Optimization and modeling of sulfur removal from liquid fuel using carbon-based adsorbents through synergistic application of RSM and machine learning.

Karim Maghfour Sarkarabad, Mohsen Shayanmehr, Ahad Ghaemi

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. 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

3 authors.

Karim Maghfour SarkarabadSchool of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, Tehran, Iran.

Mohsen ShayanmehrSchool of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, Tehran, Iran.

Ahad GhaemiSchool of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, Tehran, Iran. aghaemi@iust.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This research investigates the adsorption desulfurization of liquid fuels using artificial neural networks (ANN) and response surface methodology (RSM) approaches. The effectiveness of sulfur removal was predicted by analyzing five important factors: temperature, concentration, surface area, fuel/adsorbent, and time. We employed radial basis function (RBF) and multilayer perceptron (MLP) algorithms for ANN modeling. The optimal MLP configuration, utilizing the Levenberg-Marquardt (Trainlm) algorithm, consisted of three hidden layers with 20, 17, and 9 neurons, respectively, while the optimal RBF network contained 43 neurons. The MLP network's determination coefficient (R

Indexed as

Activated carbonAdsorptive desulfurizationArtificial neural networkResponse surface methodology

Identifiers

PMID39910113

DOI10.1038/s41598-025-88434-1

PMCPMC11799450

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LicenceCC BY-NC-ND

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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.