Utilizing Artificial intelligence to identify an Optimal Machine learning model for predicting fuel consumption in Diesel engines

Aug 2, 2026

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This study applies artificial intelligence (AI) to identify an optimal machine learning (ML) model for predicting dodecane fuel consumption in diesel combustion. Sensitivity analysis determines which parameters most strongly influence fuel consumption, while noise effects are addressed through data cleaning to improve result reliability. Prediction quality is validated using multiple metrics and validation procedures, including comparisons with computational fluid dynamics (CFD) outputs and experimental data. Neural networks, random forest regression, and Gaussian process regression (GPR) are compared, with GPR delivering the highest accuracy and faster prediction performance.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages17

Reading Time43 min

AccessFree to download

Introduction

ML-based fuel consumption prediction and limitations of traditional models

Related work and comparative studies of ML algorithms

Method and Validation

Sensitivity analysis and parameter influence assessment

Noise handling and data cleaning

Model comparison and evaluation metrics

Energy and AI 16 (2024) 100360 Contents lists available at ScienceDirect Energy and AI journal [homepage: www.sciencedirect.com/journal/energy-and-ai](homepage: www.sciencedirect.com/journal/energy-and-ai) Utilizing Artificial intelligence to identify an Optimal Machine learning model for predicting fuel consumption in Diesel engines * Amirali Shateri , Zhiyin Yang , Jianfei Xie School of Engineering, University of Derby, DE22 3AW, UK H I G H L I G H T S • An optimal ML model was identified to predict fuel consumptions in diesel engines. • Various parameters were determined to reflect fuel consumptions using sensitivity analysis. • Different ML models were performed to consider the complexity of fuel consumption prediction. • ML models demonstrated good prediction performance for both flow and heat transfer characteristics of fuel combustion. A R T I C L E I N F O Keywords: AI evaluation Machine learning Diesel engine Fuel consumption Decarbonization G R A P H I C A L A B S T R A C T | | | --- | | A B S T R A C T | This paper describes the utilization of artificial intelligence (AI) techniques to identify an optimal machine learning (ML) model for predicting dodecane fuel consumption in diesel combustion. The study incorporates sensitivity analysis to assess the impact levels of various parameters on fuel consumption, thereby highlighting the most influential factors. In addition, this study addresses the impact of noise and implements data cleaning techniques to ensure the reliability of the obtained results. To validate the accuracy of the predictions, the study performs several metrics and validation process, including comparisons with computational fluid dynamics (CFD) results and experimental data. Comprehensive comparisons are made among neural networks (NN), random forest regression (RFR), and Gaussian process regression (GPR) models, taking into account the complexity associated with fuel consumption predictions. The findings demonstrate that the GPR model outperforms the others in terms of accuracy, as evidenced by metrics such as mean absolute error (MAE), mean squared error (MSE), Pearson coefficient (PC), and R-squared (R2). The GPR model exhibits superior predictive ability, accurately detecting and predicting even individual data points that deviate from the overall trend. The significantly lower absolute error values also consistently indicate its higher accuracy compared with the NN and RFR models. Furthermore, the GPR model shows a remarkable speedup, approximately 1.7 times faster than * Corresponding author E-mail address: [j.xie@derby.ac.uk](j.xie@derby.ac.uk) (J. Xie). [https://doi.org/10.1016/j.egyai.2024.100360](https://doi.org/10.1016/j.egyai.2024.100360) Available online 18 March 2024 2666-5468/© 2024 University of Derby. Published by Elsevier Ltd. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)). A. Shateri et al. Energy and AI 16 (2024) 100360 traditional CFD solvers, and physically captures the momentum and thermal characteristics in a surface field prediction. Finally, the target optimization is assessed using the Euclidean distance as a fitness function, ensuring the reliability of predicted data. 1. Introduction Diesel combustion is a critical process in internal combustion engines, and optimizing its performance is of great importance to achieve high fuel efficiency, less emissions and improved overall thermal performance. Traditional approaches to fuel consumption predictions mainly rely on semi-empirical formulas or physical models. However, these methods often lack accuracy and are limited in their ability to capture complex relations between engine parameters and fuel consumption [1–7]. To address these limitations, ML models have emerged as a promising alternative for fuel consumption predictions. ML models can learn complex patterns and

FAQs

Which ML model delivers the best prediction performance for fuel consumption?

Gaussian process regression (GPR) outperforms neural networks and random forest regression, achieving lower MAE and MSE and stronger R-squared and Pearson correlation results.

How does the study handle the influence of different parameters on fuel consumption?

Sensitivity analysis is used to assess how various parameters affect fuel consumption, highlighting the most influential factors.

How is prediction accuracy validated in the research?

Accuracy is validated with multiple metrics and validation steps, including comparisons against computational fluid dynamics (CFD) results and experimental data.

Aug 2, 2026

Upload by Aurora