A novel multiple improved PID neural network ensemble model for pH value in wet FGD

Abstract

In the limestone/gypsum wet flue gas desulphurization (FGD) technology, the change of slurry pH value in absorber is a nonlinear and time-variation process with a large number of uncertainties, so it's difficult to acquire satisfying mathematical model. In this paper, a novel multiple improved PIDNN ensemble model is proposed to establish the model of slurry pH value. In this model, the concepts of variable integral and partial differential are introduced in the design of hidden-layer of PIDNN, and the concept of output feedback is utilized to improve the ability of PIDNN for dynamic modeling, then multiple improved PIDNN are dynamic combined to get the system output. The results of simulation with field data of wet FGD indicate the validity of this modeling approach. © Springer-Verlag Berlin Heidelberg 2007.