4. Luttmann R, Bracewell DG, Cornelissen G, Gernaey KV, Glassey J, Hass VC, Kaiser C,
Preusse C, Striedner G, Mandenius C-F (2012) Soft sensors in bioprocessing: a status report
and recommendations. Biotechnol J 7:1040
5. Schügerl K, Bellgardt KH (2012) Bioreaction engineering: modeling and control. Springer,
Berlin
6. Schügerl K (2001) Progress in monitoring, modeling and control of bioprocesses during the last
20 years. J Biotechnol 85(2):149–173
7. Narayanan H, Luna MF, von Stosch M, Bournazou MNC, Polotti G, Morbidelli M, Butté A,
Sokolov M (2019) Bioprocessing in the digital age - the role of process models. Biotechnol J
761. https://doi.org/10.1002/biot.201900172
8. Kalman RE (1960) A new approach to linear filtering and prediction problems. Trans ASME J
Basic Eng 82:S.35–S.45
9. Wan EA, van der Merwe R (2000) The unscented Kalman filter for nonlinear estimation. In:
Proceedings of the IEEE 2000 adaptive systems for signal processing, communications, and
control symposium (Cat. No. 00EX373). IEEE, pp 153–158
10. Matthews M (1990) A state-space approach to adaptive nonlinear filtering using recurrent
neural networks. In: Proceedings IASTED Internat. Symp. artificial intelligence application
and neural networks
11. Boulet G, Kerr Y, Chehbouni A, Kalma JD (2002) Deriving catchment-scale water and energy
balance parameters using data assimilation based on extended Kalman filtering. Hydrol Sci J 47
(3):449–467
12. Krämer S, Grum M, Verworn HR, Redder A (2005) Runoff modelling using radar data and flow
measurements in a stochastic state space approach. Water Sci Technol 52(5):1–8
13. Williams M, Schwarz PA, Law BE, Irvine J, Kurpius MR (2005) An improved analysis of forest
carbon dynamics using data assimilation. Glob Chang Biol 11(1):89–105
14. Annan JD, Hargreaves JC, Edwards NR, Marsh R (2005) Parameter estimation in an intermediate complexity earth system model using an ensemble Kalman filter. Ocean Model 8
(1–2):135–154
15. Julier SJ, Uhlmann JK (1997) New extension of the Kalman filter to nonlinear systems. In:
Signal processing, sensor fusion, and target recognition VI, vol 3068. International Society for
Optics and Photonics, pp 182–193
16. Evensen G (1994) Sequential data assimilation with a nonlinear quasigeostrophic model using
Monte Carlo methods to forecast error statistics. J Geophys 99(C5):10.143–10.162
17. Houtekamer PL, Mitchell HL (1998) Data assimilation using an ensemble Kalman filter
technique. Mon Weather Rev 126(3):796–811
18. van der Merwe R (2004) Sigma-point Kalman filters for probabilistic inference in dynamic
state-space models. Doctoral dissertation, OGI School of Science and Engineering at OHSU
19. Lee SC, Hwang YB, Chang HN, Chang YK (1991) Adaptive control of dissolved oxygen
concentration in a bioreactor. Biotechnol Bioeng 37(7):597–607
20. Ghoul M, Dardenne M, Fonteix C, Marc A (1991) Extended Kalman filtering technique for the
on-line control of OKT3 hybridoma cultures. Biotechnol Tech 5(5):367–370
21. Dubach AC, Märkl H (1992) Application of an extended kalman filter method for monitoring
high density cultivation of Escherichia coli. J Ferment Bioeng 73(5):396–402
22. Gudi R, Shah S (1993) The role of adaptive multirate Kalman filter as a software sensor and its
application to a bioreactor. IFAC Proc 26(2):249–254
23. Gudi R, Gray I, Shah S (1993) Multi-rate estimation and monitoring of process variables in a
bioreactor. In: Proceedings of IEEE international conference on control and applications, IEEE
24. Albiol J, Robusté J, Casas C, Poch M (1993) Biomass estimation in plant cell cultures using an
extended Kalman filter. Biotechnol Prog 9(2):174–178
25. Gudi RD, Shah SL, Gray MR (1995) Adaptive multirate state and parameter estimation
strategies with application to a bioreactor. AICHE J 41(11):2451–2464
26. Petrova M, Georgieva O, Patarinska T (1995) State and time delay estimation of continuous
microorganisms cultivation. Bioprocess Eng 12(1–2):103–107
