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

Abstract

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.