Artificial neural network modeling approach for the prediction of five-day biological oxygen demand and wastewater treatment plant performance

The measurement of the wastewater BOD5 level requires five days, and the use of a prediction model to estimate BOD-(5) saves time and enables the adoption of an online control system. This study investigates the application of artificial neural networks (ANNs) in predicting the influent BOD5 concentration and the performance of WWTPs. The WWTP performance was defined in terms of the COD, BOD, and TSS concentrations in the effluent. Sensitivity analysis was performed to identify the best-performing ANN network structure and configuration. The results showed that the ANN model developed to predict the BOD concentration performed the best among the three outputs. The top-performing ANN models yielded R-2 values of 0.752, 0.612, and 0.631 for the prediction of the BOD, COD, and TSS concentrations, respectively. The optimal performing models were obtained (three inputs - one output), which indicated that the influent temperature and conductivity greatly affect the WWTP performance as inputs in all models. The developed prediction model for the influent BOD5 concentration attained a high accuracy, i.e., R-2 = 0.754, which implies that the model is viable as a soft sensor for online control and management systems for WWTPs. Overall, the ANN model provides a simple approach for the prediction of the complex processes of WWTPs.

Automated summary

The study utilized artificial neural networks to predict the performance of wastewater treatment plants, with the model predicting BOD concentration showing the best performance. Influential factors such as influent temperature and conductivity greatly impact the performance of the models.