A new numerical method to predict the growth temperature of spontaneous combustion of 1/3 coking coal. (25th February 2018)

• Record Type: Journal Article
• Authors: Xiao, Yang; Lü, Hui-Fei; Huang, An-Chi; Deng, Jun; Shu, Chi-Min
• Is Part Of: Applied thermal engineering. Volume 131(2018)
• Page Start: 221
• Page End: 229
• Publication Date: 2018-02-25
• Subjects: Growthtemperature -- Apparent activation energy -- Kissinger–Akahira–Sunose method -- Grey correlation analysis -- Quasi-Newton method; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2017.12.007
• Languages: English
• ISSNs: 1359-4311

Abstract

Graphical abstract: Highlights: Growth temperature during spontaneous coal combustion is defined and evaluated. Apparent activation energy is calculated by the improved KAS method. The model predicts growth temperature based on ultimate and proximate analyses. The proximate analysis model exhibits little prediction error. Abstract: Hazards caused by spontaneous coal combustion are of deep concern. To explore the process of spontaneous coal combustion, we focused on the key aspects of growth temperature ( T g ) and the apparent activation energy ( E a ), where T g is associated with the E a . We used four different heating rates to perform thermogravimetric (TG) experiments to calculate E a values through an improved Kissinger–Akahira–Sunose method. The findings revealed a sudden change point in E a values (that is, T g ), and comparisons between experimental data and calculated results demonstrated favorable agreement. The grey correlation analysis method was used to analyze the relationship between the samples' T g and intrinsic properties. A quasi-Newton method and general global optimization model were adopted to derive a predictive relationship for spontaneous coal combustion. The results are beneficial and crucial for elucidating the mechanism of spontaneous coal combustion, which could facilitate a thorough understanding of this process and help provide proactive loss prevention measures.