Kybernetika 61 no. 3, 404-428, 2025

A robust hybrid observer for estimating states, reaction rates, and an external input disturbance for a continuous bioreactor

Víctor Reza, Jorge Torres and Jesús GuerreroDOI: 10.14736/kyb-2025-3-0404

Abstract:

The controlling and monitoring of bioprocesses very often requires the estimation of certain biological concentrations that are difficult to measure, usually assuming some structure of the reaction rates which might be barely known. Although many algorithms have been designed to estimate these reaction rates, they are not robust against input disturbances and cannot be updated to treat them. This paper addresses the problem of estimating unmeasurable states, reaction rates, and input disturbance by applying a hybrid observer in a continuous bioreactor. The proposed algorithm uses an extended super-twisting algorithm coupled with an adaptive observer to exponentially estimate the reaction rates and input disturbance provided the persistent excitation condition is fulfilled. Later, an asymptotic observer estimates the unmeasurable states with the previous estimations. The hybrid observer is tested through simulations in a continuous sulfate-reducing bioprocess. Finally, the advantage of estimating the external disturbance is highlighted through its use in a disturbance rejection control to counteract its undesirable effect.

Keywords:

hybrid observer, super twisting algorithm, adaptive observer, asymptotic observer, continuous bioreactor

Classification:

93B11, 93D11

References:

