Automatic Control of Bioprocesses

  • Marc Stanke
  • Bernd Hitzmann
Part of the Advances in Biochemical Engineering/Biotechnology book series (ABE, volume 132)


In this chapter, different approaches for open-loop and closed-loop control applied in bioprocess automation are discussed. Although in recent years many contributions dealing with closed-loop control have been published, only a minority were actually applied in real bioprocesses, the majority being simulations. As a result of the diversity of bioprocess requirements, a single control algorithm cannot be applied in all cases; rather, different approaches are necessary. Most publications combine different closed-loop control techniques to construct hybrid systems. These systems are supposed to combine the advantages of each approach into a well-performing control strategy. The majority of applications are soft sensors in combination with a proportional–integral–derivative (PID) controller. The fact that soft sensors have become this importance for control purposes demonstrates the lack of direct measurements or their large additional expense for robust and reliable online measurement systems. The importance of model predictive control is increasing; however, reliable and robust process models are required, as well as very powerful computers to address the computational needs. The lack of theoretical bioprocess models is compensated by hybrid systems combining theoretical models, fuzzy logic, and/or artificial neural network methodology. Although many authors suggest a possible transfer of their presented control application to other bioprocesses, the algorithms are mostly specialized to certain organisms or certain cultivation conditions as well as to a specific measurement system.

Graphical Abstract


Automation Bioprocess Closed-loop control Fuzzy logic Neural network 

Abbreviations and Nomenclature


Artificial neural network


Carbon dioxide evolution rate


Substrate concentration in feed flow


Carbon dioxide production rate


Dissolved oxygen


Control deviation at time t


Extended Kalman filter


Flow injection analysis


Feeding rate


Feeding rate due to feedback part


Gas chromatography


High-cell-density cultivation


PID controller parameter matrix


PID controller parameter, derivative part


PID controller parameter, integral part


Oxygen transfer coefficient


PID controller parameter, proportional part


Local linear model


Local linear model tree


Substrate consumption due to cell maintenance


Multiple-input multiple-output


Multiple-input single-output


Model predictive controller


Nonlinear model predictive controller


Oxidation–reduction potential


Oxygen transfer rate


Oxygen uptake rate




Specific oxygen consumption rate


Oxygen consumption rate


Single-input single-output




Control action at time t


Cultivation volume


Initial volume


Volume of head space




Input values (measurement)


