Evidence map›Paper›PMID 41120305›Full record

ArticleScientific reports2025

Machine learning predictions for enhancing engine performance and emission using aluminum oxide nano additives in castor biodiesel.

Murugu Nachippan, P Pathmanabhan, Beemkumar Nagappan, Vijay J Upadhye, Nandagopal Kaliappan, V Balaji, K Kamakshi Priya

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Murugu NachippanDepartment of Automobile Engineering, Easwari Engineering College, Chennai, Tamilnadu, India.
P PathmanabhanDepartment of Automobile Engineering, Easwari Engineering College, Chennai, Tamilnadu, India.
Beemkumar NagappanDepartment of Mechanical Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bengaluru, Karnataka, 562112, India.
Vijay J UpadhyeParul Institute of Applied Scienes, Parul University, PO Limda, Tal. Waghodia, District Vadodara, Gujarat, India.
Nandagopal KaliappanDepartment of Mechanical Engineering, Haramaya Institute of Technology, Haramaya University, Dire Dawa, Ethiopia. raghugavaskarr@gmail.com.
V BalajiDepartment of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India.
K Kamakshi PriyaDepartment of Physics, Saveetha School of Engineering, SIMATS, Saveetha University, Chennai, Tamil Nadu, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigates the role of aluminum oxide nano-additives in enhancing the performance and reducing the environmental footprint of a B30 castor biodiesel blend in a compression ignition (CI) engine, emphasizing principles of green and sustainable chemistry. With global concerns over emissions and the depletion of fossil fuels, there is an urgent need for cleaner and more efficient alternative fuels. Biodiesel derived from non-edible sources, such as castor oil, presents a sustainable solution, but improvements in its combustion efficiency and emissions profile are crucial for widespread adoption. In this research, aluminum oxide nanoparticles were incorporated into a B30 biodiesel blend to enhance combustion properties, reduce ignition delay, and significantly mitigate harmful emissions, including carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxides (NOx). The biodiesel was synthesized through the transesterification of castor oil, which is rich in ricinoleic acid, and tested on a Kirloskar diesel engine operating at a constant speed of 1500 rpm under varying load conditions. Results demonstrated that the nano-additive-infused biodiesel blend outperformed conventional diesel in terms of fuel economy, atomization, vaporization, and overall combustion efficiency. Additionally, the use of aluminum oxide nanoparticles reduced brake-specific fuel consumption (BSFC) and pollutant emissions. To further optimize engine performance and minimize emissions, a machine learning framework was applied, comparing algorithms such as Random Forest and XGBoost. The analysis identified XGBoost as the most accurate predictive tool, offering valuable insights for optimizing engine parameters. This work highlights the application of green and sustainable chemistry through the integration of nano-additives and advanced data analytics to develop cleaner, more efficient biodiesel fuels, contributing to the global shift toward environmentally friendly energy solutions.

Indexed as

Aluminium oxideCastor biodieselClean energyMachine learning modelRandom forestXG Boost

Identifiers

PMID41120305
PMCPMC12540879

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

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.