文献类型：期刊文献

英文题名：Machine learning prediction of higher heating value of biomass

作者：Dai, Zuocai[1,2];Chen, Zhengxian[3];Selmi, Abdellatif[4,5];Jermsittiparsert, Kittisak[6,7,8];Denic, Nebojsa M.[9];Nesic, Zoran[10]

第一作者：Dai, Zuocai

通讯作者：Jermsittiparsert, K[1];Jermsittiparsert, K[2];Jermsittiparsert, K[3]

机构：[1]Hunan City Univ, Coll Mech & Elect Engn, Yiyang 413002, Hunan, Peoples R China;[2]Key Lab Energy Monitoring & Edge Comp Smart City, Yiyang 413002, Hunan, Peoples R China;[3]Columbia Univ, Dept Mech Engn, New York, NY 10027 USA;[4]Prince Sattam Bin Abdulaziz Univ, Coll Engn, Dept Civil Engn, Al Kharj 11942, Saudi Arabia;[5]Ecole Natl Ingenieurs Tunis ENIT, Civil Engn Lab, BP 37, Tunis 1002, Tunisia;[6]Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam;[7]Duy Tan Univ, Fac Humanities & Social Sci, Da Nang 550000, Vietnam;[8]Henan Univ Econ & Law, MBA Sch, Zhengzhou 450046, Henan, Peoples R China;[9]Univ Pristina, Fac Sci & Math, Kosovska Mitrovica, Serbia;[10]Univ Kragujevac, Fac Tech Sci Cacak, Svetog Save 65, Cacak 32102, Serbia

第一机构：Hunan City Univ, Coll Mech & Elect Engn, Yiyang 413002, Hunan, Peoples R China

通讯机构：[1]corresponding author), Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam;[2]corresponding author), Duy Tan Univ, Fac Humanities & Social Sci, Da Nang 550000, Vietnam;[3]corresponding author), Henan Univ Econ & Law, MBA Sch, Zhengzhou 450046, Henan, Peoples R China.|[1048419]河南财经政法大学MBA学院;[10484]河南财经政法大学;

年份：0

外文期刊名：BIOMASS CONVERSION AND BIOREFINERY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000607326900003)】；

基金：This work was supported by the Project of Key Laboratory Energy monitoring and Edge Computing of for Smart City of Hunan Province (No.2017TP1024), the Scientific Research project of Hunan Education Department (20B113), the Teaching Reform Research Project of Hunan City university (202024), and the Curriculum ideological and political education reform of College of Mechanical and Electrical Engineering (202015).

语种：英文

外文关键词：Biomass; Higher heating value; ELM; Prediction

摘要：Recently, biomass sources are important for energy applications. There is need for analyzing of the biomass model based on different components such as carbon, ash, and moisture content since the biomass sources are important for energy applications. In this paper, an extreme learning machine (ELM) is used to estimate efficiency. ELM was implemented for single-layer feed-forward neural network (SLFN) architectures. Because biomass modeling could be a very challenging task for conventional mathematical, it is suitable to apply machine learning models which could overcome nonlinearities of the process. The main attempt in this study was to develop a machine learning model for prediction of the higher heating values of biomass based on proximate analysis. According the prediction accuracy (coefficient of determination and root mean square error) of the higher heating value of the biomass, the inputs' influence was determined on the higher heating value. According to the obtained results, fixed carbon has less moderate coefficient, ash has less correlation coefficient, and volatile matter has the most correlation coefficient. Therefore, the volatile matter percentage weight has the highest relevance on the higher heating value of the biomass. On the contrary, the ash has the smallest relevance on the higher heating value of the biomass based on machine learning approach.