Cetylpyridinium bromide assisted micellar-enhanced ultrafiltration for treating enrofloxacin-laden water

被引:29
作者
Chowdhury, Somnath [1 ]
Halder, Gopinath [1 ]
Mandal, Tamal [1 ]
Sikder, Jaya [1 ]
机构
[1] Natl Inst Technol Durgapur, Dept Chem Engn, Durgapur 713209, W Bengal, India
关键词
Enrofloxacin removal; Micellar-enhanced ultrafiltration; Cetylpyridinium bromide; Response surface methodology; Artificial neural network; RESPONSE-SURFACE METHODOLOGY; FLUOROQUINOLONE ANTIBIOTICS; AQUEOUS-SOLUTIONS; REACTIVE DYES; REMOVAL; OPTIMIZATION; SEPARATION; WASTEWATERS; ADSORPTION; COMPOSITE;
D O I
10.1016/j.scitotenv.2019.06.074
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The presence of a fluoroquinolone base veterinary antibacterial drug enrofloxacin in aqueous media poses a major threat due to its ecotoxicity on aquatic microbiota. Hence, for the first time, an attempt was made to remove enrofloxacin (ENX) from its aqueous solution by employing micellar-enhanced ultrafiltration (MEUF) where cetylpyridinium bromide (CPB), a cationic surfactant was used for micellization. Response surface methodology (RSM) with central composite design (CCD) approach was applied to design the experiment, and to optimize the process parameters, namely, ENX concentration (3-15mg/L), transmembrane pressure (2-6kg(f)/cm(2)), recirculation flow rate (5.5-7.5 L/min) and CPB concentration (1.4-4.2 mM). The objective of this study was to maximize the permeate flux and rejection coefficient and to find out the optimal process condition for the removal of enrofloxacin from aqueous solution. Though maximum 68.23 L/m(2)h of permeate flux and 94.20% of rejection coefficient were achieved at different process conditions, the optimization study reveals that the predicted optimal values of permeate flux and rejection coefficient are 67.53 L/m(2) h and 89.67% respectively. Modelling was also carried out with the aid of artificial neural network (ANN) to validate the prediction of RSM. The predictability of the model by RSM and ANN was compared statistically by evaluating root-mean-square error (RMSE), absolute average deviation (AAD) and mean absolute error (MAE), where ANN exhibited better predictability. The following set of parameters was proposed for industrial scale up: ENX concentration of 8.4 mg/L, TMP of 5 kg(f)/cm(2), recirculation flow rate of 6 L/min and CPB concentration of 2.1 mM. (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页码:10 / 23
页数:14
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