Using XGBoost Regression to Analyze the Importance of Input Features Applied to an Artificial Intelligence Model for the Biomass Gasification System

Recently, artificial intelligence models have been developed to simulate the biomass gasification systems. The extant research models use different input features, such as carbon, hydrogen, nitrogen, sulfur, oxygen, and moisture content, in addition to ash, reaction temperature, volatile matter (VM), a lower heating value (LHV), and equivalence ratio (ER). The importance of these input features applied to artificial intelligence models are analyzed in this study; further, the XGBoost regression model was used to simulate a biomass gasification system and investigate its performance. The top-four features, according to the results are ER, VM, LHV, and carbon content. The coefficient of determination (R2) was highest (0.96) when all eleven input features noted above were selected. Further, the model performance using the top-three features produced a R2 value of 0.93. Thus, the XGBoost model performance was validated again and observed to outperform those of previous studies with a lower mean-squared error of 1.55. The comparison error for the hydrogen gas composition produced from the gasification at a temperature of 900 °C and ER = 0.4 was 0.07%.