Moving Horizon Estimation and Nonlinear, Large-Scale Model Predictive Control of Chemical Processes

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ABSTRACTPI: James B. Rawlings Institution: University of WisconsinProposal Number: 0105360To maintain economic competitiveness, maximize product quality and operate more environmentally benign processes, the chemical process industries need to invest in process monitoring and control technology. Industrial process operations are becoming increasingly integrated and complex, operation against constraints is common and the dynamic process behavior requires more sophisticated online analysis and decision making in order to optimize the process performance. The goal of this research is to address three outstanding challenges in the further development of advanced chemical control methodology:1. State estimation based on dynamic models and online measurements is used to further improve process operations. The moving horizon estimation (MHE) approach addresses this need as it allows both nonlinear models and estimator constraints as part of the problem formulation. The PI plans to study a series of issues and application problems in order to understand and expand the current applicability of MHE theory.2. Use model predictive control (MPC) for online optimization of an integrated, large-scale plant. The PI plans to exploit the inherent communication capability provided by the forecast horizon in the MPC controllers.3. Develop a suite of nonlinear MPC test problems for closed-loop control. These problems should provide a convenient standard for the research community to test new nonlinear MPC theories, approaches and algorithms.Expected impacts for this research include: improved understanding of the fundamental issues surrounding the online use of models for monitoring, inference and decision making; direct impact on industrial plant operations and control; and rapid and wide dissemination of new research results to educators by the development and release of high-level source code.