Coordinator MPC for maximizing plant throughput

Abstract In many cases, economic optimal operation is the same as maximum plant throughput, which is the same as maximum flow through the bottleneck(s). This insight may greatly simplify implementation. In this paper, we consider the case where the bottlenecks may move, with parallel flows that give rise to multiple bottlenecks and with crossover flows as extra degrees of freedom. With the assumption that the flow through the network is represented by a set of units with linear flow connections, the maximum throughput problem is then a linear programming (LP) problem. We propose to implement maximum throughput by using a coordinator model predictive controller (MPC). Use of MPC to solve the LP has the benefit of allowing for a coordinated dynamic implementation. The constraints for the coordinator MPC are the maximum flows through the individual units. These may change with time and a key idea is that they can be obtained with almost no extra effort using the models in the existing local MPCs. The coordinator MPC has been tested on a dynamic simulator for parts of the Karsto gas plant and performs well for the simulated challenges.

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

[2]  Stephen J. Wright,et al.  Stability and optimality of distributed, linear model predictive control Part II: Output Feedback , 2006 .

[3]  Wolfgang Marquardt,et al.  Towards integrated dynamic real-time optimization and control of industrial processes , 2003 .

[4]  Sigurd Skogestad,et al.  Selection of Controlled Variables and Robust Setpoints , 2005 .

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

[6]  Sigurd Skogestad,et al.  IMPLEMENTATION OF MPC ON A DEETHANIZER AT KÅRSTØ GAS PLANT , 2005 .

[7]  Sigurd Skogestad,et al.  Self-optimizing control: the missing link between steady-state optimization and control , 2000 .

[8]  C. Georgakis,et al.  Plantwide regulatory control design procedure using a tiered framework , 1993 .

[9]  J. Fraser Forbes,et al.  Dantzig-Wolfe Decomposition and Large-Scale Constrained MPC Problems , 2004 .

[10]  R. Toepfer,et al.  Techniques of process control , 1966 .

[11]  Sigurd Skogestad,et al.  The Dos and Don’ts of Distillation Column Control , 2007 .

[12]  Sigurd Skogestad,et al.  Control structure design for complete chemical plants , 2004, Comput. Chem. Eng..

[13]  Vladimir Havlena,et al.  A DISTRIBUTED AUTOMATION FRAMEWORK FOR PLANT-WIDE CONTROL, OPTIMISATION, SCHEDULING AND PLANNING , 2005 .

[14]  J. Fraser Forbes,et al.  COORDINATED DECENTRALIZED MPC FOR PLANT-WIDE CONTROL OF A PULP MILL BENCHMARK PROBLEM , 2006 .

[15]  John F. Forbes,et al.  Price-driven coordination method for solving plant-wide MPC problems , 2007 .

[16]  James B. Rawlings,et al.  COORDINATING MULTIPLE OPTIMIZATION-BASED CONTROLLERS: NEW OPPORTUNITIES AND CHALLENGES , 2008 .

[17]  Joseph Z. Lu.  Challenging control problems and emerging technologies in enterprise optimization , 2001 .

[18]  Sigurd Skogestad,et al.  THROUGHPUT MAXIMIZATION BY IMPROVED BOTTLENECK CONTROL , 2007 .

[19]  Stig Strand,et al.  MPC in Statoil – Advantages with In-House Technology , 2004 .

[20]  Jay H. Lee,et al.  An introduction to a dynamic plant-wide optimization strategy for an integrated plant , 2004, Comput. Chem. Eng..

[21]  J. D. Perkins,et al.  Selection of process control structure based on linear dynamic economics , 1993 .

[22]  Sigurd Skogestad.  Consistency of Steady-State Models Using Insight about Extensive Variables? , 1991 .

[23]  木山 健,et al.  16th IFAC World Congress , 2006 .

[24]  S. Skogestad.  DYNAMICS AND CONTROL OF DISTILLATION COLUMNS A tutorial introduction , 1997 .