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- W4381851779 abstract "Quality syngas production having higher moles of hydrogen and methane are the major objective of gasification process which is dependent upon the process parameters and composition of biomass. However, it is always a costly and time-consuming task to get the optimum biomass composition and process parameters. In this research, artificial intelligence (AI) algorithms have been applied for high quality syngas prediction with better moles fractions of hydrogen and methane using hydrothermal gasification (HTG). Comparative analysis of Convolutional Neural Network (CNN), Artificial Neural Network (ANN), Gradient Boost Regressor (GBR), Extreme Boost Regressor (XGB), and Random Forest Regressor (RFR) based algorithms have been done to select an optimal one. Ultimate analysis of biomass and process input parameters temperature, pressure, percentage solid content of biomass, and resident time have been used as an input parameter for prediction models. Final comparative results of these AI models conclude that XGB has a better prediction result as compared to other with coefficient of determinant (R2) and mean square errors ranges from 0.85 to 0.95 and 0.008–0.01, respectively. Furthermore, process temperature and the resident time are the most contributing factors in mole fractions of hydrogen and methane. More hydrogen and oxygen contents in the biomass, contribute to produce quality syngas." @default.
- W4381851779 created "2023-06-25" @default.
- W4381851779 creator A5035499642 @default.
- W4381851779 creator A5056733829 @default.
- W4381851779 creator A5075332205 @default.
- W4381851779 date "2023-10-01" @default.
- W4381851779 modified "2023-10-11" @default.
- W4381851779 title "Estimation of syngas yield in hydrothermal gasification process by application of artificial intelligence models" @default.
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- W4381851779 doi "https://doi.org/10.1016/j.renene.2023.118953" @default.
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