| Autor: |
Samadi, Seyed Hashem1 (AUTHOR), Ghobadian, Barat1 (AUTHOR) ghobadib@modares.ac.ir, Nosrati, Mohsen2 (AUTHOR) |
| Předmět: |
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| Zdroj: |
Energy Sources Part A: Recovery, Utilization & Environmental Effects. 2021, Vol. 43 Issue 6, p672-681. 10p. |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
GreenFILE |
| Externí odkaz: |
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