Machine Learning-Based Assessment of the Influence of Nanoparticles on Biodiesel Engine Performance and Emissions: A critical review

Abstract

As researchers sought for new methods to decrease noxious emissions and improve engine performance, they discovered biodiesel as a promising biofuel. However, traditional study methodologies were deemed inadequate, prompting the need for computational methods to offer numerical solutions. This approach was seen as a creative and practical solution to the problem at hand. In response to the limitations of conventional modeling approaches, researchers turned towards the innovative solution of using machine-learning techniques as data processing systems. This creative approach has proven effective in addressing a broad variety of technical and scientific concerns, particularly in fields where traditional modeling approaches have fallen short of expectations. This review discusses using machine learning algorithms for predicting biodiesel performance and emissions with nanoparticles. Researchers have solved these problems with the application of machine learning to anticipate engine efficiency and emissions. The machine-learning algorithm predicts engine performance very precisely, proving its efficacy. Nanotechnology and biodiesel engine technologies are quickly advancing, making this review vital. Previous studies have examined nanoparticles' influence on engine performance and emissions. This review uniquely focuses on the application of machine learning techniques. Through the utilization of machine-learning algorithms, it is possible for gaining deeper understanding of intricate connections existing between the properties of nanoparticles and the behavior of engines. This methodology provides extensive comprehension of an impact of nanoparticles upon performance and emissions of biodiesel engines, hence enabling a development of more effectual and sustainable engine designs.