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“Coordinator MPC for maximizing plant throughput”

Authors: Elvira M.B. Aske, Stig Strand and Sigurd Skogestad,
Affiliation: NTNU, Department of Chemical Engineering and Statoil
Reference: 2008, Vol 29, No 3, pp. 103-115.

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Keywords: bottleneck, maximize throughput, MPC

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 Kårstø gas plant and performs well for the simulated challenges.

PDF PDF (1436 Kb)        DOI: 10.4173/mic.2008.3.3

DOI forward links to this article:
  [1] L.F. Acebes, A. Merino, A. Rodriguez, R. Mazaeda and C. de Prada (2019), doi:10.1016/j.jprocont.2019.05.005

[1] Aske, E., Skogestad, S., Strand, S. (2007). Throughput maximization by improved bottleneck control, In 8th International Symposium on Dynamics and Control of Process Systems.DYCOPS, volume 1. Cancun, Mexico, pp. 63-68.
[2] Aske, E., Strand, S., Skogestad, S. (2005). Implementation of MPC on a deethanizer at Kårstø gas plant, In 16th IFAC World Congress, paper We-M06-TO/2. Prague, Czech Republic, pp. CD-rom published by International Federation of Automatic Control.
[3] Buckley, P. S. (1964). Techniques of Process Control, John Wiley and Sons, Inc., NY, USA.
[4] Cheng, R., Forbes, J., Yip, W. (2004). Dantzig-Wolfe decomposition and large-scale constrained MPC problems, In International Symposium on Dynamics and Control of Process Systems.DYCOPS. Boston, USA, pp. paper 117, in CD rom.
[5] Cheng, R., Forbes, J., Yip, W. (2006). Coordinated decentralized MPC for plant-wide control of a pulp mill benchmark problem, In International Symposium on Advanced Control of Chemical Processes.ADCHEM, volume 2. Gramado, Brazil, pp. 971-976.
[6] Cheng, R., Forbes, J., Yip, W. (2007). Price-driven coordination method for solving plant-wide MPC problems, J. Proc. Control. 17:429-438 doi:10.1016/j.jprocont.2006.04.003
[7] Ford, L. Fulkerson, D. (1962). Flows in Networks, Princeton University Press.
[8] Govatsmark, M. Skogestad, S. (2005). Selection of controlled variables and robust setpoints, Ind. Eng. Chem. Res. 44:2207-2217 doi:10.1021/ie049750y
[9] Havlena, V. Lu, J. (2005). A distributed automation framework for plant-wide control, optimisation, scheduling and planning, In P. Horacek, M. Simandl, and P. Zitek, editors, 16th Triennial World Congress of the International Federation of Automatic Control. Prague, Czech Republic, pp. 80-94.
[10] Kadam, J., Marquardt, W., Schlegel, M., Backx, T., Bosgra, O., Brouwer, P.-J., Dünnebier, G., van Hessem, D., Tiagounov, A., de Wolf, S. (2003). Towards integrated dynamic real-time optimization and control of industrial processes, In Proceedings Foundations of Computer-Aided Process Operations.FO-CAPO2003. Coral Springs, Florida, pp. 593-596.
[11] Kister, H. Z. (1990). Distillation Operation, McGraw Hill, NY, USA.
[12] Lu, J. (2003). Challenging control problems and emerging technologies in enterprise optimization, Control Engineering Practice. 11:847-858 doi:10.1016/S0967-0661(03)00006-6
[13] Marlin, T. E. Hrymak, A. N. (1997). Real-time operations optimization of continuous processes, In J. Kantor, C. Garcia, and B. Carnahan, editors, Fifth International Conference on Chemical Process Control.CPC-5. Lake Tahoe, Nevada, pp. 156-164.
[14] Narraway, L. Perkins, J. (1993). Selection of process control structure based on linear dynamic economics, Ind. Eng. Chem. Res. 3.11:2681-2692 doi:10.1021/ie00023a035
[15] Price, R. M. Georgakis, C. (1993). Plantwide regulatory control design procedure using a tiered framework, Ind. Eng. Chem. Res. 32:2693-2705 doi:10.1021/ie00023a036
[16] Qin, S. Badgwell, T. (2003). A survey of industrial model predictive control technology, Control Engineering Practice. 11:733-764 doi:10.1016/S0967-0661(02)00186-7
[17] Rawlings, J. Stewart, B. (2007). Coordinating multiple optimization-based controllers: New opportunities and challenges, In 8th International Symposium on Dynamics and Control of Process Systems.DY-COPS, volume 1. Cancun, Mexico, pp. 19-28.
[18] Skogestad, S. (1991). Consistency of steady-state models using insights about extensive variables, Ind. Eng. Chem. Res. 30:654-661 doi:10.1021/ie00052a009
[19] Skogestad, S. (1997). Dynamics and control of distillation columns - A tutorial introduction, Trans. IChemE. 7.Part A:539-562.
[20] Skogestad, S. (2000). Self-optimizing control: the missing link between steady-state optimization and control, Comput. Chem. Eng. 24:569-575 doi:10.1016/S0098-1354(00)00405-1
[21] Skogestad, S. (2000). Control structure design for complete chemical plants, Comput. Chem. Eng. 28:219-234 doi:10.1016/j.compchemeng.2003.08.002
[22] Skogestad, S. (2007). The dos and don'ts of distillation column control, Trans. IChemE, Part A. 8.A1:13-23.
[23] Strand, S. (1991). Dynamic Optimization in State-Space Predictive Control Schemes, Ph.D. thesis, Norwegian Institute of Technology.NTH, Trondheim.
[24] Strand, S. Sagli, J. (2004). MPC in Statoil - Advantages with in-house technology, International Symposium on Advanced Control of Chemical Processes.AD-CHEM, Hong Kong, pp. 97-103.
[25] Tosukhowong, T., Lee, J., Lee, J., Lu, J. (2004). An introduction to a dynamic plant-wide optimization strategy for an integrated plant, Comput. Chem. Eng., 29:199-208 doi:10.1016/j.compchemeng.2004.07.028
[26] Venkat, A., Rawlings, J., Wright, S. (2006). Stability and optimality of distributed, linear model predictive control, Part I: State feedback. Technical report, TWMCC, Department of Chemical Engineering, University of Wisconsin-Madison.
[27] Ying, C.-M. Joseph, B. (1999). Performance and stability analysis of LP-MPC and QP-MPC cascade control systems, AlChE J., 4.7:1521-1534.

  title={{Coordinator MPC for maximizing plant throughput}},
  author={Aske, Elvira M.B. and Strand, Stig and Skogestad, Sigurd},
  journal={Modeling, Identification and Control},
  publisher={Norwegian Society of Automatic Control}


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