| Autor: |
Alsaffar, May Ali, Abde Ghany, Mohamed A., Mageed, Alya K., Sukkar, Khalid A., Khaleefa Ali, Seroor Atalah |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 3219 Issue 1, p1-6, 6p |
| Abstrakt: |
Currently, 80% of the energy used globally is generated from fossil fuels. The available energy resources is rapidly depleting which implies that if necessary measures are not taken, the energy sources will soon run out. In addition, using fossil fuels has been linked to environmental problems, such as pollution that is destroying the ecosystem. However, the reliance on fossil fuels may be lessened with the existence of other energy sources, particularly when using alternative energy which has more impact on the environment than using fossil fuels. This study proposes to model the prediction of synthetic gas (a mixture of hydrogen and carbon monoxide) from a feedstock that consists of methane and carbon dioxide to produce syngas which is often used to produce methanol. Gaussian Process Regression incorporated with four different kernel functions were used for the modeling. The analysis of the models revealed that kernel functions significantly enhance predictability. The exponential kernel functions based GPR had the best H2/CO prediction as revealed by R2 of 0.999 and RMSE. MSE, and MAE values of. 2.1x 10−5, 4.3 x10−1, and 1.6 x 10−5, respectively. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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