Steady-state identification for large-scale industrial process by means of dynamic models

This paper investigates the steady-state identification of the large-scale industrial processes. Under mild conditions, the estimate of the steady-state model is formed from the estimated parameters of the approximate linear dynamic models of subsystems. To a class of nonlinear slow time-varying large-scale processes, which have many subsystems interconnected with one another, a parallel two-stage identification algorithm is put forward. The consistency of the estimate and the convergence of the parallel iteration are also proved. Simulation examples have shown that this new identification approach is efficient and reliable for the establishment of the steady-state model of the large-scale industrial process.