Improved prediction of higher heating value of biomass using an artificial neural network model based on proximate analysis.
As biomass becomes more integrated into our energy feedstocks, the ability to predict its combustion enthalpies from routine data such as carbon, ash, and moisture content enables rapid decisions about utilization. The present work constructs a novel artificial neural network model with a 3-3-1 tangent sigmoid architecture to predict biomasses' higher heating values from only their proximate analyses, requiring minimal specificity as compared to models based on elemental composition. The model presented has a considerably higher correlation coefficient (0.963) and lower root mean square (0.375), mean absolute (0.328), and mean bias errors (0.010) than other models presented in the literature which, at least when applied to the present data set, tend to under-predict the combustion enthalpy.
Ondokuz Mayıs University, Chemical Engineering Department, Samsun, Turkey.
Harun
H
Uzun
Ondokuz Mayıs University, Chemical Engineering Department, Samsun, Turkey.
Zeynep
Z
Yıldız
Boston University, Department of Mechanical Engineering and Division of Materials Science & Engineering, 110 Cummington Mall, Boston, MA 02215, USA.
Jillian L
JL
Goldfarb
Ondokuz Mayıs University, Chemical Engineering Department, Samsun, Turkey. Electronic address: selim.ceylan@omu.edu.tr.
Selim
S
Ceylan
D018533:Biomass; D002244:Carbon; D006361:Heating; D008962:Models, Theoretical; D016571:Neural Networks, Computer
D002244:Carbon
Artificial neural network; Biomass; Higher heating value; Proximate analysis