Monitoring of sequencing batch reactor for nitrogen and phosphorus removal using neural networks

The information of nutrient dynamics is essential for the precise control of effluent quality discharged from biological wastewater treatment processes. However, these variables can usually be determined with a significant time delay. Although the final effluent quality can be analyzed after this delay, it is often too late to make proper adjustments. In this paper, a neural network approach, a software sensor, was proposed for the real-time estimation of nutrient concentrations and overcoming the problem of delayed measurements. In order to improve the neural network performance, a split network structure applied separately for anaerobic and aerobic conditions was employed with dynamic modeling methods such as auto-regressive with exogenous inputs. The proposed methodology was applied to a bench-scale sequencing batch reactor (SBR) for biological nutrient removal. The extrapolation problem of neural networks was possible to be partially overcome with the aid of multiway principal component analysis because of its ability of detecting of abnormal situations which could generate extrapolation. Real-time estimation of PO₄^3-, NO₃^- and NH₄⁺ concentrations based on neural network was successfully carried out with the simple on-line information of the SBR system only.