Coordinator MPC for maximizing plant throughputAuthors: Elvira M.B. Aske, Stig Strand and Sigurd SkogestadAffiliation: NTNU (Department of Chemical Engineering) and Statoil R&D (Trondheim) Reference: 2008, Vol. 29, No. 3, pp. 103-115. |
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 (1436 Kb)
DOI: 10.4173/mic.2008.3.3
Currently no DOI forward links to this article
References:
[1] Aske, E., Skogestad, S., and Strand, S. Throughput maximization by improved bottleneck control. In 8th International Symposium on Dynamics and Control of Process Systems (DYCOPS), volume 1. Cancun, Mexico, 2007 pp. 63-68.
[2] Aske, E., Strand, S., and Skogestad, S. Implementation of MPC on a deethanizer at Kårstø gas plant. In 16th IFAC World Congress, paper We-M06-TO/2. Prague, Czech Republic, 2005 pp. CD-rom published by International Federation of Automatic Control.
[3] Buckley, P. S. Techniques of Process Control. John Wiley and Sons, Inc., NY, USA, 1964.
[4] Cheng, R., Forbes, J., and Yip, W. Dantzig-Wolfe decomposition and large-scale constrained MPC problems. In International Symposium on Dynamics and Control of Process Systems (DYCOPS). Boston, USA, 2004 pp. paper 117, in CD rom.
[5] Cheng, R., Forbes, J., and Yip, W. 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, 2006 pp. 971-976.
[6] Cheng, R., Forbes, J., and Yip, W. Price-driven coordination method for solving plant-wide MPC problems. J. Proc. Control, 2007. 17:429-438, doi:10.1016/j.jprocont.2006.04.003
[7] Ford, L. and Fulkerson, D. Flows in Networks. Princeton University Press, 1962.
[8] Govatsmark, M. and Skogestad, S. Selection of controlled variables and robust setpoints. Ind. Eng. Chem. Res., 2005. 44:2207-2217, doi:10.1021/ie049750y
[9] Havlena, V. and Lu, J. 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, 2005 pp. 80-94.
[10] Kadam, J., Marquardt, W., Schlegel, M., Backx, T., Bosgra, O., Brouwer, P.-J., Dünnebier, G., van Hessem, D., Tiagounov, A., and de Wolf, S. Towards integrated dynamic real-time optimization and control of industrial processes. In Proceedings Foundations of Computer-Aided Process Operations (FO-CAPO2003). Coral Springs, Florida, 2003 pp. 593-596.
[11] Kister, H. Z. Distillation Operation. McGraw Hill, NY, USA, 1990.
[12] Lu, J. Challenging control problems and emerging technologies in enterprise optimization. Control Engineering Practice, 2003. 11:847-858, doi:10.1016/S0967-0661(03)00006-6
[13] Marlin, T. E. and Hrymak, A. N. 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, 1997 pp. 156-164.
[14] Narraway, L. and Perkins, J. Selection of process control structure based on linear dynamic economics. Ind. Eng. Chem. Res., 1993. 32(11):2681-2692, doi:10.1021/ie00023a035
[15] Price, R. M. and Georgakis, C. Plantwide regulatory control design procedure using a tiered framework. Ind. Eng. Chem. Res., 1993. 32:2693-2705, doi:10.1021/ie00023a036
[16] Qin, S. and Badgwell, T. A survey of industrial model predictive control technology. Control Engineering Practice, 2003. 11:733-764, doi:10.1016/S0967-0661(02)00186-7
[17] Rawlings, J. and Stewart, B. 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, 2007 pp. 19-28.
[18] Skogestad, S. Consistency of steady-state models using insights about extensive variables. Ind. Eng. Chem. Res., 1991. 30:654-661, doi:10.1021/ie00052a009
[19] Skogestad, S. Dynamics and control of distillation columns - A tutorial introduction. Trans. IChemE, 1997. 75(Part A):539-562.
[20] Skogestad, S. Self-optimizing control: the missing link between steady-state optimization and control. Comput. Chem. Eng., 2000. 24:569-575, doi:10.1016/S0098-1354(00)00405-1
[21] Skogestad, S. Control structure design for complete chemical plants. Comput. Chem. Eng., 2004. 28:219-234, doi:10.1016/j.compchemeng.2003.08.002
[22] Skogestad, S. The dos and don’ts of distillation column control. Trans. IChemE, Part A, 2007. 85(A1):13-23.
[23] Strand, S. Dynamic Optimization in State-Space Predictive Control Schemes. Ph.D. thesis, Norwegian Institute of Technology (NTH), Trondheim, 1991.
[24] Strand, S. and Sagli, J. MPC in Statoil - Advantages with in-house technology. International Symposium on Advanced Control of Chemical Processes (AD-CHEM), Hong Kong, 2004, 2003. pp. 97-103.
[25] Tosukhowong, T., Lee, J., Lee, J., and Lu, J. An introduction to a dynamic plant-wide optimization strategy for an integrated plant. Comput. Chem. Eng., 2004. 29:199-208, doi:10.1016/j.compchemeng.2004.07.028
[26] Venkat, A., Rawlings, J., and Wright, S. Stability and optimality of distributed, linear model predictive control. Part I: State feedback. Technical report, 2006-03, TWMCC, Department of Chemical Engineering, University of Wisconsin-Madison, 2006.
[27] Ying, C.-M. and Joseph, B. Performance and stability analysis of LP-MPC and QP-MPC cascade control systems. AlChE J., 1999. 45(7):1521-1534.
BibTeX:
@article{MIC-2008-3-3,
title={{Coordinator MPC for maximizing plant throughput}},
author={Aske, Elvira M.B. and Strand, Stig and Skogestad, Sigurd},
journal={Modeling, Identification and Control},
volume={29},
number={3},
pages={103--115},
year={2008},
doi={10.4173/mic.2008.3.3},
publisher={Norwegian Society of Automatic Control}
};


