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负载型 TiO2 纳米粒子光催化去除 C.I.碱性红 46:人工神经网络建模。

Photocatalytic removal of C.I. Basic Red 46 on immobilized TiO2 nanoparticles: artificial neural network modelling.

机构信息

Department of Applied Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz, Iran.

出版信息

Environ Technol. 2009 Oct;30(11):1155-68. doi: 10.1080/09593330903133911.

DOI:10.1080/09593330903133911
PMID:19947146
Abstract

C.I. Basic Red 46, commonly used as a textile dye, was photocatalytically removed using supported TiO2 nanoparticles irradiated by a 30 W UV-C lamp in a batch reactor. The investigated photocatalyst was industrial Degussa P25 (crystallite mean size 21 nm) immobilized on glass beads by a heat attachment method. The catalyst was characterized by XRD, SEM, TEM and BET techniques. The process of the dye decolorization in the presence of TiO2 nanoparticles was experimentally studied through changing the initial dye concentration, UV light intensity and initial pH. The influence of inorganic anions such as chloride, sulphate, bicarbonate, carbonate and phosphate on the photocatalytic decolorization of BR46 was investigated. The decolorization of BR46 follows the pseudo-first-order kinetic according to the Langmuir-Hinshelwood model (k1 = 0.273 mg L(-1) min(-1), 2 = 0.313 (mg L(-1))(-1)). The efficiency parameters such as apparent quantum yield and electrical energy per order (EEO) were estimated. An artificial neural network model (ANN) was developed to predict the photocatalytic decolorization of BR46 solution. The findings indicated that the ANN provided reasonable predictive performance (R2 = 0.96). The influence of each parameter on the variable studied was assessed: initial concentration of the dye being the most significant factor, followed by the initial pH and reaction time.

摘要

C.I. 基本红 46,通常用作纺织染料,在间歇反应器中使用负载 TiO2 纳米粒子在 30 W UV-C 灯照射下进行光催化去除。所研究的光催化剂是通过热附着法固定在玻璃珠上的工业 Degussa P25(晶粒度平均尺寸 21nm)。催化剂通过 XRD、SEM、TEM 和 BET 技术进行了表征。通过改变初始染料浓度、紫外光强度和初始 pH 值,实验研究了 TiO2 纳米粒子存在下染料脱色的过程。研究了氯离子、硫酸盐、碳酸氢盐、碳酸盐和磷酸盐等无机阴离子对 BR46 光催化脱色的影响。BR46 的脱色符合准一级动力学,符合 Langmuir-Hinshelwood 模型(k1 = 0.273 mg L(-1) min(-1),2 = 0.313 (mg L(-1))(-1))。估计了表观量子产率和每阶电能(EEO)等效率参数。开发了人工神经网络模型(ANN)来预测 BR46 溶液的光催化脱色。结果表明,ANN 提供了合理的预测性能(R2 = 0.96)。评估了每个参数对研究变量的影响:染料的初始浓度是最重要的因素,其次是初始 pH 值和反应时间。

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