ItemArticle

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

Title
Hydrogen-enriched dual-fuel CI engine fueled with Mahua biodiesel and hybrid nano-additives: Integrated experiments, explainable machine learning, and multi-objective optimization
Creator
Bilal, Faris S.; Elumalai, P.V.; Kiran Kavalli; Mishra, Nirmith Kumar; Chan, Choon Kit; Saleel, C Ahamed; Hussain, Fayaz; Khan, Sher Afghan; Keba?, Ali
Description
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 about 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 (Al2O3TiO2 and CeO2-MWCNT, 50-100ppm). Experimental results indicated that hydrogen enrichment hybridized 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 (XGBMOGWO) framework created Pareto-optimal solutions showing a strong and interpretable pathway for advancing trade-offs between efficiency and emissions in dual-fuel engines. 2026 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Source
International Journal of Hydrogen Energy;Volume;235;Issue;;Article No.;155037;
Date
01-01-2026
Publisher
Elsevier Ltd
Subject
Energy efficiency; Explainable artificial intelligence; Hybrid metal-oxide nanoparticles; Hydrogen dual-fuel CI engine; Mahua biodiesel; Multi-objective grey Wolf optimization; SHAP feature attribution
Coverage
Bilal F.S., Department of Mechanical Engineering, Faculty of Engineering, Al Baha University, Al Baha, 65527, Saudi Arabia; Elumalai P.V., Department of Mechanical Engineering, Aditya University, Surampalem, 533437, India; Kiran Kavalli, Department of Mechanical and Automobile Engineering, CHRIST University, Bengaluru, 560074, India; Mishra N.K., Department of Aeronautical Engineering, MLR Institute of Technology, Hyderabad, India; Chan C.K., Department of Mechanical Engineering, INTI International University, Malaysia; Saleel C.A., Department of Mechanical Engineering, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia, Center for Engineering and Technology Innovations, King Khalid University, Abha, 61421, Saudi Arabia; Hussain F., Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Selangor, Malaysia; Khan S.A., Department of Mechanical and Aerospace Engineering, Faculty of Engineering, International Islamic University, Selangor, Kuala Lumpur, 53100, Malaysia; Keba? A., Department of Energy Systems Engineering, Technology Faculty, Mu?la S?tk? Koan University, Mente?e, Mu?la, 48000, Turkey
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 3603199; CODEN: IJHED
Format
online
Language
English
Type
Article

Citation

Bilal, Faris S.; Elumalai, P.V.; Kiran Kavalli; Mishra, Nirmith Kumar; Chan, Choon Kit; Saleel, C Ahamed; Hussain, Fayaz; Khan, Sher Afghan; Keba?, Ali, “Hydrogen-enriched dual-fuel CI engine fueled with Mahua biodiesel and hybrid nano-additives: Integrated experiments, explainable machine learning, and multi-objective optimization,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 17, 2026, https://archives.christuniversity.in/items/show/22302.

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