Initial biomass


Yield factor for biomass formation


Specific growth rate


Membership function of a fuzzy logic controller


Set-point of specific growth rate


Integration variable


  1. 1.
    Becker T, Hitzmann B, Muffler K, Pörtner R, Reardon K, Stahl F, Ulber R (2007) Future aspects of bioprocess monitoring. In: Ulber R, Sell D (eds) Advances in biochemical engineering/biotechnology vol 105. Springer, Berlin, pp 249–293. doi:10.1007/10_2006_036 Google Scholar
  2. 2.
    Navrátil M, Norberg A, Lembrén L, Mandenius C-F (2005) On-line multi-analyzer monitoring of biomass, glucose and acetate for growth rate control of a vibrio cholerae fed-batch cultivation. J Biotechnol 115(1):67–79. doi: 10.1016/j.jbiotec.2004.07.013 CrossRefGoogle Scholar
  3. 3.
    Warth B, Rajkai G, Mandenius CF (2010) Evaluation of software sensors for on-line estimation of culture conditions in an Escherichia coli cultivation expressing a recombinant protein. J Biotechnol 147(1):37–45. doi: 10.1016/j.jbiotec.2010.02.023 CrossRefGoogle Scholar
  4. 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 doi: 10.1002/biot.201100506
  5. 5.
    Kadlec P, Gabrys B, Strandt S (2009) Data-driven soft sensors in the process industry. Comput Chem Eng 33(4):795–814. doi: 10.1016/j.compchemeng.2008.12.012 CrossRefGoogle Scholar
  6. 6.
    Jain G, Jayaraman G, Kökpinar Ö, Rinas U, Hitzmann B (2011) On-line monitoring of recombinant bacterial cultures using multi-wavelength fluorescence spectroscopy. Biochem Eng J 58–59:133–139. doi: 10.1016/j.bej.2011.09.005 CrossRefGoogle Scholar
  7. 7.
    Jenzsch M, Simutis R, Lübbert A (2006) Generic model control of the specific growth rate in recombinant Escherichia coli cultivations. J Biotechnol 122(4):483–493CrossRefGoogle Scholar
  8. 8.
    Hulhoven X, Wouwer AV, Bogaerts P (2006) Hybrid extended Luenberger-asymptotic observer for bioprocess state estimation. Chem Eng Sci 61(21):7151–7160. doi: 10.1016/j.ces.2006.06.018 CrossRefGoogle Scholar
  9. 9.
    Kalman RE (1960) A new approach to linear filtering and prediction problems. Trans ASME J Basic Eng (82 (Series D)):35–45. doi:citeulike-article-id:347166Google Scholar
  10. 10.
    Kawohl M, Heine T, King R (2007) Model based estimation and optimal control of fed-batch fermentation processes for the production of antibiotics. Chem Eng Process Process Intensif 46(11):1223–1241CrossRefGoogle Scholar
  11. 11.
    Lidgren L, Lilja O, Krook M, Kriz D (2006) Automatic fermentation control based on a real-time in situ SIRE® biosensor regulated glucose feed. Biosens Bioelectron 21(10):2010–2013CrossRefGoogle Scholar
  12. 12.
    Kriz D, Berggren C, Johansson A, Ansell RJ (1998) SIRE-technology. Part I. Amperometric biosensor based on flow injection of the recognition element and differential measurements. Instrum Sci Technol 26(1):45–57. doi: 10.1080/10739149808002089 CrossRefGoogle Scholar
  13. 13.
    Gnoth S, Jenzsch M, Simutis R, Lübbert A (2007) Process analytical technology (PAT): batch-to-batch reproducibility of fermentation processes by robust process operational design and control. J Biotechnol 132(2):180–186CrossRefGoogle Scholar
  14. 14.
    Arndt M, Hitzmann B (2004) Kalman filter based glucose control at small set points during fed-batch cultivation of Saccharomyces cerevisiae. Biotechnol Prog 20(1):377–383. doi: 10.1021/bp034156p CrossRefGoogle Scholar
  15. 15.
    Arndt M, Kleist S, Miksch G, Friehs K, Flaschel E, Trierweiler J, Hitzmann B (2005) A feedforward feedback substrate controller based on a Kalman filter for a fed-batch cultivation of Escherichia coli producing phytase. Comput Chem Eng 29(5):1113–1120CrossRefGoogle Scholar
  16. 16.
    Kleist S, Miksch G, Hitzmann B, Arndt M, Friehs K, Flaschel E (2003) Optimization of the extracellular production of a bacterial phytase with Escherichia coli by using different fed-batch fermentation strategies. Appl Microbiol Biotechnol 61(5):456–462. doi: 10.1007/s00253-003-1229-3 Google Scholar
  17. 17.
