The Use of Artificial Neural Network (ANN) for Modeling of Ammonia Nitrogen Removal from Landfill Leachate by the Ultrasonic Process
Background: The study examined the implementation of artificial neural network (ANN) for the prediction of Ammonia nitrogen removal from landfill leachate by ultrasonic process.
Methods: A three-layer backpropagation neural network was optimized to predict Ammonia nitrogen removal from landfill leachate by ultrasonic process. Considering the smallest mean square error (MSE), The configuration of the backpropagation neural network was three-layer ANN with tangent sigmoid transfer function (Tansig) at hidden layer with 14 neurons, linear transfer function (Purelin) at output layer and Levenberg–Marquardt backpropagation training algorithm (LMA).
Results: ANN predicted results were very close to the experimental results with correlation coefficient (R2) of 0.993 and MSE 0.000334. The sensitivity analysis showed that all studied variables (Contact time, ultrasound frequency and power and pH) had strong effect on Ammonia nitrogen removal. In addition, pH was the most influential parameter with relative importance of 44.9%.
Conclusions: The results showed that neural network modeling could effectively predict Ammonia nitrogen removal from landfill leachate by ultrasonic process.
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