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

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.

KEYWORDS:

• Machine learning
• VCR engine
• performance & emissions
• JME-ZnO Nanoemulsion
• multi-target regression

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.

Notes on contributors

Chiranjeeva Rao Seela

Chiranjeeva Rao Seela is an Associate Professor in the Department of Mechanical Engineering at GMR Institute of Technology (GMRIT), India. He obtained his Ph.D. from Acharya Nagarjuna University, marking the culmination of his academic journey. With a blend of teaching and industry experience spanning 18.5 years. His scholarly contributions are evident through his 33 publications in reputable journals such as Elsevier, Springer, Taylor and Francis Ltd., and Emerald Publishing Ltd.

Lakshmana Rao Kalabarige

Lakshmana Rao Kalabarige is an Associate Professor in the Department of Computer Science Engineering at GMR Institute of Technology (GMRIT). With a Ph.D. from GITAM University, India, he brings a strong academic background to his role. Over his 18 years of teaching experience, he has made significant contributions to the field of machine learning, computer vision, and deep learning. His research prowess is evident in his 20 publications in reputed journals.

Siva Prasad Kattela

Siva Prasad Kattela is a distinguished academician and researcher in the field of Mechanical Engineering. Armed with a Ph.D. from the prestigious NIT Warangal, he has accumulated over 15 years of enriching teaching experience. His scholarly contributions shine through his 18 publications in esteemed journals, marking him as a prominent figure in academia. Currently serving as an Assistant Professor in the Department of Mechanical Engineering at GMR Institute of Technology, India