Optimización en Tiempo Real utilizando la Metodología de Adaptación de Modificadores

  1. Rodríguez-Blanco, T.
  2. Sarabia, D.
  3. de Prada, C.
Journal:
Revista iberoamericana de automática e informática industrial ( RIAI )

ISSN: 1697-7920

Year of publication: 2018

Volume: 15

Issue: 2

Pages: 133-144

Type: Article

DOI: 10.4995/RIAI.2017.8846 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Revista iberoamericana de automática e informática industrial ( RIAI )

Abstract

Optimal process operation is carried out by a Real-Time Optimization (RTO) layer which operates above the control layer and takes decisions based on steady-state plant models by considering economic objectives. However, this optimal operation is not guaranteed due to the presence of plant-model mismatch. To bring the process to the optimum operating point, the economic optimization problem solved in the RTO layer is changed following the Modifier Adaptation methodology (MA). This methodology changes the economic optimization problem solved in the RTO layer by adding some corrector terms or modifiers estimated from plant measurements to bring the process to the real optimum. This article presents a review of the different MA techniques developed until now and analyzing their features and the way to implement them.

Bibliographic References

  • Brdys, M., Chen, S., Roberts, P.D. 1986. An extension to the modified two-step algorithm for steady-state system optimization and parameter estimation. International Journal of Systems Science, 17:8, 1229 – 1243. https://doi.org/10.1080/00207728608926883
  • Brdys, M., Roberts, P.D. 1987. Convergence and optimality of modified two-step algorithm for integrated system optimisation and parameter estimation. Int. Journal of Systems Science, 18(7), 1305-1322. https://doi.org/10.1080/00207728708967111
  • Brdys, M., Tatjewski, P. 1994. An algorithm for steady-state optimizing dual control of uncertain plants. 1st IFAC Workshop on new trends in design of control systems, 249-254. Smolenice, Slovakia.
  • Brdys, M., Tatjewski, P. 2005. Iterative algorithms for multilayer optimizing control. Imperial College Press, London UK. https://doi.org/10.1142/p372
  • Bunin, G. A., Wuillemin, Z., François, G., Nakajo, A., Tsikonis, L., & Bonvin, D. 2012. Experimental real-time optimization of a solid oxide fuel cell stack via constraint adaptation. Energy, 39, 54-62. https://doi.org/10.1016/j.energy.2011.04.033
  • Chen, C.Y., Joseph, B., 1987. On-line optimization using a two-phase approach: An application study. Industrial & Engineering Chemistry Research, 26, 1924-1930.
  • Chachuat, B., Srinivasan, B., & Bonvin, D. ,2009. Adaptation strategies for real-time optimization. Computers & Chemical Engineering, 33, 1557-1567. https://doi.org/10.1016/j.compchemeng.2009.04.014
  • Costello, S., François, G., Bonvin, D., 2016. A directional modifier adaptation algorithm for real-time optimization. J. Process Control, 39, 64–76. http://dx.doi.org/10.1016/j.jprocont.2015.11.008
  • Engell, S., 2007. Feedback control for optimal process operation, J. Process Control, 17, 203-219. https://doi.org/10.1016/j.jprocont.2006.10.011
  • François, G., Srinivasan, B., Bonvin, D. 2005. Use of measurements for enforcing the necessary conditions of optimality in the presence of constraints and uncertainty. Journal of Process Control, 15(6),701-712. https://doi.org/10.1016/j.jprocont.2004.11.006
  • François, G., Bonvin, D., 2014. Use of transient measurements for the Optimization of Steady-State Performance via Modifier Adaptation. Industrial & Engineering Chemistry Research, 53 (13), 5148–5159. https://doi.org/10.1021/ie401392s
  • Forbes, J.F., Marlin, T.E. 1994. Model accuracy for economic optimizing controllers: the bias update case. Industrial & Engineering Chemistry Research, 33, 1919-1929. https://doi.org/10.1021/ie00032a006
  • Gao, W., Engell, S., 2005. Iterative set-point optimization of batch chromatography. Computers & Chemical Engineering, 29, 1401-1409. https://doi.org/10.1016/j.compchemeng.2005.02.035
