شماره ركورد كنفرانس :
3834
عنوان مقاله :
HETEROGENEOUS PHOTOCATALYTIC OZONATION OF
CIPROFLOXACIN USING TiO2/MMT NANOCOMPOSITE: ARTIFICIAL
NEURAL NETWORK APPROACH
پديدآورندگان :
Hassani Aydin aydin.hassani@atauni.edu.tr Department of Chemistry, Faculty of Science, Atatürk University, Erzurum, Turkey; , Khataee Alireza Research Laboratory of Advanced Water and Wastewater Treatment Processes, Department of Applied Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz, Iran , Fathinia Mehrangiz Research Laboratory of Advanced Water and Wastewater Treatment Processes, Department of Applied Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz, Iran
كليدواژه :
Artificial neural network , Pharmaceuticals , Ciprofloxacin , Photocatalytic ozonation , Nanocomposite
عنوان كنفرانس :
نوزدهمين سمينار شيمي فيزيك ايران
چكيده فارسي :
Artificial neural network (ANN) was applied for the prediction of performances in the degradation of ciprofloxacin (CIP) containing pharmaceutical wastewater from aqueous solution, using TiO2/MMT nanocomposite. The catalyst was prepared through the hydrothermal method and characterized using SEM and EDX techniques. A three-layer artificial neural network was used to estimate the degradation efficiency of CIP. The input parameters used for training the neural network included reaction time (min), the initial CIP concentration (mg L-1), ozone gas flow rate (L h-1), catalyst dosage (g L-1), and the initial pH. The degradation efficiency of CIP was considered as an output of the neural network. Artificial neural network (ANN) model was then developed to predict the degradation efficiency of CIP by a photocatalytic ozonation process. The correlation coefficient between the results predicted by the ANN model and the experimental data was 0.9982, thereby showing that ANN could well predict CIP degradation efficiency under various operating conditions.
