Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Bilal, Faris S.; Elumalai, P.V.; Kiran Kavalli; Mishra, Nirmith Kumar; Chan, Choon Kit; Saleel, C Ahamed; Hussain, Fayaz; Khan, Sher Afghan; Keba?, Ali Title A name given to the resource Hydrogen-enriched dual-fuel CI engine fueled with Mahua biodiesel and hybrid nano-additives: Integrated experiments, explainable machine learning, and multi-objective optimization Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived International Journal of Hydrogen Energy;Volume;235;Issue;;Article No.;155037; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.ijhydene.2026.155037" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ijhydene.2026.155037</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036359871?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036359871?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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&gt;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. Subject The topic of the resource Energy efficiency; Explainable artificial intelligence; Hybrid metal-oxide nanoparticles; Hydrogen dual-fuel CI engine; Mahua biodiesel; Multi-objective grey Wolf optimization; SHAP feature attribution Publisher An entity responsible for making the resource available Elsevier Ltd Relation A related resource ISSN: 3603199; CODEN: IJHED Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online