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

Abstract

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 \ 3.569 RSM ). The average percentage error for ANN and RSM models was 0.18 and 0.73, respectively, indicating the superiority of ANN in capturing the nonlinear behavior of the system. It was clear that the best networks were able to predict the experimental responses more accurately than the multiple regression analysis.

Tea fiber waste as an adsorbent to remove phenol from wastewater

“…The kinetic studies were performed at the optimal values. The number of experiments were obtained from central composite design for four variables, its higher and lower range and by considering the star points, factorial points and center points [13]. The total design matrix with the experiments conducted and the percent removal results are given in Table 2.…”

Modeling and optimization by response surface methodology and neural network–genetic algorithm for decolorization of real textile dye effluent using Pleurotus ostreatus: a comparison study

“…(19), the effect of each input where I j is the relative importance of the jth input variable on the output variable, N i and N h are the numbers of input and hidden neurons, respectively, and Ws are connection weights. The superscripts h and o refer to input, hidden, and output layers, respectively; and subscripts m and n refer to input, hidden, and output neurons, respectively [21]. The relative importance of the input variables and their contribution toward decolorization is 31% by pH.…”

“…The OVAT method does not provide any information on interaction effect between variables, which is an important determinant of output response in wastewater management. The inability to predict the complete interaction effects between variables is a limitation of conventional methods [21].…”

“…RSM means to optimize an unknown and noisy function by means of simple function, for a small region under designed experimental conditions. RSM can define the effect of an independent variable either alone or in combination with one or more dependent variables in the process [21,[23][24][25][26][27].…”

“…ANN could work better than RSM, since it does not require a prior specification of fitting function. Moreover, it has the potential of universal estimation [21]. To our knowledge, there is no report available for decolorization of textile dye effluent using inverse fluidized bed bioreactor (IFBBR) by P. ostreatus.…”