A comprehensive study on estimating higher heating value of biomass from proximate and ultimate analysis with machine learning approaches. (1st December 2019)

• Record Type: Journal Article
• Authors: Xing, Jiangkuan; Luo, Kun; Wang, Haiou; Gao, Zhengwei; Fan, Jianren
• Is Part Of: Energy. Volume 188(2019)
• Publication Date: 2019-12-01
• Subjects: Biomass -- Higher heat value (HHV) -- Proximate analysis -- Artificial neural network (ANN) -- Support vector machine (SVM) -- Random forest (RF); Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.116077
• Languages: English
• ISSNs: 0360-5442

Abstract

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 ( R 2 > 0.90) compared with those of empirical correlations ( R 2 < 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 R 2 >0.94. The SVM and ANN approaches show similar performances with R 2 ∼ 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. Highlights: Estimating biomass HHV from biomass property via machine learning approaches. Machine learning models give better predictions compared with empirical correlations. Random forest model shows the best performance with R 2 > 0.94. Artificial neural network and support vector machine models show similar predictions. Relative importance of each input on biomass HHV is explored.