Review on Machine Learning as a Key Technology Enabler for Sustainable Biodiesel Production

Abstract

The increasing scarcity of fossil fuel resources has created a significant need for a clean, inexpensive, and sustainable energy source. Biodiesel, a kind of liquid biofuel, has been discovered to mitigate environmental deterioration, improve engine efficiency, and decrease the release of harmful gases. Biodiesel production and usage processes are complicated and nonlinear, requiring rapid and accurate modelling tools for design, optimisation, and monitoring. Machine learning or other forms of artificial intelligence have been found to be a superior method for modelling biodiesel production. This is due to its ability to make accurate predictions, which is inspired by the auto learning and self-improving capabilities of the brain. Uses of machine learning in biodiesel production range from quality prediction and optimisation to monitoring of process conditions and output quantification. Furthermore, the integration of AI-based solutions from Industry 4.0 with human intelligence is crucial in the context of Industry 5.0 for the biodiesel industry to increase production efficiency, guarantee economic viability, and foster sustainability. This combination facilitates the exploration of reaction mechanisms, specifically in the domain of advanced biodiesel production.