With the high dimension in chemical processes

it is necessary to coordinate the sub-systems to achieve optimality of the global system after decomposing the large-scale system.The on-line optimization of the whole system is decomposed into several small sub-problems

significantly reducing the computational complexity in model predictive control of the large-scale system.Decentralized model predictive control and the neighborhood optimization algorithm for distributed model predictive control based on the cascade process are not suitable for the chemical process containing interactive feedback. Therefore

by taking the system constraints into account

the global coordination algorithm was deduced based on those two optimization algorithms.The large-scale system was decomposed into sub-systems with lower dimension.Assumably there was one sample time delay communication between interacted subsystems.For each sub-system

the predictive model was constructed and the overall objective function was formulated by considering the interactions of the sub-systems.The optimal control of each sub-system was achieved.Then

the consistency for distributed model predictive control based on global coordination and centralized model predictive control was analyzed under certain conditions.The global stability of the closed loop system with distributed model predictive control based on global coordination was also obtained. The proposed algorithm was tested by simulation study of Shell heavy oil fractionator and TE process. When compared with other algorithms

it was feasible and effective.