Process modeling and evaluation of petroleum refinery wastewater treatment through response surface methodology and artificial neural network in a photocatalytic reactor using poly ethyleneimine (PEI)/titania (TiO2) multilayer film on quartz tube

• Published in: Applied petrochemical research 2015-03, Vol.5 (1), p.47-59
• Main Authors: Pakravan, Parvaneh, Akhbari, Aazam, Moradi, Hojatollah, Azandaryani, Abbas Hemati, Mansouri, Amir Mohammad, Safari, Mojtaba
• Format: Article
• Language: English
• Subjects: Catalysis; Chemistry; Chemistry and Materials Science; Energy Systems; Industrial Chemistry/Chemical Engineering; Learning theory; Mathematical models; Multilayers; Nanochemistry; Nanotechnology and Microengineering; Neural networks; Original Article; Polyetherimides; Quartz; Titanium dioxide; Tubes
• ISSN: 2190-5525; 2190-5525; 2190-5533
• DOI: 10.1007/s13203-014-0077-7

In this study, poly ethyleneimine (PEI)/Titania (TiO 2 ) multilayer film on quartz tubes have been successfully fabricated via a layer-by-layer (LbL) self-assembly method. Scanning electron microscopy (SEM) and Brunauer-Emmett-Teller (BET) surface area analysis were carried out for characterization of the layers on quartz tube. The SEM pictures showed that the film surface is smooth and uniform. The BET characterization verified the formation of multilayer thin film. The photocatalytic activity of the PEI/TiO 2 multilayer deposited on the quartz tubes was evaluated in the treatment of raw petroleum refinery wastewater (PRW) under UV light irradiation in three annular photocatalytic reactors. This study examined the impact of initial chemical oxygen demand (COD) concentration, H 2 O 2 concentration, pH and reaction time on the PRW treatment and the results were used to generate both a response surface methodology (RSM) model and an artificial neural network (ANN) model. Maximum COD removal (98 %) was achieved at the optimum conditions (initial COD concentration of 300 mg/l, hydrogen peroxide concentration of 8.8 mM, pH of 5 and reaction time of 120 min). A comparison between the model results and experimental data gave a high correlation coefficient ( R ANN 2  = 0.9632, R RSM 2  = 0.943) and showed that two models were able to predict COD removal from PRW by PEI/TiO 2 /UV process. However, ANN model was superior to RSM model with higher value of coefficient of determination (0.9632 ANN  > 0.94 RSM ) and the lower root mean square error (RMSE) (3.377 AAN