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AuthorLee ,Yit Kwan
AuthorTey ,Wah Yen
AuthorJawad ,Zeinab Abbas
AuthorYeap ,Swee Pin
Available date2024-04-22T04:57:06Z
Publication Date2022
Publication NameChemical Engineering Communications
AbstractThe presence of fine particles has reduced the quality of water resources. Unfortunately, the fine particles normally take a longer time to be removed. Realizing this, the present study aims to employ a magnetic adsorbent to assist a fast separation of the fine particles from their solution. Here, micron-sized SiO2 particles were used as the model fine particles; meanwhile, the magnetic adsorbent was designed by functionalizing iron oxide nanoparticles (IONPs) with chitosan. A process study was done based on the effects of medium pH, IONPs concentration, and contact time. Results implied that both bare IONPs and chitosan functionalized-IONPs (denoted as CF-IONPs) were best in removing SiO2 at pH 4 owing to the electrostatic attraction force. However, as compared to the bare counterpart, CF-IONPs is more suitable for large scale application as it is effective in a wider pH range, requires lesser adsorbent dosage and shorter adsorption time. Despite that, removal of SiO2 using both types of adsorbents were found to follow pseudo-second order kinetic. The obtained data was modeled using either the moving least squares (MLS) or multivariable power least squares (MPLS) method. In particular, the indices of each parameter obtained from the MPLS-generated equation indicated that the most dominant parameter that governing the SiO2 removal by bare IONPs is medium pH, while the one for CF-IONPs is contact time.
SponsorOpen Access funding provided by the Qatar National Library. The authors express the gratitude to UCSI University for financial support through Research Excellence & Innovation Grant (REIG) with project code REIG-FETBE-2020/015.
PublisherTaylor and Francis Ltd.
SubjectFine particles
magnetic nanoparticles
moving least squares method
multivariable power least squares method
water treatment
TitleMagnetic separation of micron-sized particles: process study and regression modelling using moving least squares and multivariable power least squares method
Issue Number12
Volume Number209

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