    Klockow C, Hüll D, Hitzmann B (2008) Model based substrate set point control of yeast cultivation processes based on FIA measurements. Anal Chim Acta 623(1):30–37. doi: 10.1016/j.aca.2008.06.011 CrossRefGoogle Scholar
  18. 18.
    Roeva O, Slavov T, Dimov I, Dimova S, Kolkovska N (2008) fed-batch cultivation control based on genetic algorithm PID controller tuning numerical methods and applications, vol 6046. Lecture Notes in Computer Science. Springer, Berlin, pp 289–296. doi:10.1007/978-3-642-18466-6_34 Google Scholar
  19. 19.
    Tsonyo S, Roeva O (2011) Genetic algorithm tuning of PID controller in smith predictor for glucose concentration control. Int J BIO Autom 15(2):101–114Google Scholar
  20. 20.
    Wahab NA, Katebi R, Balderud J (2009) Multivariable PID control design for activated sludge process with nitrification and denitrification. Biochem Eng J 45(3):239–248CrossRefGoogle Scholar
  21. 21.
    Biener R, Steinkämper A, Hofmann J (2010) Calorimetric control for high cell density cultivation of a recombinant Escherichia coli strain. J Biotechnol 146(1–2):45–53CrossRefGoogle Scholar
  22. 22.
    Biener R, Steinkämper A, Horn T (2012) Calorimetric control of the specific growth rate during fed-batch cultures of Saccharomyces cerevisiae. J Biotechnol 160(3–4):195–201Google Scholar
  23. 23.
    Soons ZITA, Voogt JA, van Straten G, van Boxtel AJB (2006) Constant specific growth rate in fed-batch cultivation of Bordetella pertussis using adaptive control. J Biotechnol 125(2):252–268CrossRefGoogle Scholar
  24. 24.
    Bodizs L, Titica M, Faria N, Srinivasan B, Dochain D, Bonvin D (2007) Oxygen control for an industrial pilot-scale fed-batch filamentous fungal fermentation. J Process Control 17(7):595–606CrossRefGoogle Scholar
  25. 25.
    Chung YC, Chien IL, Chang DM (2006) Multiple-model control strategy for a fed-batch high cell-density culture processing. J Process Control 16(1):9–26CrossRefGoogle Scholar
  26. 26.
    Ranjan AP, Gomes J (2009) Simultaneous dissolved oxygen and glucose regulation in fed-batch methionine production using decoupled input output linearizing control. J Process Control 19(4):664–677CrossRefGoogle Scholar
  27. 27.
    Davison E (1976) Multivariable tuning regulators: the feedforward and robust control of a general servomechanism problem. IEEE Trans Autom Control 21(1):35–47. doi: 10.1109/tac.1976.1101126 CrossRefGoogle Scholar
  28. 28.
    Penttinen J, Koivo HN (1980) Multivariable tuning regulators for unknown systems. Automatica 16(4):393–398CrossRefGoogle Scholar
  29. 29.
    Maciejowski JM (1989) Multivariable feedback design. Addison-Wiley, Upper Saddle RiverGoogle Scholar
  30. 30.
    Bastin G, Dochain D (1990) On-line estimation and adaptive control of bioreactors, vol 1. Elsevier, AmsterdamGoogle Scholar
  31. 31.
    Roeva O, Slavov T, Dimov I, Dimova S, Kolkovska N (2011) Fed-batch cultivation control based on genetic algorithm PID controller tuning numerical methods and applications. vol 6046. Lecture Notes in Computer Science. Springer, Berlin, pp 289–296. doi:10.1007/978-3-642-18466-6_34 Google Scholar
  32. 32.
    Roeva O (2008) Improvement of genetic algorithm performance for identification of cultivation process models. Paper presented at the proceedings of the 9th WSEAS international conference on evolutionary computing, Sofia, BulgariaGoogle Scholar
  33. 33.
    Roeva O (2005) Genetic algorithms for a parameter estimation of a fermentation process model: a comparison. Bioautomation 3:19–28Google Scholar
  34. 34.
    Perrier M, de Azevedo SF, Ferreira EC, Dochain D (2000) Tuning of observer-based estimators: theory and application to the on-line estimation of kinetic parameters. Control Eng Pract 8(4):377–388CrossRefGoogle Scholar
  35. 35.
    Mazouni D, Ignatova M, Harmand J (2004) A simple mass balance model for biological sequencing batch reactors used for carbon and nitrogen removal. Automatic systems for building the infrastructure in developing countries, vol IFAC-DECOM04. Bansko, BulgariaGoogle Scholar
  36. 36.
