Investigating the co-combustion characteristics of oily sludge and ginkgo leaves through thermogravimetric analysis coupled with an artificial neural network

作     者：LI ShuChen NIU ShengLi HAN KuiHua LI YingJie WANG YongZheng LU ChunMei LI ShuChen;NIU ShengLi;HAN KuiHua;LI YingJie;WANG YongZheng;LU ChunMei

作者机构：Shandong Engineering Laboratory for High-efficiency Energy Conservation and Energy Storage Technology&EquipmentSchool of Energy and PoweringShandong UniversityJinan 250061China 

出 版 物：《Science China(Technological Sciences)》 (中国科学（技术科学英文版）)

年 卷 期：2022年第65卷第2期

页      面：261-271页

核心收录：

学科分类：12[管理学] 083002[工学-环境工程] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(Grant No.51876106) the Primary Research&Development Plan of Shandong Province,China(Grant No.2018GGX104027) the Young Scholars Program of Shandong University(Grant No.2015WLJH33) 

主　　题：oily sludge co-combustion oxy-fuel atmosphere thermogravimetric analysis artificial neural network 

摘      要：The co-combustion characteristics of oily sludge and ginkgo leaves(GL) in an oxy-fuel atmosphere are investigated via thermogravimetric analysis coupled with an artificial neural network. The combustion characteristics of blends improve as the GL mass ratio increases. The interaction indices used to evaluate the interaction between the two solid combustibles present a complex nonlinear relationship in different stages. The Flynn-Wall-Ozawa and Kissinger-Akahira-Sunose methods are used to calculate the activation energy of the blends, which increases with an increase in the oxygen concentration, in different atmospheres. Compared with the radial basis function, the backpropagation neural network performs better in predicting the combustion curve of the blends.