Optimización en Tiempo Real utilizando la Metodología de Adaptación de Modificadores

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

ISSN: 1697-7920

Argitalpen urtea: 2018

Alea: 15

Zenbakia: 2

Orrialdeak: 133-144

Mota: Artikulua

DOI: 10.4995/RIAI.2017.8846 DIALNET GOOGLE SCHOLAR lock_openSarbide irekia editor

Beste argitalpen batzuk: Revista iberoamericana de automática e informática industrial ( RIAI )

Laburpena

La gestión óptima de las plantas de proceso normalmente se lleva a cabo en una capa de optimización en tiempo real (Real Time Optimization, RTO) que actúa sobre la capa de control y que toma decisiones considerando objetivos económicos en base a un  modelo del proceso, normalmente estacionario. Sin embargo, dicha operación óptima no está garantizada debido a la presencia de incertidumbre entre el modelo usado para la toma de decisiones y el proceso real. Con la idea de conducir el proceso a su punto de operación óptimo usando un modelo que se sabe incierto o erróneo, surge la metodología de adaptación de modificadores (Modifier Adaptation o MA). En dicha metodología, el problema de optimización económica de la capa RTO es modificado mediante unos términos correctores, conocidos como modificadores, estimados a partir de medidas de la planta, con el objetivo de conducir el proceso a su punto de operación óptimo. El presente artículo hace una revisión de las técnicas desarrolladas hasta el momento dentro de la metodología MA analizando sus características y modos de implementación.

Erreferentzia bibliografikoak

  • 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
Datuen iturria: Dialnet