Modeling of goethite iron precipitation process based on time-delay fuzzy gray cognitive network

Abstract

The goethite iron precipitation process consists of several continuous reactors and involves a series of complex chemical reactions, such as oxidation reaction, hydrolysis reaction and neutralization reaction. It is hard to accurately establish a mathematical model of the process featured by strong nonlinearity, uncertainty and time-delay. A modeling method based on time-delay fuzzy gray cognitive network (T-FGCN) for the goethite iron precipitation process was proposed in this paper. On the basis of the process mechanism, experts’ practical experience and historical data, the T-FGCN model of the goethite iron precipitation system was established and the weights were studied by using the nonlinear hebbian learning (NHL) algorithm with terminal constraints. By analyzing the system in uncertain environment of varying degrees, in the environment of high uncertainty, the T-FGCN can accurately simulate industrial systems with large time-delay and uncertainty and the simulated system can converge to steady state with zero gray scale or a small one.