Evidence map›Paper›PMID 38707394›Full record

ArticleHeliyon2024

Managing energy transition alongside environmental protection by making use of AI-led butanol powered SI engine optimization in compliance with SDGs.

Muhammad Ali Ijaz Malik, Muhammad Usman, Muhammad Waqas Rafique, Sohaib Raza, Muhammad Wajid Saleem, Naseem Abbas, Uzair Sajjad, Khalid Hamid, Mohammad Rezaul Karim, Md Abul Kalam

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Muhammad Ali Ijaz MalikSchool of Civil and Environmental Engineering, FEIT, University of Technology Sydney, NSW, 2007, Australia.
Muhammad UsmanMechanical Engineering Department, Main Campus, University of Engineering and Technology Lahore, Pakistan.
Muhammad Waqas RafiqueMechanical Engineering Department, Main Campus, University of Engineering and Technology Lahore, Pakistan.
Sohaib RazaMechanical Engineering Department, Main Campus, University of Engineering and Technology Lahore, Pakistan.
Muhammad Wajid SaleemDepartment of Mechanical and Energy Engineering, De Montfort University Dubai, United Arab Emirates.
Naseem AbbasDepartment of Mechanical Engineering, Sejong University, Gwangjin-gu, Seoul, 05006, South Korea.
Uzair SajjadDepartment of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, Taipei, 10608, Taiwan.
Khalid HamidDepartment of Energy and Process Engineering, Norwegian University of Science and Technology, 7491, Trondheim, Norway.
Mohammad Rezaul KarimDepartment of Mechanical Engineering, College of Engineering, King Saud University, Riyadh, 11421, Saudi Arabia.
Md Abul KalamSchool of Civil and Environmental Engineering, FEIT, University of Technology Sydney, NSW, 2007, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Enormous consumption of fossil fuel resources has risked energy accessibility in the upcoming years. The price fluctuation and depletion rate of fossil fuels instigate the urgent need for searching their reliable substitute. The current study tries to address these issues by presenting butanol as a replacement for gasoline in SI engines at various speeds and loading conditions. The emission and performance parameters were ascertained for eight distinct butanol-gasoline fuel blends. The oxygenated butanol substantially increases engine efficiency and boosts power with lower fuel consumption. The carbon emissions were also observed to be lower in comparison with gasoline. Furthermore, the Artificial Intelligence (AI) approach was used in predicting engine performance running on the butanol blends. The correlation coefficients for the data training, validation, and testing were found to be 0.99986, 0.99942, and 0.99872, respectively. It was confirmed that the ANN predicted results were in accordance with the established statistical criteria. ANN was paired with Response Surface Methodology (RSM) technique to comprehend the influence of the sole design parameters along with their statistical interactions controlling the responses. Similarly, the R

Indexed as

Artificial intelligenceButanol-gasoline blendsEngine performanceOptimizationStatistical approach

Identifiers

PMID38707394
PMCPMC11066330

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.