Modeling and optimization of vegetable oil biodiesel production with heterogeneous nano catalytic process: Multi-layer perceptron, decision regression tree, and K-Nearest Neighbor methods

Abstract

Biodiesels are the renewable diesel fuels prepared from natural sources. As the production cost of biodiesels stands for the major problem for commercialization therefore in this work the Machine Learning (ML) approaches were used to simulate and optimize the biodiesel production process. Modeling biodiesel production with ML is possible without an in-depth understanding of biological systems. Here, a novel approach based on the K-Nearest Neighbor (KNN) regression, Decision Regression Tree (DT), and Multi-layer perceptron (MLP), have been suggested to predict biodiesel production yield (%) from Soybean oil through transesterification process as a function of molar ratio of methanol to oil and catalyst loading (wt.%). The performance of models was compared, and all models showed high accuracy and R2 value higher than 0.9. MLP, DT, and KNN models represented a high performance with a RMSE (root mean square error) of 4.9460E−01, 1.8596E+00, and 8.0422E−01, respectively. Although, all models were accurate for predicting the production process of biodiesel, the MLP model was found to be superior to other models in terms of its accuracy. The optimization of biodiesel production yield by MLP approach demonstrated 83.88% production yield using 10.67 molar ratio of methanol to oil and 3.45 wt% of catalyst loading.