The use of artificial neural network (ANN) for the prediction and simulation of oil degradation in wastewater by AOP

Abstract

The application of advanced oxidation process (AOP) in the treatment of wastewater contaminated with oil was investigated in this study. The AOP investigated is the homogeneous photo-Fenton (UV/H2O2/Fe(+2)) process. The reaction is influenced by the input concentration of hydrogen peroxide H2O2, amount of the iron catalyst Fe(+2), pH, temperature, irradiation time, and concentration of oil in the wastewater. The removal efficiency for the used system at the optimal operational parameters (H2O2 = 400 mg/L, Fe(+2) = 40 mg/L, pH = 3, irradiation time = 150 min, and temperature = 30 °C) for 1,000 mg/L oil load was found to be 72%. The study examined the implementation of artificial neural network (ANN) for the prediction and simulation of oil degradation in aqueous solution by photo-Fenton process. The multilayered feed-forward networks were trained by using a backpropagation algorithm; a three-layer network with 22 neurons in the hidden layer gave optimal results. The results show that the ANN model can predict the experimental results with high correlation coefficient (R (2) = 0.9949). The sensitivity analysis showed that all studied variables (H2O2, Fe(+2), pH, irradiation time, temperature, and oil concentration) have strong effect on the oil degradation. The pH was found to be the most influential parameter with relative importance of 20.6%.

Artificial Neural Network Modeling for Removal of Cd (II) and Pb (II) from Wastewater by Using Three Ferrite Nanomaterial (Cu0.9Zn0.1Fe2O4, Cu0.8Zn0.2Fe2O4, and Cu0.7Zn0.3Fe2O4) and Study the Antimicrobial Effectiveness of these Ferrite Substances

“…Neural networks are composed of units named neurons or nodes utilizing for processing. A neural network is interconnected parallel buildings consist of three kinds of layers: input, hidden, and output [23,24]. The data presented to the network layers are calculated by taking a weighted sum of the result from the previous layer, and this process is performed with weights which is the force of communication between two neurons, altered by the transfer function [25].…”

“…Sensitivity analysis based on the Garson equation is employed to accurately calculate the beneficial effect of each input variable on the desired outcome of the process using the ANN model. Garson (1991) proposed an equation based totally on the partitioning of connection weights as shown in equation (4) [24,31].…”

Micro-Bubble Flotation for Removing Cadmium Ions from Aqueous Solution: Artificial Neural Network Modeling and Kinetic of Flotation

“…the potential toxicity of residuals amounts of collector in the effluent and cost [23], also this figure confirms that the ANN model could be effectively predicting the experimental results. To theoretically examine, if the amount of SDS represent the optimum value for highly removal efficiency, ANN model was used at different concentrations of SDS was varied around the best value (15,18,20,22,25,28,30 and 35 mg/L) as shown in Fig. 10.…”

“…Numerous studies have been conducted in which ANNs were used in different wastewater processes such as flotation [17], advanced oxidation process [18] power generation by microbial fuel cell [19], adsorption [20] , and sorptive flotation [21]. The scope of this study was to investigate the effect of micro-bubbles in the removal efficiency of Cd(II) ions from aqueous solution at different parameters , ionic strength , flotation kinetic and use the artificial neural network (ANN) to describe this behavior.…”

Intelligent tools to model photocatalytic degradation of beta‐naphtol by titanium dioxide nanoparticles

“…Considering the novelty of photocatalytic processes, as a powerful tool for analysis of the linear and nonlinear relations among various variables, artificial intelligence has been recently applied for predicting the trend of these processes . Using the artificial intelligence models allows lowering the costs and time needed by other modeling processes.…”