A comprehensive study on estimating higher heating value of biomass from proximate and ultimate analysis with machine learning approaches

Abstract

Higher heating value (HHV) is an important parameter for design and operation of biomass-fueled energy systems. Experimental approach is always time-consuming and expensive for determinating this property compared with mathematical models. In this paper, three machine learning approaches, including artificial neural network (ANN), support vector machine (SVM) and random forest (RF), are employed for accurately estimating biomass HHV from ultimate or proximate analysis. The linear and nonlinear empirical correlations are also carried out for comparison. The results show machine learning approaches give better predictions (R2 > 0.90) compared with those of empirical correlations (R2 < 0.70), especially for the extreme values. The RF model shows the best performances for both the ultimate and proximate analysis, with the determination coefficient R2>0.94. The SVM and ANN approaches show similar performances with R2∼ 0.90. Ultimate-based models show better performances compared with those of the proximate-based models even with much less samples. Relative importance analysis shows for the proximate analysis, the ash, volatile matter and fixed carbon fractions show the maximum, medium and minimum effects, respectively. For the ultimate analysis, carbon and hydrogen fractions hold the first two significant places with carbon fraction having the most significant influence, while the oxygen and nitrogen fractions have limited effects.