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用于四环素去除和苯酚降解的废弃“塞浦路斯咖啡”双功能复合材料:太阳能芬顿法和人工神经网络

Bifunctional composite from spent "Cyprus coffee" for tetracycline removal and phenol degradation: Solar-Fenton process and artificial neural network.

作者信息

Oladipo Akeem Adeyemi, Abureesh Mosab Ali, Gazi Mustafa

机构信息

Department of Chemistry, Eastern Mediterranean University, Gazimagusa, TRNC via Mersin 10, Turkey.

Department of Chemistry, Eastern Mediterranean University, Gazimagusa, TRNC via Mersin 10, Turkey.

出版信息

Int J Biol Macromol. 2016 Sep;90:89-99. doi: 10.1016/j.ijbiomac.2015.08.054. Epub 2015 Aug 28.

Abstract

Removals of tetracycline and photocatalytic degradation of phenol by Fe3O4/coffee residue (MCC) were investigated. Brunauer-Emmett-Teller (BET), vibrating sample magnetometer (VSM) and Boehm titration were employed to characterize MCC. Artificial neural network (ANN) model was developed to predict the tetracycline (TC) concentration in the column effluent. Maximum tetracycline adsorption capacity of 285.6mg/g was observed in a batch system. High removal efficiency (87%) was obtained at 3.3mL/min flow rate, 8.0cm bed height and 50mg/L influent TC concentration in a column system. Complete degradation of phenol by solar-Fenton was attained at 60min irradiation time. Total organic carbon (TOC) removal increased to 63.3% in the presence of 1.0g/L MCC, 1.2g/L H2O2 and solar irradiation. MCC showed remarkable potential to remove antibiotics from wastewater even in the presence of heavy metal (Ni(2+)) via magnetic separation.

摘要

研究了Fe3O4/咖啡渣(MCC)对四环素的去除及对苯酚的光催化降解。采用布鲁诺尔-埃米特-特勒(BET)法、振动样品磁强计(VSM)和 Boehm 滴定法对MCC进行表征。建立了人工神经网络(ANN)模型来预测柱流出物中的四环素(TC)浓度。在间歇系统中观察到最大四环素吸附容量为285.6mg/g。在柱系统中,当流速为3.3mL/min、床层高度为8.0cm、进水TC浓度为50mg/L时,去除效率较高(87%)。在60min的辐照时间下,通过太阳能芬顿法可实现苯酚的完全降解。在1.0g/L MCC、1.2g/L H2O2和太阳能辐照存在的情况下,总有机碳(TOC)去除率提高到63.3%。即使在存在重金属(Ni(2+))的情况下,MCC通过磁分离也显示出从废水中去除抗生素的巨大潜力。

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