. The news reporters obtained a quote from the research from the Anhui University of Technology, "In this study, a predictive model for morphological sulfur migration was developed using machine learning based on proximate analysis, ultimate analysis, sulfur forms of raw coal, ash composition, and blending ratio of coal. Three algorithms, i.e., Random Forest, XGBoost, and LightGBM were introduced and compared. The results show that six features are sufficient to accurately predict the products (R-2 ＞0.9, RMSE ＜3.01%). LightGBM model has the advantages of better accuracy, generalization, efficiency, and performance, and Hyperopt has a higher upper limit than Grid-search. H content has a significant effect on S content in chars (St,d(char)) and increasing H content from 5.0-5.3 wt% facilitates desulfurization. In addition, CaO, K2O and Fe2O3 also have remarkable effects on St,d(char). Higher H and volatile contents have a greater effect on thiophene removal in char."
NETL
NSTL
Elsevier