Automatic load change system of cryogenic air separation process

Abstract

In this paper, an automatic load change (ALC) system of cryogenic air separation process is developed to automatically and rapidly respond to the changing product demand from customers. In this automatic load change system, a two-layer framework integrated with nonlinear steady-state optimization and nonlinear model predictive control is designed. Nonlinear steady-state optimization based on homotopy-based backtracking method is performed offline to obtain optimal operating points at various load demands. Nonlinear regression models between optimal operating point and load demand are fitted, which can be easily evaluated online to avoid computational effort and convergence problem. To overcome the pronounced nonlinearities caused by load change, an operating trajectory linear parameter varying (LPV) model is identified to represent the nonlinear dynamic behavior of air separation process. A nonlinear model predictive control (MPC) based on LPV model is designed to drive air separation process rapidly to the optimal operating point of target load demand. Under this framework, material and energy balances of air separation process is actively established during the load change, and load transition time can be shortened. Industrial application results show that oxygen release ratio and unit electric consumption of air separation process are reduced by the implementation of ALC system.