TY - JOUR
T1 - Functionalized chitosan-magnetic flocculants for heavy metal and dye removal modeled by an artificial neural network
AU - Sun, Yongjun
AU - Yu, Yuanyuan
AU - Zhou, Shengbao
AU - Shah, Kinjal J.
AU - Sun, Wenquan
AU - Zhai, Jun
AU - Zheng, Huaili
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2022/2/1
Y1 - 2022/2/1
N2 - 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.
AB - 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.
KW - Artificial neural network model
KW - Heavy metal wastewater
KW - Magnetic flocculant
KW - Magnetic flocculation
UR - http://www.scopus.com/inward/record.url?scp=85118945766&partnerID=8YFLogxK
U2 - 10.1016/j.seppur.2021.120002
DO - 10.1016/j.seppur.2021.120002
M3 - 文章
AN - SCOPUS:85118945766
SN - 1383-5866
VL - 282
JO - Separation and Purification Technology
JF - Separation and Purification Technology
M1 - 120002
ER -