  1. R. Aguilar-López and I. Neria-González: Controlling continuous bioreactor via nonlinear feedback: modelling and simulations approach. Bull. Polish Academy Sci., Techn. Sci. 64 (2016), 1, 235-241.   DOI:10.1515/bpasts-2016-0025
  2. R. Aguilar López, B. Ruiz Camacho, M. I. Neria-González, E. Rangel, O. Santos and P. A. López Pérez: State estimation based on nonlinear observer for hydrogen production in a photocatalytic anaerobic bioreactor. Int. J. Chemical Reactor Engrg. 15 (2017), 5, 20170004.   DOI:10.1515/ijcre-2017-0004
  3. V. Alcaraz-Gonzalez and V. Gonzalez-Alvarez: Robust nonlinear observers for bioprocesses: Application to wastewater treatment. In: Selected topics in dynamics and control of chemical and biological processes, Springer, Berlin Heidelberg 2007, pp. 119-164.   CrossRef
  4. J. M. Ali, N. H. Hoang, M. A. Hussain and D. Dochain: Review and classification of recent observers applied in chemical process systems. Computers Chemical Engrg. 76 (2015), 27-41.   DOI:10.1016/j.compchemeng.2015.01.019
  5. E. Alvarado-Santos, J. L. Mata-Machuca, P. A. López-Pérez, R. A. Garrido-Moctezuma, F. Pérez-Guevara and R. Aguilar-López: Comparative analysis of a family of sliding mode observers under real-time conditions for the monitoring in the bioethanol production. Fermentation 8 (2022), 9, 446.   DOI:10.3390/fermentation8090446
  6. A. R. Babaei, M. Malekzadeh, M. and D. Madhkhan: Adaptive super-twisting sliding mode control of 6-DOF nonlinear and uncertain air vehicle. Aerospace Sci. Technol. 84 (2019), 361-374.   DOI:10.1016/j.ast.2018.09.013
  7. B D. Anderson, R. R. Bitmead, C. R. Johnson Jr, P. V. Kokotovic, R. L. Kosut, I. M. Mareels, I. M. and B. D. Riedle: Stability of adaptive Systems: Passivity and Averaging Analysis. MIT Press, 1986.   CrossRef
  8. P. Ascencio, D. Sbarbaro and S. F. de Azevedo: An adaptive fuzzy hybrid state observer for bioprocesses. IEEE Trans. Fuzzy Systems 12 (2004), 5, 641-651.   DOI:10.1109/TFUZZ.2004.834815
  9. M. Bahrami, M. Naraghi, M. and M. Zareinejad: Adaptive super-twisting observer for fault reconstruction in electro-hydraulic systems. ISA Trans. 76 (2018), 235-245.   DOI:10.1016/j.isatra.2018.03.014
  10. G. Bastin and D. Dochain: On-line estimation of microbial specific growth rates. Automatica 22 (1986), 6, 705-709.   DOI:10.1016/0005-1098(86)90007-5
  11. G. Bastin and D. Dochain: On-line Estimation and Adaptive Control of Bioreactors. Elsevier, New York, Amsterdam 1990.   CrossRef
  12. O. Bernard, Z. Hadj‐Sadok, D. Dochain, A. Genovesi and J. P. Steyer: Dynamical model development and parameter identification for an anaerobic wastewater treatment process. Biotechnol. Bioengrg. 75 (2001), 4, 424-438.   DOI:10.1002/bit.10036
  13. I. Bouraoui, M. Farza, T. Ménard, R. B. Abdennour, M. M'Saad and H. Mosrati: Observer design for a class of uncertain nonlinear systems with sampled outputs - Application to the estimation of kinetic rates in bioreactors. Automatica 55 (2015), 78-87.   DOI:10.1016/j.automatica.2015.02.036
  14. H. Castaneda, O. S. Salas-Pena and J. de León-Morales: Extended observer based on adaptive second order sliding mode control for a fixed wing UAV. ISA Trans. 66 (2017), 226-232.   DOI:10.1016/j.isatra.2016.09.013
  15. S. Čelikovský, J. A. Torres-Munoz and A. R. Dominguez-Bocanegra: Adaptive high gain observer extension and its application to bioprocess monitoring. Kybernetika 54 (2018), 1, 155-174.   DOI:10.14736/kyb-2018-1-0155
  16. A. K. Coker: Modeling of Chemical Kinetics and Reactor Design. Gulf Professional Publishing, 2001.   CrossRef
  17. L. Cui, R. Zhang, H. Yang and Z. Zuo: Adaptive super-twisting trajectory tracking control for an unmanned aerial vehicle under gust winds. Aerospace Sci. Technol. 115 (2021), 106833.   DOI:10.1016/j.ast.2021.106833
  18. P. Darvehei, P. A. Bahri and N. R. Moheimani: Model development for the growth of microalgae: A review. Renewable Sustainable Energy Rev. {\mi} 97 (2018), 233-258.   DOI:10.1016/j.rser.2018.08.027
  19. H. De Battista, J. Picó, F. Garelli and A. Vignoni: Specific growth rate estimation in (fed-) batch bioreactors using second-order sliding observers. J. Process Control 21 (2011), 7, 1049-1055.   DOI:10.1016/j.jprocont.2011.05.008
