Open-Loop Dynamic Optimization for Nonlinear Multi-Input Systems. Application to Recombinant Protein Production

Authors

Keywords:

bioreactors, Nonlinear System, Fourier Series, trajectory optimization

Abstract

 This paper proposes a novel strategy for dynamic open-loop optimization of multivariable nonlinear systems. The methodology is based on the Fourier series and orthonormal polynomials for the control vector parameterization in a sequential direct solution approach. The advantages of this technique are that a few number of parameters is required for optimization and a smooth control profile is obtained. The proposed strategy is evaluated in the case study of recombinant protein production,  that is a nonlinear system with two control actions, the substrate and inhibitor feed flow rate. The algorithms are tested through simulations and the results are compared with those published in the bibliography.

Downloads

Download data is not yet available.

Author Biographies

María Nadia Pantano, Instituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET.

Nadia Pantano received the Chemical Engineering degree from the National University of San Juan - Argentina, in 2008. Then the Doctorate in Chemical Engineering - Mention Clean Processes degree from the National University of San Juan - Argentina, in 2019. At this time, she is dedicated to process engineering, specifically to optimization and control of multivariable non-linear processes. Her main research interests include modeling, optimization, and trajectory tracking control of biochemical processes.

María Cecilia Fernandez Puchol, Instituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET

Cecilia Fernández received the Food Processing Engineering degree from the National University of San Juan - Argentina, in 2014. Then the Doctorate in Chemical Engineering - Mention Clean Processes degree from the National University of San Juan - Argentina, in 2019. At this time, she is dedicated to process engineering, specifically to optimization and control of multivariable non-linear processes. Her main research interests include modeling, state estimation, and trajectory tracking control of biochemical processes.

Francisco Guido Rossomando, Instituto de Automática, Universidad Nacional de San Juan (UNSJ), CONICET.

Francisco Rossomando was born in San Juan, Argentina. He received the electronic engineering degree and the master degree in engineering from the Universidad Nacional de San Juan (UNSJ), Argentina, in 1997 and 2002, respectively. From 2002 to 2006, he worked on his doctorate degree at the Universidad Federal of Espirito Santo (ES-Brazil); with a thesis on the modelling and control of hot rolling mills. He also completed an executive MBA in administration and management in science and technology at the Getulio Vargas Foundation (Brazil) He is currently asociate researcher of the National Council for Scientific and Technical Research of Argentina (CONICET), at the Universidad Nacional de San Juan (UNSJ) His main interests are algorithms for tracking trajectories, nonlinear and adaptive control theory, and mechatronical and industrial process.

Gustavo J. E. `Scaglia, nstituo de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET.

Gustavo Scaglia received the Ing. degree in Electronic Engineering with orientation in Control Systems from the National University of San Juan, Argentina, in 1999, and the Ph.D in Control Systems from the Institute of Automatic Control at the Instituto de Automática, Argentina in 2006, his work was about a new tracking trajectories algorithms. He is a Research Fellow of the Council for Scientific and Technological Research, Argentina, since 2011. He leads different technological projects and his current scientific research at the Engineering Chemical Institute from National University of San Juan. His main interests are algorithms for tracking trajectories, nonlinear and adaptive control theory, and mechanical and chemical process.

References

A. Tholudur and W. F. Ramirez, "Obtaining smoother singular arc policies using a modified iterative dynamic programming algorithm," International Journal of Control, vol. 68, pp. 1115-1128, 1997.

E. Balsa-Canto, J. R. Banga, A. A. Alonso, and V. S. Vassiliadis, "Efficient optimal control of bioprocesses using second-order information," Industrial and Engineering Chemistry Research, vol. 39, pp. 4287-4295, 2000.

M. S. Croughan, K. B. Konstantinov, and C. Cooney, "The future of industrial bioprocessing: Batch or continuous?," Biotechnology and bioengineering, vol. 112, pp. 648-651, 2015.

H. S. Shin and H. C. Lim, "Maximization of metabolite in fed-batch cultures: Sufficient conditions for singular arc and optimal feed rate profiles," Biochemical Engineering Journal, vol. 37, pp. 62-74, 2007.

J. Lee, S. Y. Lee, S. Park, and A. P. J. Middelberg, "Control of fed-batch fermentations," Biotechnology Advances, vol. 17, pp. 29-48, 1999.

S. Rómoli, G. J. E. Scaglia, M. E. Serrano, S. A. Godoy, O. A. Ortiz, and J. R. Vega, "Control of a Fed-Batch Fermenter Based on a Linear Algebra Strategy," Latin America Transactions, IEEE (Revista IEEE America Latina), vol. 12, pp. 1206-1213, 2014.

