4.7 Article

Functionalized chitosan-magnetic flocculants for heavy metal and dye removal modeled by an artificial neural network

Journal

SEPARATION AND PURIFICATION TECHNOLOGY
Volume 282, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.seppur.2021.120002

Keywords

Magnetic flocculant; Artificial neural network model; Magnetic flocculation; Heavy metal wastewater

Funding

  1. National Natural Science Foundation of China [51508268]
  2. Natural Science Foundation of Jiangsu Province in China [BK20201362]
  3. 2018 Six Talent Peaks Project of Jiangsu Province [JNHB-038]

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In this study, an amphoteric magnetic chitosan-based flocculant was prepared and its flocculation performance and mechanism were investigated. The material showed excellent removal rates of Cu(II) and DB56, and maintained stable performance even after consecutive regeneration/flocculation cycles.
In this study, an amphoteric magnetic chitosan (CS)-based flocculant MFe3O4@CS-g-PIA was prepared from CS, Fe3O4, and itaconic acid (IA), and its apparent morphology and characteristic structure were systematically studied. The flocculation performance and mechanism of the fabricated material were also investigated in different pollution systems, and the effects of total monomer concentration, m(CS):m(IA), IA pre-neutralization degree, reaction temperature, reaction time, and initiator concentration on the synthesis of MFe3O4@CS-g-PIA were studied. Characterization results showed that MFe3O4@CS-g-PIA forms a three-dimensional network with excellent magnetic induction. The optimal removal rates of Cu(II) and Disperse Blue 56 (DB56; 90.2% and 97.0%, respectively) were obtained under the conditions of 150 mg.L- 1 MFe3O4@CS-g-PIA, pH 6.0, and 300 rpm stirring speed. MFe3O4@CS-g-PIA maintained removal rates of over 80.0% for Cu(II) and DB56 after five consecutive cycles of regeneration/flocculation and demonstrated excellent acid resistance stability. Changes in the particle size distribution, fractal dimensions, and zeta potentials of the flocs indicated that the relevant flocculation mechanism involves the synergistic functions of chelation, charge neutralization, and adsorption bridging. An artificial neural network model was finally established on the basis of the experimental flocculation data to predict the removal rates of Cu(II) (R = 0.97) and DB56 (R = 0.98) accurately.

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