Evidence map›Paper›PMID 40753334›Full record

ArticleScientific reports2025

A machine learning approach for significant utilization of high-ash Indian coals by metal chloride modification.

Aparna Singh, Deepak Singh Panwar, Satish Kumar Maurya, Ajay Oraon, Sanjeev Srivastava, Md Irfanul Haque Siddiqui, Saurav Dixit, Choon Kit Chan, Chandrakant Sonawane

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. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Aparna SinghWA School of Mines, Minerals, Energy and Chemical Engineering, Curtin University, Perth, Australia. Aparna.s21192@gmail.com.
Deepak Singh PanwarDepartment of Chemical Engineering, P P Savni University, Surat, Gujarat, India.
Satish Kumar MauryaDepartment of Computer Engineering and Applications, G.L.A. University, Mathura, UP, India.
Ajay OraonDepartment of Chemical Engineering, BIT Sindri, Dhanbad, India.
Sanjeev SrivastavaDepartment of Mechanical Engineering, DAV, IET, Daltonganj, India.
Md Irfanul Haque SiddiquiDepartment of Mechanical Engineering, College of Engineering, King Saud University, Riyadh, 12372, Saudi Arabia.
Saurav DixitCentre of Research Impact and Outcome, Chitkara University, Rajpura, Punjab, 140417, India.
Choon Kit ChanFaculty of Engineering and Quantity Surveying, INTI International University, Nilai, Negeri Sembilan, Malaysia.
Chandrakant SonawaneDepartment of Mechanical Engineering, Symbiosis International University, Pune, India.

Funding

king saud University ORF-2025-999
6 · The paper itself

Abstract

A continuous and dependable energy supply is essential for maintaining a nation's economic stability. Globally, coal ranks as the second largest fossil fuel resource after oil and gas, leading to the establishment of coal-fired power infrastructure. Nonetheless, the pyrolysis and "burn-out" reactions of High-ash coal impose fundamental limitations that hinder its efficient use and exacerbate environmental degradation. Coal pyrolysis processes is significantly influenced by numerous experimental factors, including the, chemical concentration, operating temperature, process time. A significant weight loss was seen for periods of up to 30 min at 510 °C; yet, the change in responsiveness reduced after this time. It was found that as an increasing the concentration of SnCl

Indexed as

ANN% Burn-outHigh-ash coalProcess innovationPyrolysisRSMTGA

Identifiers

PMID40753334
PMCPMC12318060

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.