Since hot-rolled strip laminar cooling (HSLC) process is a large-scale, nonlinear system, a distributed model predictive control (DMPC) framework is proposed for computational reason and enhancing the precision and flexibility of control system. The overall system is divided into several interconnected subsystems and each subsystem is controlled by local model predictive control (MPC). These lo...

Process data measurements are important for process monitoring, control and optimization. However, process data may be deteriorated by gross errors in measurements. Therefore, it is significant to detect and estimate gross errors with data reconciliation. Meanwhile, in any modern petrochemical plant, the plant-wide mass data derived from process data rendering the real conditions of manufacturi...

A coordinated-control scheme with a compensator for a fossil-fuel power plant is presented. The compensator is intended to reduce the interaction effects among the control loops to ease control during wide-range normal operating conditions. Placed between the controllers and the power plant, the compensator only introduces compensation factors among the control signals; preserving the direct co...

  Bi-level programming, a tool for modeling decentralized decisions, consists of the objective(s) of the leader at its first level and that is of the follower at the second level. Three level programming results when second level is itself a bi-level programming. By extending this idea it is possible to define multi-level programs with any number of levels. Supply chain planning problems are co...

In this paper, a coordinated-distributed model predictive control (MPC) scheme is presented for large-scale discrete-time linear process systems. Coordinated-distributed MPC control aims at enhancing the performance of fully decentralized MPC controllers by achieving the plant-wide optimal operations. The ‘price-driven’ decomposition-coordination method is used to adjust the operations of the i...