Chemical process modeling with multiple neural networks

It is difficult to identify some chemical processes which are operated in complex environments and the operation conditions are changed frequently. In this paper we combine the two effective identification tools, multiple models and dynamic neural networks, and propose a new class of identification approach. A hysteresis switching algorithm is used to select the best model in each time. The convergence of the multiple neuro identifier is proved. The simulation results show that the multiple neuro identifier has a better performance for the pH neutralization and the fermentation process.