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Applicability of electrical resistance tomography to the analysis of fluid distribution in haemodialysis modules
Author(s) -
Paglianti Alessandro,
Marotta Gaspare,
Montante Giuseppina
Publication year - 2020
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.23826
Subject(s) - bundle , work (physics) , tomography , materials science , electrical resistance and conductance , fluid dynamics , flow (mathematics) , mass transfer , computational fluid dynamics , biomedical engineering , biological system , computer science , mechanics , mechanical engineering , geometry , mathematics , engineering , composite material , physics , optics , biology
Abstract This work aims to explore the applicability of electrical resistance tomography (ERT) in the analysis of fluid distribution in haemodialysis modules, which is not straightforward due to the complex geometry of the hollow fibre bundles and the small sizes of the modules. On the other hand, ERT is potentially a suitable and convenient technique for investigation in this field due to its cost‐effectiveness and capacity to perform measurements in opaque systems. After a preliminary estimation of the fibre bundle local distribution, the assessment of the technique is performed by observing the time evolution of the measured conductivity maps during the module filling and emptying operations with water and air, which are alternatively fed inside or outside the fibre bundle. Reliable conductivity maps are obtained by placing the module vertically or horizontally. Additional experimental data collected by feeding liquid mixtures of different sodium chloride concentrations show that the technique is suitable for detecting concentration variations, due to the mass transfer through the fibres, and flow maldistribution, due to the specific geometry of the module. From the preliminary results collected in this work, the technique appears to be adequate for the collection of data that can support the optimization of the module geometry and computational model validation.