Prediction of Biodiesel Production from Algae Oil by Different Algorithm in Machine Learning

Biodiesel emerged as a renewable and clean energy source with potential to decline earth warming. So, data-driven machine learning system for biodiesel prediction via regression is investigated in study. Support vector machines, Linear regression, regression trees, and Gaussian process regression with various functions are among the regression algorithms used. The developed models were evaluated using a variety of performance indicators (such as R2, residual analysis, MAE, and RMSE), as well as 5-fold cross validation. GPR model with Matérn class best results with R2 of 0.91 and 4.1134 RMSE. The built models demonstrate performance using various algorithms. As a result, proposed approach would certify fast calculation of biodiesel yield from algal oil, potentially reducing time-consuming, expensive, and labor-intensive laboratory testing.