    Lyubenova V, Ignatova M, Novak M, Patarinska T (2007) Reaction rates estimators of fed-batch process for poly- b-hydroxybutyrate (PHB) production by mixed culture. Biotechnol BioE 21(1):113–116Google Scholar
  37. 37.
    Ignatova M, Lyubenova V (2007) Control of class bioprocesses using on-line information of intermediate metabolite production and con-sumption rates. Acta universitatis cibiniensis Series E: Food Technol 11:3–16Google Scholar
  38. 38.
    Ignatova M, Lyubenova V (2007) Adaptive control of fed-batch process for poly-b hydroxybutyrate production by mixed culture. Acad Sci 60(5):517–524Google Scholar
  39. 39.
    Kansha Y, Jia L, Chiu MS (2008) Self-tuning PID controllers based on the Lyapunov approach. Chem Eng Sci 63(10):2732–2740CrossRefGoogle Scholar
  40. 40.
    Chang WD, Hwang RC, Hsieh JG (2002) A self-tuning PID control for a class of nonlinear systems based on the Lyapunov approach. J Process Control 12(2):233–242. doi: 10.1016/s0959-1524(01)00041-5 CrossRefGoogle Scholar
  41. 41.
    Renard F, Vande Wouwer A, Valentinotti S, Dumur D (2006) A practical robust control scheme for yeast fed-batch cultures—an experimental validation. J Process Control 16(8):855–864. doi: 10.1016/j.jprocont.2006.02.003 CrossRefGoogle Scholar
  42. 42.
    Renard F, Vande Wouwer A (2008) Robust adaptive control of yeast fed-batch cultures. Comput Chem Eng 32(6):1238–1248. doi: 10.1016/j.compchemeng.2007.05.008 CrossRefGoogle Scholar
  43. 43.
    Dewasme L, Richelle A, Dehottay P, Georges P, Remy M, Bogaerts P, Vande Wouwer A (2010) Linear robust control of S. cerevisiae fed-batch cultures at different scales. Biochem Eng J 53(1):26–37. doi: 10.1016/j.bej.2009.10.001
  44. 44.
    Cannizzaro C, Valentinotti S, von Stockar U (2004) Control of yeast fed-batch process through regulation of extracellular ethanol concentration. Bioprocess Biosyst Eng 26(6):377–383. doi: 10.1007/s00449-004-0384-y CrossRefGoogle Scholar
  45. 45.
    Valentinotti S, Srinivasan B, Holmberg U, Bonvin D, Cannizzaro C, Rhiel M, von Stockar U (2003) Optimal operation of fed-batch fermentations via adaptive control of overflow metabolite. Control Eng Pract 11(6):665–674. doi: 10.1016/s0967-0661(02)00172-7 CrossRefGoogle Scholar
  46. 46.
    Hocalar A, Tüker M (2010) Model based control of minimal overflow metabolite in technical scale fed-batch yeast fermentation. Biochem Eng J 51(1):64–71CrossRefGoogle Scholar
  47. 47.
    Ruano MV, Ribes J, Seco A, Ferrer J (2012) An advanced control strategy for biological nutrient removal in continuous systems based on pH and ORP sensors. Chem Eng J 183:212–221. doi: 10.1016/j.cej.2011.12.064 CrossRefGoogle Scholar
  48. 48.
    Causa J, Karer G, Núnez A, Sáez D, Skrjanc I, Zupancic B (2008) Hybrid fuzzy predictive control based on genetic algorithms for the temperature control of a batch reactor. Comput Chem Eng 32(12):3254–3263CrossRefGoogle Scholar
  49. 49.
    Potocnik B, Music G, Zupancic B (2004) Model predictive control systems with discrete inputs. In: Electrotechnical conference, 2004. MELECON 2004. Proceedings of the 12th IEEE mediterranean, 12–15 May 2004, pp 383–386 vol 381.doi: 10.1109/melcon.2004.1346886
  50. 50.
    Karer G, Mušič G, Škrjanc I, Zupančič B (2007) Hybrid fuzzy modelling for model predictive control. J Intell Robot Syst 50(3):297–319. doi: 10.1007/s10846-007-9166-5 CrossRefGoogle Scholar
  51. 51.
    Karer G, Musšič G, Škrjanc I, Zupančič B (2007) Hybrid fuzzy model-based predictive control of temperature in a batch reactor. Comput Chem Eng 31(12):1552–1564CrossRefGoogle Scholar
  52. 52.
    Cosenza B, Galluzzo M (2011) Nonlinear fuzzy control of a fed-batch reactor for penicillin production. Comput Chem Eng 36:273–281CrossRefGoogle Scholar
  53. 53.
    Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybernet 15(1):116–132CrossRefGoogle Scholar
  54. 54.
    Belchior CAC, Araujo RAM, Landeck JAC (2011) Dissolved oxygen control of the activated sludge wastewater treatment process using stable adaptive fuzzy control. Comput Chem Eng 37:152–162CrossRefGoogle Scholar
  55. 55.
