Application of ANFIS-PSO algorithm as a novel method for estimation of higher heating value of biomass

ABSTRACT

One of the important parameters in economic study of energy sources and bioenergy is higher heating value (HHV). In this investigation, adaptive neuro fuzzy inference system (ANFIS) was applied as a novel method to predict HHV of biomass in terms of fixed carbon (FC), ash content (ASH), and volatile matters (VMs). Due to the fact that experimental investigations are time- and cost-consuming, this investigation was selected purely computational and a total number of 350 experimental data were extracted from literature for different steps of modeling. The proposed algorithm was evaluated by statistical indexes such as coefficient of determination (R²), root mean squared error (RMSE), and average absolute relative deviation (AARD), which are 0.90757, 1.1792, and 5.266, respectively. The reported indexes showed that ANFIS-particle swarm optimization can be used as a novel computational approach for prediction of HHV as function of proximate analysis.

KEYWORDS:

• ANFIS-PSO
• biomass
• energy source
• HHV
• proximate analysis