Coal quality regression data by machine learning and LIBS
收藏科学数据银行2024-06-18 更新2026-04-23 收录
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资源简介：
Laser-induced breakdown spectroscopy (LIBS) was utilized to simultaneously determine the elemental carbon content, ash content, volatile matter, total sulfur, and gross calorific value of coal samples by establishing quantitative algorithms between LIBS spectra and values tested by standard methods of 49 coal samples. The quantitative analysis performance of support Vector Machine (SVM), Random Forest (RF), Kernel Extreme Learning Machine (K-ELM), and Least Squares Support Vector Machine (LS-SVM) after preprocessing of abnormal data removal, baseline correction and noise reduction.
提供机构：
State Power Environmental Protection Research Institute; Youquan Dou; National Energy Investment Group; Donglian Zhang; Xianjing Jie; Maorong Lu; Qingsong Wang; Nanjing University of Science and Technology
创建时间：
2024-06-17
