Investigation and optimization of biodiesel production based on multiple machine learning technologies

Biodiesel prepared by transesterification reaction was a potential fuel to address the global energy issues due to obtaining from biomass resources (waste oils, micro-algae, and plant oils) and environmentally friendly. However, biodiesel production achieved by means of the transesterification process was affected by various factors such as feedstock type, reaction time, reaction temperature, and catalyst. Recently, machine learning (ML) presents a versatile approach to predicting biodiesel yield which avoids a number of experiments. Herein, we collected 13 cases with 381 individuals experimentally data and used four ML algorithms containing k-nearest neighbor algorithm (kNN), Support Vector Machine (SVM), Random Forest regression (RF), and AdaBoost regression to predict the biodiesel yield using transesterification reaction. The Random Forest regression can be more suitable to accurately predict biodiesel yield than other three ML models due to presenting a lower RMSE values for both Training (2.778) and Validation (5.178), and a higher correlation coefficient.

Automated summary

Biodiesel produced through transesterification is a promising and environmentally friendly fuel derived from biomass resources. However, its production is affected by factors such as feedstock type, reaction time, temperature, and catalyst. Machine learning algorithms, including k-nearest neighbor, Support Vector Machine, Random Forest regression, and AdaBoost regression, have been used to predict biodiesel yield, with Random Forest regression showing the most accurate predictions based on lower RMSE values and higher correlation coefficients.