Utilisation of machine learning algorithms for the prediction of syngas composition from biomass bio-oil steam reforming

被引:26
|
作者
Adeniyi, Adewale George [1 ]
Ighalo, Joshua O. [1 ,2 ]
Marques, Goncalo [3 ]
机构
[1] Univ Ilorin, Dept Chem Engn, Ilorin, Nigeria
[2] Nnamdi Azikiwe Univ, Dept Chem Engn, Awka, Nigeria
[3] Univ Beira Interior, Inst Telecommun, Covilha, Portugal
关键词
ANN; biomass; bio-oil; hydrogen; machine learning; steam reforming; HYDROGEN-PRODUCTION; THERMODYNAMIC ANALYSIS; NEURAL-NETWORKS; FAST PYROLYSIS; PERFORMANCE; CATALYSTS; MODEL; ANN;
D O I
10.1080/14786451.2020.1803862
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The aim of this study was to utilise artificial neural network (ANN) and AdaBoost (AB) algorithms to model the synthesis gas composition from the steam reforming of biomass bio-oil. At testing on training data, it was observed that R-2 > 0.999 was achieved for both algorithms for all product selectivity indicating a 99.9% capture of data variability. Also, the RMSE values were <0.007 in most cases. The MAE values were <0.005 in most cases. The ANN predictions were observed to be more accurate than AB predictions for the current application. On the other hand, considering stratified 10-fold cross-validation the proposed models present R-2 > 0.9 using AB considering hydrogen and carbon dioxide, and using ANN considering methane and carbon monoxide.
引用
收藏
页码:310 / 325
页数:16
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