Machine learning-based prediction and SHAP sensitivity analysis of sound speed in hydrogen-rich gas mixtures.

Mohammad Rasool Dehghani, Ali Akbari, Mehdi Maleki, Maryam Aghel, Moein Kafi, Yousef Kazemzadeh

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

6 authors.

Mohammad Rasool DehghaniDepartment of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran. mrd3hghani@gmail.com.

Ali AkbariDepartment of Petroleum Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf University, Bushehr, Iran.

Mehdi MalekiDepartment of Petroleum Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf University, Bushehr, Iran.

Maryam AghelStudent Research Committee, Bushehr University of Medical Sciences, Bushehr, Iran.

Moein KafiDepartment of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran. moein.kafi.petro@gmail.com.

Yousef KazemzadehDepartment of Petroleum Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf University, Bushehr, Iran. yusefkazemzade@pgu.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The speed of sound is of particular importance in determining the physical and chemical properties of gas mixtures. Given the importance of this parameter in hydrogen transport and the high speed and accuracy of machine learning methods compared to traditional methods such as equations of state, this study estimates the speed of sound in hydrogen/cushion gas mixtures. For this purpose, 665 data sets of sound speed in hydrogen/cushion gas mixtures were collected from previous studies, and then 4 machine learning methods were used, including Linear Regression (LR), Extra Trees Regressor (ETR), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and K-Nearest Neighbor (KNN). The hyperparameters of these models were optimized using the Bayesian algorithm, and fivefold cross-validation was used to prevent overfitting. The ETR method had the best performance with R

Indexed as

Acoustic propertiesGas mixturesHydrogen transportMachine learning (ML)Speed of sound

Identifiers

PMID41258270

DOI10.1038/s41598-025-24505-7

PMCPMC12630880

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.