Exploring multi-feedstock biodiesel-hydrogen synergies for enhanced diesel engine performance using hybrid AI techniques.

Shiv Lal, Mohd Parvez, Osama Khan, Khaled Alnamasi, Saad Arif, Ashok Kumar Yadav

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Shiv LalDepartment of Mechanical Engineering, Rajasthan Technical University, Kota, India.

Mohd ParvezDepartment of Mechanical Engineering, Al-Falah University, Faridabad, Haryana, India.

Osama KhanDepartment of Mechanical Engineering, College of Engineering, King Faisal University, 31982, Al-Ahsa, Saudi Arabia. okhan@kfu.edu.sa.

Khaled AlnamasiDepartment of Mechanical Engineering, College of Engineering, King Faisal University, 31982, Al-Ahsa, Saudi Arabia.

Saad ArifDepartment of Mechanical Engineering, College of Engineering, King Faisal University, 31982, Al-Ahsa, Saudi Arabia.

Ashok Kumar YadavSymbiosis Institute of Technology, Pune, Symbiosis International (Deemed University), Pune, India. ashok.yadav@sitpune.edu.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years of crude oil shortage and price rise, biodiesel has emerged as a new-age sustainable fuel, owing to its positives mainly providing lower amount of gas emissions and compatibility with prevailing petro-diesel engines. The contemporary study explores multiple feasible biodiesels (Mustard, Binola, Caster, Rapeseed, Canola, Peanut, Jatropha, and Thumba) based on composition and testing feasibility, so as to be inter-mixed with hydrogen gas, for improved engine performance and minimal effluents. Primary engine characteristics brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), and multiple effluents of diesel engine were experimentally explored and compared for specific fuel sets. A hybrid methodology comprising of machine learning (k-means clustering) and prediction models is employed to find the optimal biodiesel among the variants, validated against ASTM standards. BTE and BSFC show a strong negative correlation, CO and BSFC a positive correlation, CO₂ and BTE a positive one. With minimal prediction uncertainty, Canola-based oil emerged as the superior biodiesel among others with a RMSE = 1.0, MAPE = 2.95%, R

Indexed as

BiodieselDiesel engineEnergy efficiencyEngine performanceHydrogen gasMachine learning

Identifiers

PMID42185437

DOI10.1038/s41598-026-54790-9

PMCPMC13434391

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.