K. Y. Rani and V. R. Rao, "Control of fermenters–a review," Bioprocess Engineering, vol. 21, pp. 77-88, 1999.

A. S. Soni and R. S. Parker, "Closed-loop control of fed-batch bioreactors: A shrinking-horizon approach," Industrial & engineering chemistry research, vol. 43, pp. 3381-3393, 2004.

B. Srinivasan, S. Palanki, and D. Bonvin, "Dynamic optimization of batch processes: I. Characterization of the nominal solution," Computers & Chemical Engineering, vol. 27, pp. 1-26, 2003.

P. Dra̧g, K. Styczeń, M. Kwiatkowska, and A. Szczurek, "A review on the direct and indirect methods for solving optimal control problems with differential-algebraic constraints," in Recent Advances in Computational Optimization, ed: Springer, 2016, pp. 91-105.

M. Bartl, P. Li, and L. T. Biegler, "Improvement of state profile accuracy in nonlinear dynamic optimization with the quasi‐sequential approach," AIChE Journal, vol. 57, pp. 2185-2197, 2011.

L. T. Biegler, "An overview of simultaneous strategies for dynamic optimization," Chemical Engineering and Processing: Process Intensification, vol. 46, pp. 1043-1053, 2007.

M. Rocha, R. Mendes, O. Rocha, I. Rocha, and E. C. Ferreira, "Optimization of fed-batch fermentation processes with bio-inspired algorithms," Expert Systems with Applications, vol. 41, pp. 2186-2195, 2014.

J. A. Jaleel, A. Benny, and D. K. Daniel, "Adaptive and neural pH neutralization for strong acid-strong base system," in Global Trends in Intelligent Computing Research and Development, ed: IGI Global, 2014, pp. 496-515.

V. S. Vassiliadis, R. W. Sargent, and C. C. Pantelides, "Solution of a class of multistage dynamic optimization problems. 1. Problems without path constraints," Industrial & Engineering Chemistry Research, vol. 33, pp. 2111-2122, 1994.

E. F. Carrasco and J. R. Banga, "Dynamic optimization of batch reactors using adaptive stochastic algorithms," Industrial & engineering chemistry research, vol. 36, pp. 2252-2261, 1997.

T. Binder, L. Blank, H. G. Bock, R. Bulirsch, W. Dahmen, M. Diehl, et al., "Introduction to model based optimization of chemical processes on moving horizons," in Online optimization of large scale systems, ed: Springer, 2001, pp. 295-339.

M. Schlegel, K. Stockmann, T. Binder, and W. Marquardt, "Dynamic optimization using adaptive control vector parameterization," Computers & Chemical Engineering, vol. 29, pp. 1731-1751, 2005.

F. Assassa and W. Marquardt, "Dynamic optimization using adaptive direct multiple shooting," Computers & Chemical Engineering, vol. 60, pp. 242-259, 2014.

P. Liu, G. Li, X. Liu, and Z. Zhang, "Novel non-uniform adaptive grid refinement control parameterization approach for biochemical processes optimization," Biochemical engineering journal, vol. 111, pp. 63-74, 2016.

L. Xiao, P. Liu, X. Liu, Z. Zhang, Y. Wang, C. Yang, et al., "Sensitivity-based adaptive mesh refinement collocation method for dynamic optimization of chemical and biochemical processes," Bioprocess and biosystems engineering, vol. 40, pp. 1375-1389, 2017.

L. de Sousa Santos, K. M. F. de Souza, M. R. Bandeira, V. R. R. Ahón, F. C. Peixoto, and D. M. Prata, "Dynamic optimization of a continuous gas lift process using a mesh refining sequential method," Journal of Petroleum Science and Engineering, vol. 165, pp. 161-170, 2018.

L. Chang, X. Liu, and M. A. Henson, "Nonlinear model predictive control of fed-batch fermentations using dynamic flux balance models," Journal of Process Control, vol. 42, pp. 137-149, 2016.

S. Craven, J. Whelan, and B. Glennon, "Glucose concentration control of a fed-batch mammalian cell bioprocess using a nonlinear model predictive controller," Journal of Process Control, vol. 24, pp. 344-357, 2014.

M. C. Fernández, M. N. Pantano, E. Serrano, and G. Scaglia, "Multivariable Tracking Control of a Bioethanol Process under Uncertainties," Mathematical Problems in Engineering, vol. 2020, 2020.

M. Cecilia Fernández, M. Nadia Pantano, F. G. Rossomando, O. Alberto Ortiz, and G. J. Scaglia, "State estimation and trajectory tracking control for a nonlinear and multivariable bioethanol production system," Brazilian Journal of Chemical Engineering, vol. 36, pp. 421-437, 2019.

