Abstract
Nonlinear model predictive controllers (NLMPC) using fundamental dynamic models and online nonlinear optimization have been in service in ExxonMobil Chemical since 1994. The NLMPC algorithm used in this work employs a state space formulation, a finite prediction horizon, a performance specification in terms of desired closed loop response characteristics for the outputs, and costs on incremental manipulated variable action. The controller can utilize fundamental or empirical models. The simulation and optimization problems are solved simultaneously using sequential quadratic programming (SQP). In the paper, we present results illustrating regulatory and grade transition (servo) control by NLMPC on several industrial polymerization processes. The paper outlines the NLMPC technology employed, describes the current status in industry for extending linear model predictive control to nonlinear processes or applying NLMPC directly, and identifies several needs for improvements to components of NLMPC.
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