4.6 Article

Magnetic seeded filtration for the separation of fine polymer particles from dilute suspensions: Microplastics

Journal

CHEMICAL ENGINEERING SCIENCE
Volume 207, Issue -, Pages 1278-1287

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ces.2019.07.052

Keywords

Magnetic seeded filtration; Microplastic; Hetero agglomeration; Solid-liquid separation

Funding

  1. DFG (German Research Foundation) in the priority program 2045 Highly specific and multidimensional fractionation of fine particle systems with technical relevance

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Microplastics have become a highly discussed topic: Whether through the decomposition of larger plastic waste or direct usage on a small particle scale, microplastics find their way into the natural ecosystem with uncertain consequences: In particular, the ingestion and therefore the introduction of these particles into the food web (often carrying adsorbed chemicals) poses still not fully understood hazards for mankind. This work focuses on the separation of such particles from aquatic suspensions and evaluates the viability of magnetic seeded filtration in this field. In this study it could be shown that it is generally possible to separate fine polymeric particles from dilute suspensions in a highly effective manner. A broad range of process conditions were investigated with separation efficiencies reaching up to 95%. The pH and ultimately the surface charge of the particles were identified to be the crucial process relevant parameters. More exactly, the surface potentials should be either small or oppositely charged leading to a diminishing or even attractive electrostatic interaction. However, when both agglomeration partners exhibit an electrostatic repulsion, a thorough parameter optimization nevertheless yielded high separation efficiencies. In particular, the ionic strength and magnetic seed concentration proved to be determining factors in this case. Lastly, the process kinetics were investigated and a limited exponential growth character was observed which could be modeled with basic equations from agglomeration theory under simplifying assumptions. This knowledge further assists the selection of an optimal parameter set for a process engineering application. (C) 2019 Elsevier Ltd. All rights reserved.

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