Prediction of higher heating value of biomass materials based on proximate analysis using gradient boosted regression trees method

被引：48
作者：
Samadi, Seyed Hashem [1 ]
Ghobadian, Barat [1 ]
Nosrati, Mohsen [2 ]
机构：
[1] Tarbiat Modares Univ, Dept Biosyst Engn, POB 14115-336, Tehran, Iran
[2] Tarbiat Modares Univ, Chem Engn Dept, Biotechnol Grp, Tehran, Iran
来源：
ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS | 2021年 / 43卷 / 06期
关键词：
Biomass; higher heating value; gradient boosting; proximate analysis; renewable energy; CALORIFIC VALUE; MLP-ANN; ULTIMATE; GASIFICATION; TORREFACTION; PYROLYSIS; MODELS; FUELS; TERMS;
D O I：
10.1080/15567036.2019.1630521
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
In the present research work, a machine learning tool based on the gradient boosted regression trees (GBRT) was used to predict the HHV of biomass. Data of 511 biomass samples were used to develop GBRT for prediction of HHV by utilizing proximate analysis. The values of mean absolute percentage error, root-mean-square error, and the determination coefficient for the developed model were 3.783%, 0.946, and 0.93, respectively, which represents high precision of HHV predictive capability. By comparing the models used to predict HHV, it was proved that the proposed model is better than the models found in literature so far.
引用
收藏
页码：672 / 681
页数：10
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