Economic model predictive control of nonlinear process systems using empirical models

Alanqar, Anas; Ellis, Matthew; Christofides, Panagiotis D.

doi:10.1002/aic.14683

Title: Economic model predictive control of nonlinear process systems using empirical models

Journal Article · Fri Nov 28 00:00:00 EST 2014 · AIChE Journal

DOI:https://doi.org/10.1002/aic.14683· OSTI ID:1401594

Alanqar, Anas ^[1]; Ellis, Matthew ^[1]; Christofides, Panagiotis D. ^[2]

Dept. of Chemical and Biomolecular Engineering University of California Los Angeles CA 90095
Dept. of Chemical and Biomolecular Engineering University of California Los Angeles CA 90095, Dept. of Electrical Engineering University of California Los Angeles CA 90095

Economic model predictive control (EMPC) is a feedback control technique that attempts to tightly integrate economic optimization and feedback control since it is a predictive control scheme that is formulated with an objective function representing the process economics. As its name implies, EMPC requires the availability of a dynamic model to compute its control actions and such a model may be obtained either through application of first principles or through system identification techniques. In industrial practice, it may be difficult in general to obtain an accurate first‐principles model of the process. Motivated by this, in the present work, Lyapunov‐based EMPC (LEMPC) is designed with a linear empirical model that allows for closed‐loop stability guarantees in the context of nonlinear chemical processes. Specifically, when the linear model provides a sufficient degree of accuracy in the region where time varying economically optimal operation is considered, conditions for closed‐loop stability under the LEMPC scheme based on the empirical model are derived. The LEMPC scheme is applied to a chemical process example to demonstrate its closed‐loop stability and performance properties as well as significant computational advantages. © 2014 American Institute of Chemical Engineers AIChE J , 61: 816–830, 2015

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Sponsoring Organization:: USDOE

OSTI ID:: 1401594

Journal Information:: AIChE Journal, Journal Name: AIChE Journal Vol. 61 Journal Issue: 3; ISSN 0001-1541

Publisher:: Wiley Blackwell (John Wiley & Sons)Copyright Statement

Country of Publication:: United States

Language:: English

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