Developing a Proximate Component Prediction Model of Biomass Based on Element Analysis

Interest in biomass has increased due to current environmental issues, and analysis is usually performed using element proximate analyses ascertain its fuel characteristics. Mainly, component prediction models have been developed based on analysis, yet few studies predicted components analysis. Hence, this study a model following the calorific value calculation. Analysis of Pearson’s correlation coefficient showed that volatile matter (VM) fixed carbon (FC) were positively correlated with hydrogen oxygen, carbon, respectively. Thus, was combination “stepwise” “enter” methods along linear or nonlinear regressions. The optimal for VM ash content (Ash). values were: R2 = 0.9402, root-mean-square error (RMSE) 7.0063, average absolute (AAE) 14.8170%, bias (ABE) −11.7862%. For Ash, 0.9249, RMSE 2.9614, AAE 168.9028%, ABE 167.2849%, FC, 9505, 6.3214, 18.3199%, 15.0094%. This provides predict by Contrary existing method, can be elemental shows consume samples at once.