Biomass higher heating value (HHV) modeling on the basis of proximate analysis using iterative network-based fuzzy partial least squares coupled with principle component analysis (PCA-INFPLS)

In this study, a novel iterative network-based fuzzy partial least squares coupled with principle component analysis (PCA-INFPLS) was proposed to predict the HHV of biomass fuels as a function of fixed carbon (FC), volatile matter (VM), and ash content. In this methodology, the PCA analysis was used to eliminate the co-linearity of experimental data for providing the required background to the INFPLS model. In the INFPLS structure, adaptive network-based fuzzy inference system (ANFIS) was applied to correlate the inputs and the outputs of iterative PLS score vectors. Furthermore, the capability of the PCA-INFPLS approach in estimating the biomass fuels HHV was compared with those of the PLS, ANFIS, NFPLS, and INFPLS models. Generally, the PCA-INFPLS approach was much more efficient than the other applied methods in modeling the biomass fuels HHV. More specifically, the developed model predicted the HHV of biomass fuels with an R2 > 0.96, an MSE < 0.51, and an MAPE < 2.5%. Therefore, this approach could be utilized for reliable and accurate approximation of the HHV of biomass feedstocks based on the proximate analysis instead of lengthy laboratorial measurements. The PCA-INFPLS approach was then embedded into a simple and user-friendly software for estimating the biomass fuels HHV based on the proximate analysis.