Abstract
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 or MPC for this layer. The MPC uses a dynamic model and regulates the plant dynamic behavior to meet the setpoints determined by the RTO.
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Rawlings, J.B., Amrit, R. (2009). Optimizing Process Economic Performance Using Model Predictive Control. In: Magni, L., Raimondo, D.M., Allgöwer, F. (eds) Nonlinear Model Predictive Control. Lecture Notes in Control and Information Sciences, vol 384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01094-1_10
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DOI: https://doi.org/10.1007/978-3-642-01094-1_10
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