  20. H. De Battista, J. Picó, F. Garelli and J. L. Navarro: Reaction rate reconstruction from biomass concentration measurement in bioreactors using modified second-order sliding mode algorithms. Bioprocess Biosystems Engrg. 35 (2012), 1615-1625.   DOI:10.1007/s00449-012-0752-y
  21. H. De Battista, M. Jamilis, F. Garelli and J. Picó: Global stabilisation of continuous bioreactors: Tools for analysis and design of feeding laws. Automatica 89 (2018), 340-348.   DOI:10.1016/j.automatica.2017.12.041
  22. A. J. De Assis and R. Maciel Filho: Soft sensors development for on-line bioreactor state estimation. Computers Chemical Engrg. 24 (2000), 2-7, 1099-1103.   DOI:10.1016/S0098-1354(00)00489-0
  23. D. Dochain: State and parameter estimation in chemical and biochemical processes: a tutorial. J. Process Control 13 (2003), 8, 801-818.   DOI:10.1016/S0959-1524(03)00026-X
  24. F. M. Escalante, K. A. Reyna‐Angeles, J. Villafaña‐Rojas and E. Aguilar‐Garnica: Kinetic model selection to describe the growth curve of Arthrospira (Spirulina) maxima in autotrophic cultures. J. Chemical Technol. Biotechnol. 92 (2017), 6, 1406-1414.   DOI:10.1002/jctb.5136
  25. A. D. Falehi: An innovative optimal RPO-FOSMC based on multi-objective grasshopper optimization algorithm for DFIG-based wind turbine to augment MPPT and FRT capabilities. Chaos Solitons Fractals 130 (2020), 109407.   DOI:10.1016/j.chaos.2019.109407
  26. M. Farza, M. M'Saad, M. L. Fall, E. Pigeon, O. Gehan and K. Busawon: Continuous-discrete time observers for a class of MIMO nonlinear systems. IEEE Trans. Automat. Control 59 (2013), 4, 1060-1065.   DOI:10.1109/tac.2013.2283754
  27. F. García-Maaas, J. L. Guzmán, M. Berenguel and F. G. Acién: Biomass estimation of an industrial raceway photobioreactor using an extended Kalman filter and a dynamic model for microalgae production. Algal Research 37 (2019), 103-114.   DOI:10.1016/j.algal.2018.11.009
  28. J. P. Gauthier, H. Hammouri and S. Othman: A simple observer for nonlinear systems applications to bioreactors. IEEE Trans. Automat. Control 37 (1992), 6, 875-880.   DOI:10.1109/9.256352
  29. W. M. Haddad and V. Chellaboina: Nonlinear Dynamical Systems and Control: A Lyapunov-Based Approach. Princeton University Press 2008.   CrossRef
  30. H. Haimi, M. Mulas, F. Corona and R. Vahala: Data-derived soft-sensors for biological wastewater treatment plants: An overview. Environment. Modell. Foftware 47 (2013), 88-107.   DOI:10.1016/j.envsoft.2013.05.009
  31. Q. Huang, F. Jiang, L. Wang, L. and C. Yang: Design of photobioreactors for mass cultivation of photosynthetic organisms. Engrg. 3 (2017), 3, 318-329.   DOI:10.1016/J.ENG.2017.03.020
  32. P. A. Ioannou and J. Sun: Robust Adaptive Control. Courier Corporation 2012.   CrossRef
  33. Z. Li, S. Zhou, Y. Xiao and L. Wang: Sensorless vector control of permanent magnet synchronous linear motor based on self-adaptive super-twisting sliding mode controller. IEEE Access 7 (2019), 44998-45011.   DOI:10.1109/ACCESS.2019.2909308
  34. Z. Li and J. Zhai: Super-twisting sliding mode trajectory tracking adaptive control of wheeled mobile robots with disturbance observer. Int. J. Robust Nonlinear Control 32 (2022), 18, 9869-9881.   DOI:10.1002/rnc.6343
  35. F. L. Liu, M. Farza and M. M'Saad: Unknown input observers design for a class of nonlinear systems-application to biochemical processes. IFAC Proceed. Vol. 39 (2006), 9, 131-136.   DOI:10.3182/20060705-3-FR-2907.00024
  36. N. D. Lourenco, J. A. Lopes, C. F. Almeida, M. C. Sarraguca and H. M. Pinheiro: Bioreactor monitoring with spectroscopy and chemometrics: a review. Anal. Bioanal. Chemistry 404 (2012), 1211-1237.   DOI:10.1007/s00216-012-6073-9
  37. A. Markana, N. Padhiyar and K. Moudgalya: Multi-criterion control of a bioprocess in fed-batch reactor using EKF based economic model predictive control. Chemical Engrg. Research Design 136 (2018), 282-294.   DOI:10.1016/j.cherd.2018.05.032
  38. J. A. Moreno: Observer design for bioprocesses using a dissipative approach. IFAC Proc. Vol. 41 (2008), 2, 15559-15564.   DOI:10.3182/20080706-5-KR-1001.02631
  39. J. A. Moreno and D. Dochain: Global observability and detectability analysis of uncertain reaction systems and observer design. Int. J. Control 81 (2008), 7, 1062-1070.   DOI:10.1080/00207170701636534
