Estimation of the higher heating value of biomass using proximate analysis

The higher heating value (HHV) parameter of biomass is well known for its wide application in bioenergy industry and the economical study of energy resources. In the present study, the least squares support vector machine (LSSVM) strategy is used as a novel approach to estimate HHV of biomass as a function of volatile matters (VM), fixed carbon (FC), and ash content (ASH). A total number of 350 experimental data points have been extracted from previous works to train and test the proposed algorithm. In order to judge the proposed model, the statistical parameters such as R2, RMSE, and AARD are calculated as 0.92936, 4.2731%. Based on the calculated parameters, it can be concluded that the LSSVM approach has great ability to estimate the HHV of biomass in terms of VM, FC, and ASH. Due to the fact that the experimental investigations are time-consuming and costly, the importance of this study has been highlighted.