Model Predictive Control for changing economic targets DanielLim´on ∗ Antonio Ferramosca ∗∗ Teodoro Alamo ∗ Alejandro H. Gonz´ alez ∗∗ Darci Odloak ∗∗∗ ∗ Departamento de Ingenier´ ıa de Sistemas y Autom´ atica, Universidad de Sevilla, Escuela Superior de Ingenieros, Camino de los Descubrimientos s/n. 41092, Sevilla, Spain. (e-mail: {limon,alamo}@cartuja.us.es) ∗∗ Institute of Technological Development for the Chemical Industry (INTEC), CONICET-Universidad Nacional del Litoral (UNL). G¨ uemes 3450, (3000) Santa Fe, Argentina. (e-mail: {ferramosca,alejgon}@santafe-conicet.gov.ar.) ∗∗∗ Department of Chemical Engineering, University of S˜ao Paulo. Av. Prof. Luciano Gaulberto, trv 3 380, 61548, S˜ao Paulo, Brazil. (e-mail: odloak@usp.br.) Abstract: The objective of this paper is to present recent results on model predictive control for tracking in the context of economic operation of a industrial plants. The well-established hierarchical economic control is based on a Real Time Optimizer that calculates the economic target to the advanced controller, in this case model predictive controllers. The change of the economic parameters or constraints, or the existence of disturbances and modelling errors make that this target may change throughout the plant evolution. The MPC for tracking is an appealing formulation to deal with this issue since maintain the recursive feasibility and convergence under any change of the target. Thus, this MPC formulation is summarized as well as its properties. In virtue of these properties, it is demonstrated how the economic operation can be improved by integrating the Steady State Target Optimizer in the MPC. Then it is also shown how the proposed MPC can deal with practical problems such us zone control or distributed control. Finally, the economic control of the plant can be enhanced by adopting an economic MPC approach. A formulation capable to ensure economic optimality and target tracking is also shown. 1. INTRODUCTION The main goal of an advanced control system in the process industries is to ensure a safe operation of the plant while the economic profits are maximized, attending the policies of the operator of the plant. The economic control of the plant in the process industries is implemented in a hierarchical control structure [Qin and Badgwell, 2003, Engell, 2007, Tatjewski, 2008]: at the top of this structure, en economic scheduler and planner decides what, when and how much the plant has to produce, taking into account information from the market and from the same plant itself. The output of this layer are production goals, prices, cost functions and constraints that are sent to a Real Time Optimizer (RTO). The RTO is a model-based system, operated in closed- loop, whose task is to provide the economic targets of the process variables controlled by the control level, taking into account a number of information like production goals, prices of products, energy costs, and constraints. ⋆ This work has been funded by the National Plan Projects DPI2008-05818 and DPI2010-21589-C05-01 of the Spanish Ministry of Science and Innovation and FEDER funds, and ANPCYT, Ar- gentina (PICT 2008, contract number 1833). It employs a stationary model of the plant and for this reason its sampling time is usually larger than the one of the process. The economic operation point of the plant is calculated by solving the following optimization problem: min xs,us Φ(x s ,u s ) s.t. F (x s ,u s ,w)=0 H(x s ,u s ) ≤ 0 where Φ(x s ,u s ) is the profit function of the process, F (x s ,u s ,w) = 0 is the stationary model of the plant that depends on a set of parameters/signals w which are provided by the operator or estimated from the data of the plant, as estimated disturbances, updated parameters of the model or corrected measurement from the data reconciliation procedure. The inequalities H(x s ,u s ) ≤ 0 define the operational constraints of the plant. The RTO provides the economic targets y t for a set of process variables y controlled by the advanced control scheme. The most extended advanced control scheme is the model predictive control [Qin and Badgwell, 1997]. This must be designed to maintain the controlled variables as close as possible to the targets y t , and hence to operate the plant close to the economic optimum. Typically, as it Preprints of 4th IFAC Nonlinear Model Predictive Control Conference International Federation of Automatic Control Noordwijkerhout, NL. August 23-27, 2012 Copyright © IFAC. All rights reserved. 384