  • Gao, W., Wenzel, S., Engell, S., 2015a. A reliable modifier-adaptation strategy for real-time optimization. Computers & Chemical Engineering, 91, 318-328. https://doi.org/10.1016/j.compchemeng.2016.03.019
  • Gao, W., Wenzel, S., & Engell, S., 2015b. Comparison of Modifier Adaptation Schemes in Real-Time Optimization. In ADCHEM 2015 (Vol. 48, 182-187). Whistler, Canada.: IFAC. https://doi.org/10.1016/j.ifacol.2015.08.178
  • Guay, M., 2014. A time-varying extremum-seeking control approach for discrete-time systems. Journal of Process Control 24, 98-112. https://doi.org/10.1016/j.jprocont.2013.11.014
  • Krstic, M., Wang, H. 2000. Stability of extremum seeking feedback for general nonlinear dynamic systems. Automatica, 36, 595-601. https://doi.org/10.1016/S0005-1098(99)00183-1
  • Marchetti, A., Chachuat, B., & Bonvin, D., 2009. Modifier-Adaptation Methodology for Real-Time Optimization. Industrial & Engineering Chemistry Research, 48, 6022-6033. https://doi.org/10.1021/ie801352x
  • Marchetti, A., Chachuat, B., Bonvin, D., 2010. A dual modifier-adaptation approach for real-time optimization. Journal of Process Control, 20, 1027-1037. https://doi.org/10.1016/j.jprocont.2010.06.006
  • Marlin, T., Hrymak, E. A. N., 1997. Real-time operations optimization of continuous processes. AIChE Symposium Series, 93, 156–164.
  • Navia, D., Gutiérrez, G., de Prada, C. 2013. Nested Modifier- Adaptation Methodology for RTO in the Otto Williams Reactor. In 10th International Symposium on Dynamics and Control Process Systems (DYCOPS 2013); IFAC: Mumbai, India.
  • Navia, D., Brice-o, L., Gutiérrez, G., de Prada, C., 2015. Modifier- adaptation methodology for real-time optimization reformulated as a nested optimization problem. Ind. Eng. Chem. Res 2015; 54,12054-71. https://doi.org/10.1021/acs.iecr.5b01946
  • Navia, D., Villegas, D., Cornejo, I., & de Prada, C., 2016. Real-time optimization for a laboratory-scale flotation column. Computers & Chemical Engineering, 86, 62-74. https://doi.org/10.1016/j.compchemeng.2015.12.006
  • Roberts, P.D., 1979. An algorithm for steady-state system optimization and Parameter-Estimation. International Journal of Systems Science, 10, 719-734. https://doi.org/10.1080/00207727908941614
  • Rodríguez-Blanco, T., Sarabia, D., Navia, D., de Prada, C., 2015. Modifier- adaptation methodology for RTO applied to distillation columns. ADCHEM 2015, Whistler, British Columbia, Canada.
  • Rodríguez-Blanco, T., Sarabia, D., de Prada, C., 2016. Modifier- adaptation methodology for RTO applied to distillation columns using a simplified steady-state model. MSC 2016, Buenos Aires, Argentina.
  • Rodríguez-Blanco, T., Sarabia, D., de Prada, C., 2017a. Modifier- adaptation approach using RELS to compute process gradients. FOCAPO-CPC 2017. Tucson, Arizona.
  • Rodríguez-Blanco, T., Sarabia, D., de Prada, C., 2017b. Nested Modifier Adaptation for RTO correcting the Lagrangian gradients applied to the Otto Williams reactor. ESCAPE 27. Barcelona, España.
  • Rodríguez-Blanco, T., Sarabia, D., de Prada, C., 2017c. Modifier Adaptation methodology based on transient and static measurements for RTO to cope with structural uncertainty. Computers & Chemical Engineering, 106, 480-500. https://doi.org/10.1016/j.compchemeng.2017.07.001
  • Skogestad, S., 2000. Self- optimizing control: The missing link between steady-state optimization and control. Computers and Chemical Engineering, 24, 569-575. https://doi.org/10.1016/S0098-1354(00)00405-1
  • Tatjewski, P., 2008. Advanced control and on-line process optimization in multilayer structures. Annual Reviews in Control, 32, 71-85. https://doi.org/10.1016/j.arcontrol.2008.03.003
  • Vahidi, A., Stefanopoulou, A., Peng, H., 2005. Recursive least squares with forgetting for online estimation of vehicle mass and road grade: Theory and experiments. Vehicle System Dynamics, 2005: 43, 31-55. https://doi.org/10.1080/00423110412331290446