Joseph Z. Lu

Challenging control problems and emerging technologies in enterprise optimization

Abstract As the scale of control systems increases from individual model predictive control (MPC) applications to integrated control systems for enterprise optimization, two challenges arise. First, the classic MPC regulatory control formulation is not always sufficientthe multi variable controller needs to be more than a regulator. Second, dynamic coordination among MPC controllers is a key to tight integration between advanced process control and plantwide optimization. The first half of this paper discusses the common control needs in the process industries and proposes a range control solution. The balance of the paper discusses the needs and complexity of the dynamic coordination problem, and proposes a three-tier integration method. Because enterprise optimization is a relatively new endeavor, this paper focuses on problems and issues, as well as solutions. The problems and proposed solutions are intended to stimulate discussion and attract more research interest.

A DISTRIBUTED AUTOMATION FRAMEWORK FOR PLANT-WIDE CONTROL, OPTIMISATION, SCHEDULING AND PLANNING

Abstract The objective of the talk will be to identify current open problems and trends in plant wide control and demonstrate a solution based on distributed, solution component based architecture for integrated process management, embracing the layers of Advanced Process Control, Real Time Optimisation and Planning & Scheduling, in selected application areas. The problems and outlined solutions are intended to stimulate discussion as well as attract more research interest.

Closing the gap between planning and control: A multiscale MPC cascade approach

Abstract A one-to-many, multiscale model predictive control (MPC) cascade is proposed for closing the gap between production planning and process control. The gap originates from the fact that planning and control use models at different scales, and the gap has existed since the first planning tool was deployed. Multiscaleness has been at the core of the challenge to coordinating heterogeneous solution layers, and there has been a lack of systematic treatment for multiscaleness in a control system. The proposed MPC cascade is devised as a plantwide master MPC controller cascading on top of multiple ( n ) slave MPC controllers. 1 The master can use a coarse-scale, single-period planning model as the gain matrix of its dynamic model, and it then can control the same set of variables that are only monitored by the planning tool. Each slave controller, using a fine-scale model, performs two functions: (1) model predictive control for a process unit, and (2) computation of proxy limits that represent the current constraints inside the slave. The masters economic optimizer amends the single-period planning optimization in real time with the slaves proxy limits, and the embedded planning model is thus reconciled with the MPC models for process units in the sense that the masters optimal solution now honors the slaves constraints. With this new approach, the proposed MPC cascade becomes the plantwide closed-loop control system that performs the reconciled planning optimization in its master controller and carries out the just-in-time production plan through its slave controllers.

On interfacing model predictive controllers with a real-time optimizer

Abstract In this paper, we develop a real-time optimization strategy based on dynamic optimization using a reduced-order model to maximize profitability of an integrated plant. The steady-state assumption in conventional real-time optimizers severely limits the frequency of optimization and precludes the use of dynamic degrees of freedom in the plant ( e.g. storage capacities), resulting in suboptimal solutions. Other approaches that synchronize the frequency of the higher-level optimization with that of the local model predictive controllers can be sensitive to local disturbances and model uncertainty in the high-frequency dynamics. We reason that a logical middle ground is to perform a dynamic optimization at a rate lower than the model predictive controllers in order to keep the modeling and computational requirements at a reasonable level. We discuss the obtaining of the reduced-order model valid up to a chosen optimization frequency and the interfacing of the real-time optimizer with the unit controllers. An example is given to compare the various approaches.

SYSTEM ARCHITECTURE FOR PROCESS AUTOMATION: REVIEW AND TRENDS

Abstract New developments in information technologies are radically transforming process automation. Their impact and benefit derive both from these technologies individually and from their convergence in new system architecture concepts. This paper reviews how process automation system architectures have evolved and discusses future trends. We draw an analogy between the synergistic new technologies being developed today and the technology landscape of the early 1970s–-characterized by the near-simultaneous appearance of microprocessors, communication networks, CRT displays–-that resulted in the first DCS system (the Honeywell TDC2000). Emerging technologies highlighted include wireless, embedded devices, service-oriented architecture, and application infrastructures.