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];Petkovic, 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, K[1];Jermsittiparsert, K[2];Jermsittiparsert, K[3]

机构：[1]MB Univ, Fac Business & Law, Belgrade, Serbia;[2]South Ural State Univ, Dept Hydraul & Hydraul & Pneumat Syst, Inst Engn & Technol, Lenin Prospect 76, Chelyabinsk 454080, Russia;[3]Univ Kragujevac, Fac Econ, Licej Knezevine Srbije 3, Kragujevac, Serbia;[4]Univ Nis, Pedag Fac Vranje, Partizanska 14, Vranje, Serbia;[5]Inst Agr Econ, Belgrade, Serbia;[6]Univ Tuzla, Dept Math, Fac Nat Sci & Math, Tuzla, Bosnia & Herceg;[7]Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam;[8]Duy Tan Univ, Fac Humanities & Social Sci, Da Nang 550000, Vietnam;[9]Henan Univ Econ & Law, MBA Sch, Zhengzhou 450046, Henan, Peoples R China;[10]Ton Duc Thang Univ, Inst Computat Sci, Div Computat Phys, Ho Chi Minh City, Vietnam;[11]Ton Duc Thang Univ, Fac Elect & Elect Engn, Ho Chi Minh City, Vietnam

第一机构：MB Univ, Fac Business & Law, Belgrade, Serbia

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:000604498500005)】；

语种：英文

外文关键词：Higher heating value; Adaptive neuro-fuzzy; Biomass; Prediction

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.