Machine learning prediction of pyrolytic gas yield and compositions with feature reduction methods: Effects of pyrolysis conditions and biomass characteristics

Abstract This study aimed to utilize machine learning algorithems combined with feature reduction for predicting pyrolytic gas yield and compositions based on pyrolysis conditions and biomass characteristics. To this end, random forest (RF) and support vector machine (SVM) was introduced and compared. The results suggested that six features were adequate to accurately forecast (R2 > 0.85, RMSE