Background and Objective: In recent years, applications of carbon-based nanomaterials have been developed in various fields such as water and wastewater industry. One of these compounds is graphene oxide (GO), which has attracted a lot of attention due to its high speci
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Background and Objective: In recent years, applications of carbon-based nanomaterials have been developed in various fields such as water and wastewater industry. One of these compounds is graphene oxide (GO), which has attracted a lot of attention due to its high specific surface two-dimensional structure and various surface groups. In this regard, the main purpose of this study is to investigate the coagulation properties of graphene oxide in removing turbidity from water and modeling the process using artificial neural network (ANN).Material and Methodology: The samples were prepared by using garden soil and tap water and the GO was purchased in the form of suspension. Jar tests were performed to assess the influence of pH, GO dosage, initial turbidity, settling time and other parameters on the turbidity removal efficiency. In order to simulate the process, Perceptron neural network was used.Findings: Under acidic pH conditions and with increasing the GO dosage from 2.5 mg/L to 40 mg/L, the removal efficiency increased considerably. However, the initial turbidity did not show a clear effect on the process performance. Much of the turbidity removal occurred within the first 10 minutes of the settling time and the flocs’ exhibited higher settling rates at higher GO dosages and acidic pH condition. According to the results obtained from the created ANN model, the coefficient of determination (R2) and the correlation coefficient (R) between the observed and predicted values of the test data were 0.9492 and 0.974, respectively, which reveal the model’s high capability in predicting the process results.Discussion and Conclusion: GO showed high capability in turbidity removal from water. The pH and GO dosage were recognized as the process controller parameters. The ANN data mining model showed good performance in predicting process efficiency.
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