In this paper, a software sensor based on a genetic algorithm-based neural fuzzy system (GA-NFS) was proposed for real-time estimation of nutrient concentrations in a biological wastewater treatment process. In order to improve the network performance, self-adapted fuzzy c-means clustering algorithm and genetic algorithm were employed to extract and optimize the structure of the network. The GA-NFS was applied to a biological wastewater treatment process for nutrient removal. The simulative results indicate that the learning and generalization ability of the model performed well and also worked well for normal batch i.e., two data points. Real-time estimation of COD, NO3 − and PO4 3− concentration based on GA-NFS functioned effectively with the simple on-line information on the anoxic/oxic system. 
A sensor-software based on a genetic algorithm-based neural fuzzy system for modeling and simulating a wastewater treatment process