Evidence map›Paper›PMID 40883521›Full record

ArticleScientific reports2025

Optimization of needle punched nonwoven filter media for enhanced dust filtration performance.

Keivan Naderi, Parham Soltani, Yu Song

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.

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1 · What the graph read from it

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Keivan NaderiDepartment of Textile Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.
Parham SoltaniDepartment of Textile Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran. pa.soltani@iut.ac.ir.
Yu SongEngineering Research Center of Technical Textile, Ministry of Education, College of Textiles, Donghua University, Shanghai, 201620, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nonwoven fabrics play a critical role in filtration applications due to their unique structural properties, including high porosity, irregular structure, and customizable fiber arrangements. These characteristics make them ideal for various filtration purposes, from industrial air filters to indoor air filters for HVAC and engine air filters for automobiles. To this end, polypropylene fibers were spun at two different finenesses (1.5 and 3 denier) using an industrial-scale melt-spinning machine. The nonwoven filter media were then fabricated based on an I-optimal design response surface methodology (RSM-I-optimal) method using a semi-industrial needle punching line. The process parameters considered include the blending ratio of 1.5 denier fibers (0-100%), needle penetration depth (9-16 mm), needling strokes (110-430 strokes/min), calendering temperature (120-140 °C), and calendering pressure (2-6 bar). The 3D microstructure of the samples was analyzed using X-ray µCT, and the morphology was examined through image analysis. Filtration performance, including efficiency, pressure drop, and quality factor, was characterized according to ISO 11,057. The influence of input variables on filtration performance was evaluated using ANOVA, leading to the development of quadratic models for predicting efficiency, pressure drop, and quality factor. The interaction effects of the process parameters were also studied. Optimal conditions were determined to maximize efficiency and quality factor while maintaining pressure drop below 70 Pa. Under these conditions, the predicted values of efficiency, pressure drop, and quality factor were 94.99%, 61.07 Pa, and 0.0456 1/Pa, respectively. Subsequently, four filter media were manufactured under optimal conditions, and their performance was assessed. The experimental results yielded efficiency, pressure drop, and quality factor of 93.37%, 62.52 Pa, and 0.0434 1/Pa, respectively. Additionally, a numerical dust loading simulation was performed through the realistic 3D structure of the optimized filter medium. The optimized filter exhibited good filtration performance in experiments, modeling, and CFD. This research demonstrates that by carefully optimizing process parameters, the filtration performance of needle punch nonwoven fabrics can be significantly enhanced, making them more effective for industrial filtration applications.

Indexed as

Filtration performanceMicrostructural properties, numerical simulationNeedled nonwoven fabricProcess parametersRSM-I-optimalX-ray micro-computed tomography

Identifiers

PMID40883521
PMCPMC12397395

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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.