Hydrogen-enriched dual-fuel CI engine fueled with Mahua biodiesel and hybrid nano-additives: Integrated experiments, explainable machine learning, and multi-objective optimization

Bilal, Faris S. and Elumalai, P.V. and Kavalli, Kiran and Mishra, Nirmith Kumar and Chan, Choon Kit and Saleel, C Ahamed and Hussain, Fayaz and Khan, Sher Afghan and Keçebas, Ali (2026) Hydrogen-enriched dual-fuel CI engine fueled with Mahua biodiesel and hybrid nano-additives: Integrated experiments, explainable machine learning, and multi-objective optimization. International Journal of Hydrogen Energy, 235 (155037). pp. 1-25. ISSN 0360-3199 E-ISSN 1879-3487

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Official URL: https://www.sciencedirect.com/science/article/pii/...

Abstract

Hydrogen-enriched dual-fuel compression-ignition (CI) engines are a potential pathway towards higher efficiency and lower carbon-intensive emissions. Studies conducted so far have considered hydrogen enrichment, biodiesel fuels, nano-additives, and data-driven optimization as separate entities; hence, there is no integration or comprehensive understanding of them, which leads to an efficiency-nitrogen oxides trade-off. This study presents an integrated experimental-machine learning-explainable artificial intelligence-multi-objective optimization framework for a hydrogen-assisted dual-fuel CI engine fueled with a Mahua biodiesel-diesel (B20) blend and hybrid nano-additives (Al2O3–TiO2 and CeO2-MWCNT, 50-100 ppm). Experimental results indicated that hydrogen-enriched hybridization with nano-additives improves brake thermal efficiency by 8-14% and reduces brake-specific fuel consumption by 10-18%. HC, CO, and smoke emissions are reduced by up to 35%, 32%, and 45%, respectively. There is a moderate increase in NOx by 12-28%. Machine-learning models achieved high predictive accuracy (R2 > 0.99). The XGBoost exhibited superior generalization. The SHapley Additive exPlanations analysis found that the dominant factors were engine load, the hydrogen energy share, and the concentration of nano additives. The XGBoost-Multi-Objective Grey Wolf Optimizer (XGB–MOGWO) framework created Pareto-optimal solutions showing a strong and interpretable pathway for advancing trade-offs between efficiency and emissions in dual-fuel engines.

Item Type:	Article (Journal)
Subjects:	T Technology > TJ Mechanical engineering and machinery > TJ255 Heat engines
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Mechanical Engineering
Depositing User:	Prof. Dr. Sher Afghan Khan
Date Deposited:	06 May 2026 12:21
Last Modified:	06 May 2026 12:21
Queue Number:	2026-04-Q3029
URI:	http://irep.iium.edu.my/id/eprint/128599

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