Strong Consistency Analysis of Steady-State Models for Nonlinear Large-Scale Industrial Processes

The steady-state optimization problem is optimizing objective functions based on mathematical model under constrained conditions, in fact the large-scale industrial processes are often nonlinear and slowly time varying. In allusion to dynamic nonlinear large-scale industrial processes, to bring up gained the method of decentralized identification for the strong consistency estimates of the divisible steady-state models, it is used that property of polynomial can infinitely approach to the nonlinear function and in optimization processes use step signals as input signals, the divisible steady-state models of dynamic nonlinear large-scale industrial processes, and the cognizable conditions are obtained.