Modeling and Forecasting of nanoFeCu Treated Sewage Quality Using Recurrent Neural Network (RNN)

Abstract

Rapid industrialization and population growth cause severe water pollution and increased water demand. The use of FeCu nanoparticles (nanoFeCu) in treating sewage has been proven to be a space-efficient method. The objective of this work is to develop a recurrent neural network (RNN) model to estimate the performance of immobilized nanoFeCu in sewage treatment, thereby easing the monitoring and forecasting of sewage quality. In this work, sewage data was collected from a local sewage treatment plant. pH, nitrate, nitrite, and ammonia were used as the inputs. One-to-one and three-to-three RNN architectures were developed, optimized, and analyzed. The result showed that the one-to-one model predicted all four inputs with good accuracy, where R2 was found within a range of 0.87 to 0.98. However, the stability of the one-to-one model was not as good as the three-to-three model, as the inputs were chemically and statistically correlated in the later model. The best three-to-three model was developed by a single layer with 10 neurons and an average R2 of 0.91. In conclusion, this research provides data support for designing the neural network prediction model for sewage and provides positive significance for the exploration of smart sewage treatment plants.