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AI based optimization of injection pressure for hydrogen and spirogyra biodiesel dual fuel engine to enhance combustion performance and emission characteristics
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  • Published: 10 February 2026

AI based optimization of injection pressure for hydrogen and spirogyra biodiesel dual fuel engine to enhance combustion performance and emission characteristics

  • S. Aravind1,
  • Debabrata Barik1,2,
  • Prabhu Paramasivam3,
  • Dhinesh Balasubramanian4,
  • Utku Kale5,6 &
  • …
  • Artūras Kilikevičius6 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Energy science and technology
  • Engineering

Abstract

The principal objective of this research is to employ modern machine learning techniques to optimize high-pressure biofuel injection strategies for sustainable energy applications. An engine powered with biofuel and hydrogen (H₂) under dual-fuel (DF) mode was tested under a varied fuel injection pressure range from 180 to 240 bar for optimization and modeling. The results demonstrate that an injection pressure of 220 bar produces enhanced engine performance. At this pressure, enhancements were noted in combustion characteristics, efficiency, and emission levels. The ignition delay (ID) at 220 bar injection pressure was 9.4% longer than at 240 bar injection pressure. The 220 bar IP mix demonstrated reduced peak cylinder pressure (PCP) and heat release rate (HRR) compared to the 240 bar. A 12.4% rise in brake-specific fuel consumption (BSFC) was observed at 220 bar inlet pressure. Nevertheless, although brake thermal efficiency (BTE) increased with increasing injection pressure (IP), the increase at 220 bar was somewhat less than that at 240 bar. Despite elevated nitrogen oxide (NOx) emissions with the 220 bars compared to pure diesel, carbon monoxide (CO) and hydrocarbon (HC) emissions were markedly decreased. Smoke emissions were reduced with the 220 bars in comparison to diesel and other fuel combinations. Three machine learning models were employed to establish a predictive control framework. The decision tree (DT) model had the greatest accuracy, with R² values of 0.9792 for PCP and 0.9710 for HC, alongside near-zero MAPE for BTE and HC This study underscores the potential of AI-driven biofuel optimization for fostering sustainable transportation and renewable fuel strategies, paving the way for large-scale adoption of low-carbon, high-efficiency energy solutions.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

H2 :

Hydrogen

DF:

Dual-fuel

IP:

Injection pressure

ID:

Ignition delay

PCP:

Peak cylinder pressure

HRR:

Heat release rate

BSFC:

Brake-specific fuel consumption

BTE:

Brake thermal efficiency

EGT:

Exhaust gas temperature

NOx:

Nitrogen oxides

CO:

Carbon monoxide

DT:

Decision tree

R2 :

Coefficient of determination

MAPE:

Mean absolute percentage error

Wi:

Wilmott’s index

LR:

Linear regression

RF:

Random forest

bTDC:

Before top dead center

DI CI:

Direct injection compression ignition

CRDi:

Common rail direct injection

IMEP:

Indicated mean effective pressure

ML:

Machine learning

CV:

Calorific value

ARCNP:

Advanced renewable catalytic nano particles

DTBP:

Di-tert-butyl peroxide

ASTM:

American Society for Testing and Materials

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Funding

Open access funding provided by Budapest University of Technology and Economics. The authors sincerely thank the Karpagam Academy of Higher Education (KAHE), Coimbatore, India, for providing facilities to carry out the research to complete the present study. This project has received funding from the Research Council of Lithuania (LMTLT), agreement No S-PD-24-180.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Karpagam Academy of Higher Education, Coimbatore, 641021, India

    S. Aravind & Debabrata Barik

  2. Centre for Energy and Environment, Karpagam Academy of Higher Education, Coimbatore, 641021, India

    Debabrata Barik

  3. Centre of Research Impact and Outcome, Chitkara University, Rajpura, Punjab, 140401, India

    Prabhu Paramasivam

  4. Department of Port Engineering, Lithuanian Maritime Academy (LMA), Vilnius Gediminas Technical University, Klaipėda, Lithuania

    Dhinesh Balasubramanian

  5. Department of Aeronautics and Naval Architecture, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3., Budapest, H-1111, Hungary

    Utku Kale

  6. Mechanical Science Institute, Vilnius Gediminas Technical University, Plytinės g. 25, Vilnius, 10105, Lithuania

    Utku Kale & Artūras Kilikevičius

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  1. S. Aravind
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Contributions

S.A., D.B., P.P., D.B., K.U., and A.K. wrote the main manuscript text. All authors reviewed the manuscript.

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Correspondence to Debabrata Barik, Dhinesh Balasubramanian or Utku Kale.

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Aravind, S., Barik, D., Paramasivam, P. et al. AI based optimization of injection pressure for hydrogen and spirogyra biodiesel dual fuel engine to enhance combustion performance and emission characteristics. Sci Rep (2026). https://doi.org/10.1038/s41598-025-34179-w

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  • Received: 19 July 2025

  • Accepted: 25 December 2025

  • Published: 10 February 2026

  • DOI: https://doi.org/10.1038/s41598-025-34179-w

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Keywords

  • Sustainable biofuels
  • Hydrogen energy
  • AI-Driven calibration
  • Renewable energy
  • Machine learning for biofuels
  • Eco-Friendly fuel optimization
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