Steady-state target optimization designs for integrating real-time optimization and model predictive control

Abstract In industrial practice, the optimal steady-state operation of continuous-time processes is typically addressed by a control hierarchy involving various layers. Therein, the real-time optimization (RTO) layer computes the optimal operating point based on a nonlinear steady-state model of the plant. The optimal point is implemented by means of the model predictive control (MPC) layer, which typically uses a linear dynamical model of the plant. The MPC layer usually includes two stages: a steady-state target optimization (SSTO) followed by the MPC dynamic regulator. In this work, we consider the integration of RTO with MPC in the presence of plant-model mismatch and constraints, by focusing on the design of the SSTO problem. Three different quadratic program (QP) designs are considered: (i) the standard design that finds steady-state targets that are as close as possible to the RTO setpoints; (ii) a novel optimizing control design that tracks the active constraints and the optimal inputs for the remaining degrees of freedom; and (iii) an improved QP approximation design were the SSTO problem approximates the RTO problem. The main advantage of the strategies (ii) and (iii) is in the improved optimality of the stationary operating points reached by the SSTO-MPC control system. The performance of the different SSTO designs is illustrated in simulation for several case studies.

[1]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[2]  Dominique Bonvin,et al.  Use of measurements for enforcing the necessary conditions of optimality in the presence of constraints and uncertainty , 2005 .

[3]  Babu Joseph,et al.  Performance and stability analysis of LP‐MPC and QP‐MPC cascade control systems , 1999 .

[4]  J. Rawlings,et al.  Feasibility issues in linear model predictive control , 1999 .

[5]  Piotr Tatjewski,et al.  Advanced Control of Industrial Processes: Structures and Algorithms , 2006 .

[6]  Deoki N. Saraf,et al.  Steady-State Identification, Gross Error Detection, and Data Reconciliation for Industrial Process Units , 2004 .

[7]  Dominique Bonvin,et al.  Modifier-adaptation methodology for real-time optimization , 2009 .

[8]  John R. Beaumont,et al.  Control and Coordination in Hierarchical Systems , 1981 .

[9]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[10]  Dominique Bonvin,et al.  Adaptation strategies for real-time optimization , 2009, Comput. Chem. Eng..

[11]  Manfred Morari,et al.  Optimal operation of integrated processing systems. Part I: Open‐loop on‐line optimizing control , 1981 .

[12]  Thomas E. Marlin,et al.  Model accuracy for economic optimizing controllers : the bias update case , 1994 .

[13]  George Stephanopoulos,et al.  Studies in the synthesis of control structures for chemical processes: Part IV. Design of steady‐state optimizing control structures for chemical process units , 1980 .

[14]  Wolfgang Marquardt,et al.  Integration of Economical Optimization and Control for Intentionally Transient Process Operation , 2007 .

[15]  R. R. Rhinehart,et al.  An efficient method for on-line identification of steady state , 1995 .

[16]  Manfred Morari,et al.  Nonlinear offset-free model predictive control , 2012, Autom..

[17]  James B. Rawlings,et al.  Model predictive control with linear models , 1993 .

[18]  Kenneth R. Muske.  Steady-state target optimization in linear model predictive control , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[19]  Gabriele Pannocchia,et al.  Disturbance models for offset‐free model‐predictive control , 2003 .

[20]  Piotr Tatjewski,et al.  Optimizing control of uncertain plants with constrained feedback controlled outputs , 2001 .

[21]  Theodore J. Williams,et al.  A generalized chemical processing model for the investigation of computer control , 1960, Transactions of the American Institute of Electrical Engineers, Part I: Communication and Electronics.

[22]  Ignacio E. Grossmann,et al.  A note on approximation techniques used for process optimization , 1985 .

[23]  C. V. Rao,et al.  Steady states and constraints in model predictive control , 1999 .

[24]  Mokhtar S. Bazaraa,et al.  Nonlinear Programming: Theory and Algorithms , 1993 .

[25]  J. Fraser Forbes,et al.  Extended design cost: a performance criterion for real-time optimization systems , 2000 .

[26]  Dominique Bonvin,et al.  Process optimization via constraints adaptation , 2008 .

[27]  A. Maarleveld,et al.  Constraint control on distillation columns , 1970 .

[28]  Carl D. Meyer,et al.  Matrix Analysis and Applied Linear Algebra , 2000 .

[29]  Michael Nikolaou,et al.  RTO: An overview and assessment of current practice , 2011 .

[30]  David Q. Mayne,et al.  Constrained model predictive control: Stability and optimality , 2000, Autom..