  40. J. A. Moreno and I. Mendoza: Application of super-twisting-like observers for bioprocesses. In: 13th International Workshop on Variable Structure Systems (VSS), IEEE 2014.pp. 1-6.   DOI:10.1109/vss.2014.6881102
  41. J. A. Moreno, E. Rocha-Cózatl and A. V. Wouwer: A dynamical interpretation of strong observability and detectability concepts for nonlinear systems with unknown inputs: application to biochemical processes. Bioprocess Biosyst. Engrg. 37 (2014), 37-49.   DOI:10.1007/s00449-013-0915-5
  42. K. S. Narendra and M. A. Annaswamy: Stable Adaptive Systems. Courier Corporation 2012.   CrossRef
  43. S. Nunez, H. De Battista, F. Garelli, A. Vignoni and J. Picó: Second-order sliding mode observer for multiple kinetic rates estimation in bioprocesses. Control Engrg. Practice 21 (2013), 9, 1259-1265.   DOI:10.1016/j.conengprac.2013.03.003
  44. X. Pan, J. P. Raftery, C. Botre, M. R. DeSessa, T. Jaladi and M. N. Karim: Estimation of unmeasured states in a bioreactor under unknown disturbances. Industr. Engrg. Chemistry Res. 58 (2019), 6, 2235-2245.   DOI:10.1021/acs.iecr.8b02235
  45. L. Pawlowski, O. Bernard, E. Le Floc'h and A. Sciandra: Qualitative behaviour of a phytoplankton growth model in a photobioreactor. IFAC Proc. Vol. 35 (2002), 1, 437-442.   DOI:10.3182/20020721-6-ES-1901.01382
  46. M. Perrier, S. F. De Azevedo, E. C. Ferreira and D. Dochain: Tuning of observer-based estimators: theory and application to the on-line estimation of kinetic parameters. Control Engrg. Practice 8 (2000), 4, 377-388.   DOI:10.1016/S0967-0661(99)00164-1
  47. E. Picó-Marco, J. Picó and H. De Battista: Sliding mode scheme for adaptive specific growth rate control in biotechnological fed-batch processes. Int. J. Control 78 (2005), 2, 128-141.   DOI:10.1080/002071705000073772
  48. J. Picó, H. De Battista and F. Garelli: Smooth sliding-mode observers for specific growth rate and substrate from biomass measurement. J. Process Control 19 (2009), 8, 1314-1323.   DOI:10.1016/j.jprocont.2009.04.001
  49. A. Rapaport and D. Dochain: Interval observers for biochemical processes with uncertain kinetics and inputs. Math. Biosci. 193 (2005), 2, 235-253.   DOI:10.1016/j.mbs.2004.07.004
  50. V. A. Reza López, J. N. Guerrero Tavares and J. A. Torres Munoz: An extended super-twisting algorithm for simultaneous estimation of reaction rates and input disturbance in bioprocess. J. Process Control 123 (2023), 131-140.   DOI:10.1016/j.jprocont.2023.02.009
  51. J. L. Robles-Magdaleno, A. E. Rodríguez-Mata, M. Farza and M. M'Saad: A filtered high gain observer for a class of non uniformly observable systems–Application to a phytoplanktonic growth model. J. Process Control 87 (2020), 68-78.   DOI:10.1016/j.jprocont.2020.01.007
  52. E. Rocha-Cozatl, J. A. Moreno and A. V. Wouwer: Application of a continuous-discrete unknown input observer to estimation in phytoplanktonic cultures. IFAC Proceed. Vol. 45 (2012), 15, 579-584.   DOI:10.3182/20120710-4-SG-2026.00028
  53. G. Sethia, S. K. Nayak and S. Majhi: An approach to estimate lithium-ion battery state of charge based on adaptive Lyapunov super twisting observer. IEEE Trans. Circuits Systems I: Regular Papers 68 (2020), 3, 1319-1329.   DOI:10.1109/TCSI.2020.3044560} \enlargethispage{5mm
  54. Y. Shtessel, C. Edwards, L. Fridman and A. Levant: Sliding Mode Control and Observation (Vol. 10). Springer, New York 2014.   CrossRef
  55. A. Vargas, J. A. Moreno and A. V. Wouwer: A weighted variable gain super-twisting observer for the estimation of kinetic rates in biological systems. J. Process Control 24 (2014), 6, 957-965.   DOI:10.1016/j.jprocont.2014.04.018
  56. H. H. Wang, M. Krstic and G. Bastin: Optimizing bioreactors by extremum seeking. Int. J. Adaptive Control Signal Process. 13 (1999), 8, 651-669.   DOI: 10.1002/(SICI)1099-1115(199912)13:8<651::AID-ACS563>3.0.CO;2-8
  57. S. Wu, J. Zhang and B. Chai: Adaptive super-twisting sliding mode observer based robust backstepping sensorless speed control for IPMSM. ISA Trans. 92 (2019), 155-165.   DOI:10.1016/j.isatra.2019.02.007
  58. I. T. Zuniga, A. Vargas, E. Latrille and G. Buitrón: Robust observation strategy to estimate the substrate concentration in the influent of a fermentative bioreactor for hydrogen production. Chemical Engrg. Sci. 129 (2015), 126-134.   DOI:10.1016/j.ces.2015.02.042