A weight coefficient distribution method based on quantile impact value for modeling low calorific value in coal-fired power plants. Issue 2 (1st June 2023)

• Record Type: Journal Article
• Authors: Qi, Minfang; Luo, Huageng; Fu, Zhongguang
• Is Part Of: Energy sources. Volume 45:Issue 2(2023)
• Page Start: 4682
• Page End: 4696
• Publication Date: 2023-06-01
• Subjects: Low calorific value of blended coals -- proximate analysis -- quantile impact value -- support vector regression -- weight coefficient distribution; Natural resources -- Periodicals; Energy consumption -- Periodicals; Energy consumption -- Climatic factors -- Periodicals; Energy conversion -- Periodicals; Energy conversion -- Environment aspects -- Periodicals; Power (Mechanics) -- Periodicals; 333.7905
• DOI: 10.1080/15567036.2019.1670753
• Languages: English
• ISSNs: 1556-7036

Abstract

ABSTRACT: In this paper, the quantile impact value (QIV) method was proposed as a weight coefficient distribution method to evaluate the influence of proximate analysis components on the low calorific value (LCV) of blended coals. Through data analysis technology, a new prediction model based on the QIV and the support vector regression (SVR) has been proposed to estimate the LCV. The comparisons between different analysis results reveal that the QIVW-GA-SVR model proposed here is consistently better than those models without weight coefficient distribution.