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]; Deni, Neboja M.[9]; Ni, Zoran[10]

第一作者：Dai, Zuocai

通讯作者：Jermsittiparsert, Kittisak

机构：[1] College of Mechanical and Electrical Engineering, Hunan City University, Yiyang, Hunan, 413002, China; [2] Key Laboratory Energy Monitoring and Edge Computing for Smart City of Hunan Province, Yiyang, Hunan, 413002, China; [3] Department of Mechanical Engineering, Columbia University, New York, NY, 10027, United States; [4] Department of Civil Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia; [5] Ecole Nationale d’Ingénieurs deTunis [ENIT], Civil Engineering Laboratory, B.P. 37, Le belvédère1002, Tunis, Tunisia; [6] Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam; [7] Faculty of Humanities and Social Sciences, Duy Tan University, Da Nang, 550000, Viet Nam; [8] MBA School, Henan University of Economics and Law, Zhengzhou, Henan, 450046, China; [9] Faculty of Sciences and Mathematics, University of Pritina, Kosovska Mitrovica, Serbia; [10] Faculty of Technical Sciences aak, University in Kragujevac, Svetog Save 65, aak, 32102, Serbia

第一机构：College of Mechanical and Electrical Engineering, Hunan City University, Hunan, Yiyang, 413002, China

年份：2021

外文期刊名：Biomass Conversion and Biorefinery

收录：EI(收录号：20210309792231);Scopus(收录号：2-s2.0-85099392816)

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 - Carbon - Feedforward neural networks - Forecasting - Heating - Machine learning - Mean square error - Multilayer neural networks - Predictive analytics - Turing machines

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. ? 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.