GOALI: Optimization Based Design and Plantwide Control Systems

University Of Maryland, College Park, College Park MD

Investigators

Abstract

Abstract - McAvoy - 0071320 Plantwide control involves the issue of which variables should be measured, controlled, and manipulated, and how these variables should be linked together. For some plants a decentralized proportional-integral-derivative (PID) approach produces acceptable results, while in others it does not. At present there is no systematic methodology that allows a designer to determine how well a particular plantwide control architecture can be expected to perform. The basic question addressed in this research is: given a dynamic process model and expected set point changes/disturbances, is a decentralized architecture sufficient for acceptable performance, or should an interacting model predictive control (MPC) architecture be used for all or part of the plant? The PI has developed two optimization-based approaches to answering this question. One involves optimal control (OC) and it is the focus of the research planned here, while the other makes use of a mixed integer linear programming (MILP) formulation. The approach to be studied assumes that a dynamic process model is available, since such models are becoming more and more common in practice. The objective of the approach is to extract information from the process model about what type of architecture is required to control the plant that the model represents. The PI's OC research used initial state forcing, full state feedback, and it required tuning PID feedback and P feedforward controllers for each candidate decentralized architecture. This OC approach was applied successfully to two DuPont examples. The research to be done under this grant uses output feedback, step forcing for either set points or loads, and no PID tuning is required during the architecture screening process. The PI plans to apply the new methodology to the two DuPont examples, as well as to several other, larger plantwide examples. These other examples include the Tennessee Eastman process, a vinyl acetate process, and possibly an HAD process. Dynamic simulation software has been provided by DuPont and by Aspen Technology and will be used in this research. The DuPont collaborators will provide assistance in developing model predictive controllers for the processes studied.