SYSTEM-IDENTIFICATION AND REAL-TIME PATTERN-RECOGNITION BY NEURAL NETWORKS FOR AN ACTIVATED-SLUDGE PROCESS

This study introduces the application of a neural network method to es timate chemical oxygen demand (COD) in a wastewater treatment process. It presents the back propagation algorithm with a generalized sigmoid function in detail. Several simulations were investigated in order to select suitable learning rates and relative coefficients in the netwo rk for accelerating the speed of convergence in learning. The results of simulation to estimate real sampling data of COD for a specific rea l world wastewater treatment process are explained.