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Optimizing process economic performance using model predictive control. (English) Zbl 1195.93023
Magni, Lalo (ed.) et al., Nonlinear model predictive control. Towards new challenging applications. Selected papers based on the presentations at the international workshop on assessment and future directions of nonlinear model predictive control (NMPC08), Pavia, Italy, September 5–9, 2008. Berlin: Springer (ISBN 978-3-642-01093-4/hbk; 978-3-642-01094-1/ebook). Lecture Notes in Control and Information Sciences 384, 119-138 (2009).
Summary: The current paradigm in essentially all industrial advanced process control systems is to decompose a plant’s economic optimization into two levels. The first level performs a steady-state optimization. This level is usually referred to as Real-Time Optimization (RTO). The RTO determines the economically optimal plant operating conditions (setpoints) and sends these setpoints to the second level, the advanced control system, which performs a dynamic optimization. Many advanced process control systems use some form of Model Predictive Control (MPC) for this layer. The MPC uses a dynamic model and regulates the plant dynamic behavior to meet the setpoints determined by the RTO.
This paper considers aspects of the question of how to use the dynamic MPC layer to optimize directly process economics. We start with the problem of a setpoint that becomes unreachable due to the system constraints. A popular method to handle this problem is to transform the unreachable setpoint into a reachable steady-state target using a separate steady-state optimization. This paper explores the alternative approach in which the unreachable setpoint is retained in the controller’s stage cost and objective function. The use of this objective function induces an interesting fast/slow asymmetry in the system’s tracking response that depends on the system initial condition, speeding up approaches to the unreachable setpoint, but slowing down departures from the unreachable setpoint.
For the entire collection see [Zbl 1165.93004].

MSC:
93A30 Mathematical modelling of systems (MSC2010)
49N90 Applications of optimal control and differential games
90C90 Applications of mathematical programming
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