What question did this study set out to answer?

This research aims to develop a hybrid model to predict and optimize the performance and emissions of a butanol/diesel fueled CI engine.

May 26, 2026Open Access

Machine learning-driven optimization of performance and emissions in a butanol/diesel CI engine

KPK. Siva PrasadGITAM University TVT.R. VijaybabuGITAM University GKG. Kiran KumarAditya Birla (India)

Key Points

This research aims to develop a hybrid model to predict and optimize the performance and emissions of a butanol/diesel fueled CI engine.
Developed a hybrid modeling framework integrating experiments, CONVERGE CFD, and machine learning optimization.
Examined four engine parameters: Start of Injection (SOI), Compression Ratio (CR), Exhaust Gas Recirculation (EGR), and Fuel Injection Pressure (FIP).
Used Optuna to optimize an ensemble Meta Model for performance predictions.
Achieved R² values of 0.98 for ISFC and 0.97 for NOx, with RMSE below 5%.
Ensemble Meta Model showed the most accurate predictions for combustion and emission characteristics under varying conditions.
Demonstrated strong generalization in the model predictions.

Abstract

A hybrid modeling framework integrating experiments, CONVERGE CFD, and Optuna-optimized ensemble machine learning is developed to predict and optimize performance and emission characteristics of a DI-CI engine fueled with butanol/diesel blends. Machine learning was employed to model and predict key combustion and emission characteristics under varying engine operating conditions. This work presents a numerical and modeling examination of a DI-CI engine's performance and emission characteristics. CONVERGE CFD software was used to do the numerical analysis. Four distinct engine parameters were examined in this study: Start of Injection (SOI) (17 to 29° CA bTDC), Compression Ratio (CR) (14.5 to 19.5), Exhaust Gas Recirculation (EGR) (0 to 30%), and Fuel Injection Pressure (FIP) (200 to 280 bar). The Optuna-optimized ensemble Meta Model achieved the most accurate predictions, exhibiting the lowest RMSE and highest R² values for ISFC and NOₓ, along with reasonable accuracy for Soot estimation. The ensemble Meta Model achieved the best performance with R² values of 0.98 (ISFC) and 0.97 (NOx) and RMSE below 5%, demonstrating strong generalization.

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