文献类型：期刊文献

英文题名：Management of higher heating value sensitivity of biomass by hybrid learning technique

作者：Lakovic, Nadja[1]; Khan, Afrasyab[2]; Petkovi, Biljana[3]; Petkovic, Dalibor[4]; Kuzman, Boris[5]; Resic, Sead[6]; Jermsittiparsert, Kittisak[7,8,9]; Azam, Sikander[10,11]

第一作者：Lakovic, Nadja

通讯作者：Jermsittiparsert, Kittisak

机构：[1] Faculty of Business and Law, MB University, Belgrade, Serbia; [2] Institute of Engineering and Technology, Department of Hydraulics and Hydraulic and Pneumatic Systems, South Ural State University, Lenin Prospect 76, Chelyabinsk, 454080, Russia; [3] Faculty of Economics, University of Kragujevac, Licej Kneevine Srbije 3, Kragujevac, Serbia; [4] Pedagogical Faculty in Vranje, University of Ni, Partizanska 14, Vranje, Serbia; [5] Institute of agricultural economics, Belgrade, Serbia; [6] Faculty of Natural Sciences and Mathematics, Department for Mathematics, University of Tuzla, Tuzla, Bosnia and Herzegovina; [7] Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam; [8] Faculty of Humanities and Social Sciences, Duy Tan University, Da Nang, 550000, Viet Nam; [9] MBA School, Henan University of Economics and Law, Henan, Zhengzhou, 450046, China; [10] Division of Computational Physics, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; [11] Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam

第一机构：Faculty of Business and Law, MB University, Belgrade, Serbia

年份：2023

卷号：13

期号：4

起止页码：3029-3036

外文期刊名：Biomass Conversion and Biorefinery

收录：EI(收录号：20210109720321);Scopus(收录号：2-s2.0-85098545514)

语种：英文

外文关键词：Biomass - Carbon - Fuzzy inference - Fuzzy neural networks - Fuzzy systems - Heating - Learning systems - Soft computing

摘要：Recently, biomass sources are important for energy applications. Therefore, there is need for analyzing the biomass model based on different components such as carbon, ash, and moisture content. Since the biomass modeling could be very challenging task for conventional mathematical, it is suitable to apply soft computing models which could overcome the nonlinearities of the process. The main attempt in the study was to develop a soft computing model for the prediction of the higher heating values of biomass based on the proximate analysis. Adaptive neuro-fuzzy inference system (ANFIS) was used as soft computing methodology. According to the prediction accuracy 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 a correlation coefficient of 0.7644, the volatile matter has a correlation coefficient of 0.7225, and ash has a correlation coefficient of 0.9317. Therefore, the ash percentage weight has the highest relevance on the higher heating value of the biomass. On the contrary, the volatile matter has the smallest relevance on the higher heating value of the biomass. ? 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.