Evidence map›Paper›PMID 40710754›Full record

ArticleMembranes2025

Study on Separation of Desulfurization Wastewater in Ship Exhaust Gas Cleaning System with Rotating Dynamic Filtration.

Shiyong Wang, Juan Wu, Yanlin Wu, Wenbo Dong

Abstract read
In one paragraph

Article in Membranes, 2025. 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.

Shiyong WangDepartment of Environmental Science and Engineering, Fudan University, Shanghai 200433, China.
Juan WuShanghai Research Institute of Chemical Industry Co., Ltd., Shanghai 200062, China.
Yanlin WuSchool of Resources and Environmental Engineering, Shanghai Polytechnic University, Shanghai 201209, China.ORCID 0000-0001-7535-7504
Wenbo DongDepartment of Environmental Science and Engineering, Fudan University, Shanghai 200433, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Current treatment methods for desulfurization wastewater in the ship exhaust gas cleaning (EGC) system face several problems, including process complexity, unstable performance, large spatial requirements, and high energy consumption. This study investigates rotating dynamic filtration (RDF) as an efficient treatment approach through experimental testing, theoretical analysis, and pilot-scale validation. Flux increases with temperature and pressure but decreases with feed concentration, remaining unaffected by circulation flow. For a small membrane (152 mm), flux consistently increases with rotational speed across all pressures. For a large membrane (374 mm), flux increases with rotational speed at 300 kPa but firstly increases and then decreases at 100 kPa. Filtrate turbidity in all experiments complies with regulatory standards. Due to the unique hydrodynamic characteristics of RDF, back pressure reduces the effective transmembrane pressure, whereas shear force mitigates concentration polarization and cake layer formation. Separation performance is governed by the balance between these two forces. The specific energy consumption of RDF is only 10-30% that of cross-flow filtration (CFF). Under optimized pilot-scale conditions, the wastewater was concentrated 30-fold, with filtrate turbidity consistently below 2 NTU, outperforming CFF. Moreover, continuous operation proves more suitable for marine environments.

Indexed as

back pressureEGC desulfurization wastewaterenergy consumptionfluxpilot-scale validationrotating dynamic filtrationshear forceturbidity

Identifiers

PMID40710754
PMCPMC12300965

What OpenQuestion holds

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LicenceCC BY
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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.