Investigation on optimization of biodiesel production using machine learning techniques
The efficiency and sustainability of biodiesel manufacturing processes may be enhanced by machine learning, as demonstrated by the results derived from the best-fitted model.
Abstract
One crucial step towards commercialization is the research of machine learning techniques for producing biodiesel. The current study uses experimental data to construct various models utilizing machine learning methods. With an 89.6% biodiesel production, the optimum transesterification temperature and duration were 600â—¦C, 5.95% catalyst concentration, 15:1 methanol to oil molar ratio, 55.9 â—¦C temperature and 77 min time. These results were derived from the best-fitted model. The efficiency and sustainability of biodiesel manufacturing processes may be enhanced by machine learning, as demonstrated by our findings. This successful application underscores the potential of advanced analytical techniques in driving innovation and sustainability within the renewable energy sector.