A Shortened Time Horizon Approach for Optimization with Differential-Algebraic Constraints

• Autorzy: Drąg Paweł

Identyfikatory

DOI

10.15439/2021F47

• Konferencja: Federated Conference on Computer Science and Information Systems (16 ; 02-05.09.2021 ; online)
• Języki publikacji: EN

Abstrakty

EN

In this work a new numerical optimization scheme based on a shortened time horizon approach was designed. The shortened time horizon strategy has never been presented or tested numerically. The new methodology was applied for a single objective optimization task subject to a system of nonlinear differential-algebraic constraints, which can take a form of differential-algebraic equations (DAEs). Moreover, it was assumed, that an application of a cooperated multiple shooting with direct solution method, like direct shooting approach, does not enable us to solve the DAE system, even on relatively small subintervals. Therefore, the new solution procedure is based on two main parts: i) designing of an alternative differentialalgebraic constraints, ii) parametrization of a new constraints system by the multiple shooting approach and further simulation of the alternative system independently on small subintervals. Then, the simulation interval can be modified by the shortened time horizon approach. The presented algorithm was used to solve a highly nonlinear optimization task of a fed-batch reactor operation.

Słowa kluczowe

EN

numerical optimization; differential-algebraic constraints; shortened time horizon; multiple shooting method

PL

optymalizacja numeryczna; więzy różniczkowo-algebraiczne; skrócony horyzont czasowy; wielokrotna metoda strzelania

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2021
• Tom: Vol. 25
• Strony: 211--215
• Opis fizyczny: Bibliogr. 11 poz., wz., wykr.

Twórcy

autor

Drąg Paweł

• Department of Control Systems and Mechatronics Wrocław University of Science and Technology Wrocław, Poland

Bibliografia

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• 4. A. Caspari, L. Lüken, P. Schäfer, Y. Vaupel, A. Mhamdi, L.T. Biegler, A. Mitsos, Dynamic optimization with complementarity constraints: Smoothing for direct shooting, Computers and Chemical Engineering, vol. 139, 2020, article no. 106891, https://doi.org/10.1016/j.compchemeng.2020.106891.
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• 6. P. Dra̧g, K. Styczeń, The new approach for dynamic optimization with variability constraints. In: S. Fidanova (ed.), Recent advances in computational optimization : results of the Workshop on Computational Optimization WCO 2017. Cham, Springer, 2019. pp. 35-46, https://doi.org/10.1007/978-3-319-99648-6 3.
• 7. M.T. Kelley, R. Baldick, M. Baldea, A direct transcription-based multiple shooting formulation for dynamic optimization, Computers and Chemical Engineering, vol. 140, 2020, art. no. 106846, https://doi.org/10.1016/j.compchemeng.2020.106846.
• 8. R. Luus, O. Rosen, Application of dynamic programming to final state constrained optimal control problems, Industrial & Engineering Chemistry Research, vol. 30, 1991, pp. 1525-1530.
• 9. D. Pandelidis, M. Dra̧g, P. Dra̧g, W. Worek, S. Cetin, Comparative analysis between traditional and M-Cycle based cooling tower, International Journal of Heat and Mass Transfer, vol. 159, 2020, art. no. 120124, pp. 1-13, https://doi.org/10.1016/j.ijheatmasstransfer.2020.120124.
• 10. J. Nocedal, S. Wright, Numerical Optimization. Springer, New York, NY, 2006, https://doi.org/10.1007/978-0-387-40065-5.
• 11. D.M. Yancy-Caballero, L.T. Biegler, R. Guirardello, Large-scale DAE-constrained optimization applied to a modified spouted bed reactor for ethylene production from methane, Computers and Chemical Engineering, vol. 113, 2018, pp 162-183, https://doi.org/10.1016/j.compchemeng.2018.03.017.

Uwagi

1. Track 1: Artificial Intelligence in Applications

2. Session: 14th International Workshop on Computational Optimization

3. Short Paper