M. C. Fernández, S. Rómoli, M. N. Pantano, O. A. Ortiz, D. Patiño, and G. J. Scaglia, "A New Approach for Nonlinear Multivariable Fed-Batch Bioprocess Trajectory Tracking Control," Automatic Control and Computer Sciences, vol. 52, pp. 13-24, 2018.

M. N. Pantano, M. C. Fernández, M. E. Serrano, O. A. Ortiz, and G. J. E. Scaglia, "Tracking Control of Optimal Profiles in a Nonlinear Fed-Batch Bioprocess under Parametric Uncertainty and Process Disturbances," Industrial & Engineering Chemistry Research, vol. 57, pp. 11130-11140, 2018/08/15 2018.

M. N. Pantano, M. E. Serrano, M. C. Fernández, F. G. Rossomando, O. A. Ortiz, and G. J. Scaglia, "Multivariable Control for Tracking Optimal Profiles in a Nonlinear Fed-Batch Bioprocess Integrated with State Estimation," Industrial & Engineering Chemistry Research, vol. 56, pp. 6043-6056, 2017.

M. N. Pantano, M. C. Fernández, M. E. Serrano, O. A. Ortíz, and G. J. E. Scaglia, "Trajectory Tracking Controller for a Nonlinear Fed-batch Bioprocess," Revista Ingeniería Electrónica, Automática y Comunicaciones ISSN: 1815-5928, vol. 38, p. 78, 2017.

M. C. Fernández, M. N. Pantano, R. A. F. Machado, O. A. Ortiz, and G. J. Scaglia, "Nonlinear multivariable tracking control: application to an ethanol process," International Journal of Automation and Control, vol. 13, pp. 440-468, 2019.

M. C. Fernández, M. N. Pantano, S. Rómoli, H. D. Patiño, O. A. Ortiz, and G. J. Scaglia, "An algebra approach for nonlinear multivariable fed-batch bioprocess control," International Journal of Industrial and Systems Engineering, vol. 33, pp. 38-57, 2019.

W. Wu, S.-Y. Lai, M.-F. Jang, and Y.-S. Chou, "Optimal adaptive control schemes for PHB production in fed-batch fermentation of Ralstonia eutropha," Journal of Process Control, vol. 23, pp. 1159-1168, 2013.

J. Lee and W. F. Ramirez, "Mathematical modeling of induced foreign protein production by recombinant bacteria," Biotechnology and bioengineering, vol. 39, pp. 635-646, 1992.

J. Lee and W. F. Ramirez, "Optimal fed‐batch control of induced foreign protein production by recombinant bacteria," AIChE Journal, vol. 40, pp. 899-907, 1994.

A. Tholudur and W. F. Ramirez, "Optimization of Fed‐Batch Bioreactors Using Neural Network Parameter Function Models," Biotechnology Progress, vol. 12, pp. 302-309, 1996.

D. Sarkar and J. M. Modak, "Optimization of fed-batch bioreactors using genetic algorithm: multiple control variables," Computers & Chemical Engineering, vol. 28, pp. 789-798, 2004.

M. N. Pantano, M. C. Fernández, O. A. Ortiz, G. J. Scaglia, and J. R. Vega, "A Fourier-based control vector parameterization for the optimization of nonlinear dynamic processes with a finite terminal time," Computers & Chemical Engineering, vol. 134, p. 106721, 2020.

E. Kreyszig, Introductory functional analysis with applications vol. 1: wiley New York, 1978.

A. M. C. A. Marin, J. A. H. R. J. Hernand, and J. A. J. B. J. Jimenez, "Tuning Multivariable Optimal PID Controller for a Continuous Stirred Tank Reactor Using an Evolutionary Algorithm," IEEE Latin America Transactions, vol. 16, pp. 422-427, 2018.

C. Fernández, N. Pantano, S. Godoy, E. Serrano, and G. Scaglia, "Optimización de Parámetros Utilizando los Métodos de Monte Carlo y Algoritmos Evolutivos. Aplicación a un Controlador de Seguimiento de Trayectoria en Sistemas no Lineales," Revista Iberoamericana de Automática e Informática industrial, vol. 16, pp. 89-99, 2019.

Published

2021-03-19

How to Cite

Pantano, M. N., Fernandez Puchol, M. C., Rossomando, F. G., & `Scaglia, G. J. E. (2021). Open-Loop Dynamic Optimization for Nonlinear Multi-Input Systems. Application to Recombinant Protein Production. IEEE Latin America Transactions, 19(8), 1307–1314. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/4474

Most read articles by the same author(s)