Evidence map›Paper›PMID 40664901›Full record

ArticleScientific reports2025

Investigation of spark ignition engine performance in ethanol-petrol blended fuels using artificial neural network.

Menelik Walle, Kumlachew Yeneneh, Gadisa Sufe

Abstract read
PubMed Full text at PMC Publisher
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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
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

3 authors.

Menelik WalleDepartment of Motor Vehicle Engineering, College of Engineering, Ethiopian Defence University, P.O. Box 1041, Bishoftu, Ethiopia.
Kumlachew YenenehDepartment of Motor Vehicle Engineering, College of Engineering, Ethiopian Defence University, P.O. Box 1041, Bishoftu, Ethiopia. kumynnh2023@gmail.com.
Gadisa SufeFaculty of Mechanical Engineering, Wrocław University of Science and Technology, Wrocław, 50-370, Poland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The increasing global cost of fossil fuels and growing environmental concerns have accelerated the search for sustainable energy alternatives, positioning bioethanol as a promising renewable fuel for spark-ignition (SI) engines. This study uniquely integrates ethanol-petrol blends (E0, E10, E20, and E30) with Artificial Neural Networks (ANNs) to address critical gaps in predictive modeling and fuel optimization. Experimental tests were conducted on a single-cylinder, four-stroke SI engine under constant load conditions, capturing data on engine speed, mass flow rate, combustion efficiency, peak cylinder pressure, brake-specific fuel consumption (BSFC), and exhaust gas temperature. A feed-forward backpropagation ANN model was developed using 75% of the collected data for training and 25% for validation, achieving high predictive accuracy with R

Indexed as

Artificial neural networks (ANNs)Brake-specific fuel consumption (BSFC)Combustion efficiencyEngine performance predictionEthanol–petrol blendsExhaust gas temperatureSpark-ignition engine

Identifiers

PMID40664901
PMCPMC12264062

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.