The Kalman Filter for the Supervision of Cultivation Processes
123
Preusse C, Striedner G, Mandenius C-F (2012) Soft sensors in bioprocessing: a status report
and recommendations. Biotechnol J 7:1040
5. Schügerl K, Bellgardt KH (2012) Bioreaction engineering: modeling and control. Springer,
Berlin
6. Schügerl K (2001) Progress in monitoring, modeling and control of bioprocesses during the last
20 years. J Biotechnol 85(2):149–173
7. Narayanan H, Luna MF, von Stosch M, Bournazou MNC, Polotti G, Morbidelli M, Butté A,
Sokolov M (2019) Bioprocessing in the digital age - the role of process models. Biotechnol J
761. https://doi.org/10.1002/biot.201900172
8. Kalman RE (1960) A new approach to linear filtering and prediction problems. Trans ASME J
Basic Eng 82:S.35–S.45
9. Wan EA, van der Merwe R (2000) The unscented Kalman filter for nonlinear estimation. In:
Proceedings of the IEEE 2000 adaptive systems for signal processing, communications, and
control symposium (Cat. No. 00EX373). IEEE, pp 153–158
10. Matthews M (1990) A state-space approach to adaptive nonlinear filtering using recurrent
neural networks. In: Proceedings IASTED Internat. Symp. artificial intelligence application
and neural networks
11. Boulet G, Kerr Y, Chehbouni A, Kalma JD (2002) Deriving catchment-scale water and energy
balance parameters using data assimilation based on extended Kalman filtering. Hydrol Sci J 47
(3):449–467
12. Krämer S, Grum M, Verworn HR, Redder A (2005) Runoff modelling using radar data and flow
measurements in a stochastic state space approach. Water Sci Technol 52(5):1–8
13. Williams M, Schwarz PA, Law BE, Irvine J, Kurpius MR (2005) An improved analysis of forest
carbon dynamics using data assimilation. Glob Chang Biol 11(1):89–105
14. Annan JD, Hargreaves JC, Edwards NR, Marsh R (2005) Parameter estimation in an intermediate complexity earth system model using an ensemble Kalman filter. Ocean Model 8
(1–2):135–154
15. Julier SJ, Uhlmann JK (1997) New extension of the Kalman filter to nonlinear systems. In:
Signal processing, sensor fusion, and target recognition VI, vol 3068. International Society for
Optics and Photonics, pp 182–193
16. Evensen G (1994) Sequential data assimilation with a nonlinear quasigeostrophic model using
Monte Carlo methods to forecast error statistics. J Geophys 99(C5):10.143–10.162
17. Houtekamer PL, Mitchell HL (1998) Data assimilation using an ensemble Kalman filter
technique. Mon Weather Rev 126(3):796–811
18. van der Merwe R (2004) Sigma-point Kalman filters for probabilistic inference in dynamic
state-space models. Doctoral dissertation, OGI School of Science and Engineering at OHSU
19. Lee SC, Hwang YB, Chang HN, Chang YK (1991) Adaptive control of dissolved oxygen
concentration in a bioreactor. Biotechnol Bioeng 37(7):597–607
20. Ghoul M, Dardenne M, Fonteix C, Marc A (1991) Extended Kalman filtering technique for the
on-line control of OKT3 hybridoma cultures. Biotechnol Tech 5(5):367–370
21. Dubach AC, Märkl H (1992) Application of an extended kalman filter method for monitoring
high density cultivation of Escherichia coli. J Ferment Bioeng 73(5):396–402
22. Gudi R, Shah S (1993) The role of adaptive multirate Kalman filter as a software sensor and its
application to a bioreactor. IFAC Proc 26(2):249–254
23. Gudi R, Gray I, Shah S (1993) Multi-rate estimation and monitoring of process variables in a
bioreactor. In: Proceedings of IEEE international conference on control and applications, IEEE
24. Albiol J, Robusté J, Casas C, Poch M (1993) Biomass estimation in plant cell cultures using an
extended Kalman filter. Biotechnol Prog 9(2):174–178
25. Gudi RD, Shah SL, Gray MR (1995) Adaptive multirate state and parameter estimation
strategies with application to a bioreactor. AICHE J 41(11):2451–2464
26. Petrova M, Georgieva O, Patarinska T (1995) State and time delay estimation of continuous
microorganisms cultivation. Bioprocess Eng 12(1–2):103–107
The Kalman Filter for the Supervision of Cultivation Processes
123