    Karakuzu C, Türker M, Öztürk S (2006) Modelling, on-line state estimation and fuzzy control of production scale fed-batch baker’s yeast fermentation. Control Eng Pract 14(8):959–974CrossRefGoogle Scholar
  56. 56.
    Gadkar KG, Mehra S, Gomes J (2005) On-line adaptation of neural networks for bioprocess control. Comput Chem Eng 29(5):1047–1057CrossRefGoogle Scholar
  57. 57.
    Ashoori A, Moshiri B, Khaki-Sedigh A, Bakhtiari MR (2009) Optimal control of a nonlinear fed-batch fermentation process using model predictive approach. J Process Control 19(7):1162–1173CrossRefGoogle Scholar
  58. 58.
    Birol G, Ündey C, Cinar A (2002) A modular simulation package for fed-batch fermentation: penicillin production. Comput Chem Eng 26(11):1553–1565CrossRefGoogle Scholar
  59. 59.
    Santos LO, Dewasme L, Coutinho D, Wouwer AV (2011) Nonlinear model predictive control of fed-batch cultures of micro-organisms exhibiting overflow metabolism: assessment and robustness. Comput Chem Eng 39:143–151CrossRefGoogle Scholar
  60. 60.
    Xu Z, Zhao J, Qian J, Zhu Y (2009) Nonlinear MPC using an identified LPV model. Ind Eng Chem Res 48(6):3043–3051. doi: 10.1021/ie801057q CrossRefGoogle Scholar
  61. 61.
    Gong Z (2009) A multistage system of microbial fed-batch fermentation and its parameter identification. Math Comput Simul 80(9):1903–1910CrossRefGoogle Scholar
  62. 62.
    Lawryńczuk M (2011) Online set-point optimisation cooperating with predictive control of a yeast fermentation process: a neural network approach. Eng Appl Artif Intell 24(6):968–982. doi: 10.1016/j.engappai.2011.04.007 CrossRefGoogle Scholar
  63. 63.
    Nelles O (2001) Nonlinear system identification. Springer, BerlinGoogle Scholar
  64. 64.
    Meleiro LAC, Von Zuben FJ, Filho RM (2009) Constructive learning neural network applied to identification and control of a fuel-ethanol fermentation process. Eng Appl Artif Intell 22(2):201–215. doi: 10.1016/j.engappai.2008.06.001 CrossRefGoogle Scholar
  65. 65.
    Velut S, de Marco L, Hagander P (2007) Bioreactor control using a probing feeding strategy and mid-ranging control. Control Eng Pract 15(2):135–147CrossRefGoogle Scholar
  66. 66.
    Velut S, Castan A, Short KA, Axelsson JP, Hagander P, Zditosky BA, Rysenga CW, De Maré L, Haglund J (2007) Influence of bioreactor scale and complex medium on probing control of glucose feeding in cultivations of recombinant strains of Escherichia coli. Biotechnol Bioeng 97(4):816–824. doi: 10.1002/bit.21294 CrossRefGoogle Scholar
  67. 67.
    Xue WJ, Fan DD (2011) Fed-batch production of human-like collagen with recombinant Escherichia coli using feed-up DO-transient control. Huaxue Gongcheng/Chem Eng (China) 39(10):6–10Google Scholar
  68. 68.
    Dochain D, Perrier M, Guay M (2011) Extremum seeking control and its application to process and reaction systems: a survey. Math Comput Simul 82(3):369–380. doi: 10.1016/j.matcom.2010.10.022 CrossRefGoogle Scholar
  69. 69.
    Cougnon P, Dochain D, Guay M, Perrier M (2011) On-line optimization of fedbatch bioreactors by adaptive extremum seeking control. J Process Control 21(10):1526–1532. doi: 10.1016/j.jprocont.2011.05.004 CrossRefGoogle Scholar
  70. 70.
    Dewasme L, Srinivasan B, Perrier M, Vande Wouwer A (2011) Extremum-seeking algorithm design for fed-batch cultures of microorganisms with overflow metabolism. J Process Control 21(7):1092–1104. doi: 10.1016/j.jprocont.2011.05.002 CrossRefGoogle Scholar
  71. 71.
    Hantelmann K, Kollecker M, Hüll D, Hitzmann B, Scheper T (2006) Two-dimensional fluorescence spectroscopy: a novel approach for controlling fed-batch cultivations. J Biotechnol 121(3):410–417. doi: 10.1016/j.jbiotec.2005.07.016 CrossRefGoogle Scholar
  72. 72.
    Schenk J, Marison IW, von Stockar U (2007) A simple method to monitor and control methanol feeding of Pichia pastoris fermentations using mid-IR spectroscopy. J Biotechnol 128(2):344–353. doi: 10.1016/j.jbiotec.2006.09.015 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Process Analytics and Cereal Technology, Institute of Food Science and BiotechnologyUniversity of HohenheimStuttgartGermany

Personalised recommendations