Machine learning-based modeling of variable compression ratio engine performance and emissions with JME-ZnO nanoemulsion

Abstract

ABSTRACT Diesel engines fueled with nanoemulsion of biodiesel and diesel have shown promising results in reducing emissions without significant engine problems. However, the evaluation of the impact of biodiesel-nano emulsion on engine performance and exhaust emissions requires time-consuming and costly experimental testing. Modelling simulations have gained attention as a useful approach to overcome these challenges. Artificial intelligence (AI) has an extensive variety of applications in various domains, including energy systems. By training computers using machine learning (ML), they can make better decisions and outperform humans. AI-based models offer promising prospects in energy generation predictions. However, these prediction models can be computationally demanding. In this paper, the authors propose a Boosting-based Multi-Target Regression model using ML techniques to predict the pursuance and exhaust of a diesel engine. The study aims to advance the understanding of enhanced engine performance through the application of ML models. The MTXGB model demonstrates high average accuracy (high R2-score of 0.9989) and low error rates of 0.0123 for all six targets.