A Review of the artificial neural network’s roles in alternative fuels: Optimization, prediction, and prospects

Artificial Neural Networks (ANN) are increasingly employed in alternative fuels to enhance efficiency and mitigate environmental impacts. This article comprehensively reviews the application of ANNs in modeling, optimizing, and predicting the properties of various alternative fuels. ANNs excel at capturing the complex non-linear relationships inherent in these fuels' physicochemical properties and combustion processes, which can be challenging to forecast using traditional mathematical models. By leveraging ANNs, combustion parameters can be optimized, thereby improving fuel efficiency, reducing exhaust emissions, and enhancing overall engine performance. Additionally, this research explores the effective use of ANNs in forecasting engine performance and emissions for alternative fuels, while also addressing key challenges, including the need for high-quality data and the optimization of algorithms for better accuracy. Additionally, the article considers the future potential of ANNs in supporting sustainable energy development and facilitating the transition to a green fuel economy. With advancements in computing technology, ANNs are anticipated to remain a vital instrument in the progression of alternative fuel research and its associated applications.