Estimation of syngas yield in hydrothermal gasification process by application of artificial intelligence models

Keyphrases

• Methane 100%
• Artificial Intelligence Applications 100%
• Gasification Process 100%
• Artificial Intelligence Models 100%
• Syngas Yield 100%
• Hydrothermal Gasification 100%
• Mole Fraction 66%
• Syngas Production 66%
• Resident Time 66%
• Artificial Neural Network 33%
• Ultimate Analysis 33%
• Comparative Analysis 33%
• Processing Temperature 33%
• Hydrogen Content 33%
• Prediction Model 33%
• Oxygen Content 33%
• Solid Content 33%
• Mean Square Error 33%
• Error Range 33%
• Syngas 33%
• Artificial Intelligence Algorithm 33%
• Convolutional Neural Network 33%
• Biomass Composition 33%
• Regressor 33%
• Gradient Boosting Regressor 33%
• High-quality Syngas 33%
• Biomass Input 33%
• Result Prediction 33%
• Process Inputs 33%
• Random Forest Regressor 33%

Engineering

• Syngas 100%
• Gasification 100%
• Regressors 100%
• Methane 100%
• Artificial Intelligence 100%
• Input Parameter 66%
• Process Parameter 66%
• Mole Fraction 66%
• Syngas Generation 66%
• Phase Composition 66%
• Comparative Analysis 33%
• Ultimate Analysis 33%
• Solid Content 33%
• Random Forest 33%
• Mean Square Error 33%
• Oxygen Content 33%
• Artificial Neural Network 33%
• Convolutional Neural Network 33%

Chemical Engineering

• Artificial Intelligence 100%
• Gasification 100%
• Syngas Generation 66%
• Neural Network 66%