Evidence map›Paper›PMID 39343810›Full record

ArticleScientific reports2024

Integration of experimental study and neural network modeling for estimating iron recovery in Davis tube tests.

Mohammad Tahami, Mohammad-Reza Mohammadi, Mahin Schaffie, Mohammad Ranjbar

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In one paragraph

Article in Scientific reports, 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

4 authors.

Mohammad TahamiDepartment of Mining Engineering, Shahid Bahonar University of Kerman, Kerman, Iran. tahami@eng.uk.ac.ir.
Mohammad-Reza MohammadiDepartment of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
Mahin SchaffieDepartment of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
Mohammad RanjbarDepartment of Mining Engineering, Shahid Bahonar University of Kerman, Kerman, Iran. m.ranjbar@uk.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Magnetic separation is a common procedure for the enrichment of magnetic iron ores. Davis tube (DT) test is a standard laboratory technique used to determine the optimum magnetic recovery of iron ore using wet low-intensity magnetic separators. However, the DT test is time-consuming and labour-intensive. In this study, based on the results of DT-tests, generalized regression (GRNN) and radial basis function (RBF) neural networks were developed to predict iron recovery through the Fe and FeO content of the feed. First, the DT tests were performed on 613 iron ore samples with varying Fe and FeO content. Then, neural networks were used to model the iron recovery from the DT test, using the Fe and FeO content of the feed as input data. The modeling results showed that GRNN is a better model for predicting iron recovery. The main statistical metrics indicated that GRNN has AAPRE, RMSE, and R

Indexed as

Artificial neural networksDavis tubeIron recoveryMagnetic separationModeling

Identifiers

PMID39343810
PMCPMC11439934

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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.