Iron precipitation is consisted of several continuous reactors

which involves a series of complex chemical reactions such as oxidation

hydrolysis and neutralization. Owing to its strong nonlinearity and uncertainty

it is difficult to establish an accurate mathematical model of the iron precipitation process. A modeling method based on fuzzy gray cognition network was proposed from expert experience and historical data. The weighted values were studied by nonlinear Hebbian learning algorithm with terminal constraints. The analysis results on system at various extents of uncertainty show that FGCN can effectively simulate complex industrial systems in environment with high uncertainty. The simulated system can be converged to a gray number equilibrium point of very small or zero gray scale and then be solved to obtain an accurate control output by whitening function.