Product identification in industrial batch fermentation using a variable forgetting factor

Abstract For reliable operation and the optimization of production, industrial fermentation processes require appropriate tools for monitoring the process in real time. This work presents the structure and operation of a soft sensor for the on-line monitoring of biomass and product concentration during salinomycin and bacitracin fermentation in an industrial, 80-m 3 batch reactor; moreover it provides a tool for evaluation of batch production verified in industrial application. The process estimation algorithm consists of decoupled growth and product models, which ensures an unbiased convergence of the estimator and the robustness of the model. The production of secondary metabolites is described with a non-structured model upgraded with a variable forgetting factor that demonstrated a successful estimation of the non-measured parameters and states of this highly interactive and interlinked system with complex dynamics. The possibility of using various input signals in product identification yields independent soft sensors. This serves to improve the reliability of the predictions, mutual sensor control and enables the detection of irregularities in the fermentation process before the broth becomes useless.

[1]  Urs von Stockar,et al.  A simple method to monitor and control methanol feeding of Pichia pastoris fermentations using mid-IR spectroscopy. , 2007, Journal of biotechnology.

[2]  Cihan Karakuzu,et al.  Modelling, on-line state estimation and fuzzy control of production scale fed-batch baker's yeast fermentation , 2006 .

[3]  Ana P Ferreira,et al.  Study of the application of multiway multivariate techniques to model data from an industrial fermentation process. , 2007, Analytica chimica acta.

[4]  Elmer Ccopa Rivera,et al.  Development of real-time state estimators for reaction–separation processes: A continuous flash fermentation as a study case , 2010 .

[5]  Zhongping Shi,et al.  On-line prediction of products concentrations in glutamate fermentation using metabolic network model and linear programming , 2005 .

[6]  Xionglin Luo,et al.  A novel calibration approach of soft sensor based on multirate data fusion technology , 2010 .

[7]  Zoltan K. Nagy,et al.  Evaluation study of an efficient output feedback nonlinear model predictive control for temperature tracking in an industrial batch reactor , 2007 .

[8]  Rolf Isermann,et al.  Adaptive control systems , 1991 .

[9]  Carl-Fredrik Mandenius,et al.  Evaluation of software sensors for on-line estimation of culture conditions in an Escherichia coli cultivation expressing a recombinant protein. , 2010, Journal of biotechnology.

[10]  Pratap R Patnaik,et al.  Can imperfections help to improve bioreactor performance? , 2002, Trends in biotechnology.

[11]  M. U. Estler.  Recursive on-line estimation of the specific growth rate from off-gas analysis for the adaptive control of fed-batch processes , 1995 .

[12]  Cenk Undey,et al.  Applied advanced process analytics in biopharmaceutical manufacturing: Challenges and prospects in real-time monitoring and control , 2010 .

[13]  José Ferrer,et al.  A methodology for sequencing batch reactor identification with artificial neural networks: A case study , 2009, Comput. Chem. Eng..

[14]  D. Dochain,et al.  On-Line Estimation and Adaptive Control of Bioreactors , 2013 .

[15]  D. Sbarbaro,et al.  Adaptive Soft-Sensors for On-Line Particle Size Estimation in Wet Grinding Circuits , 2004 .

[16]  J. R. Cueli,et al.  Iterative nonlinear model predictive control. Stability, robustness and applications , 2008 .

[17]  Michel Cabassud,et al.  Predictive functional control for the temperature control of a chemical batch reactor , 2006, Comput. Chem. Eng..

[18]  H Honda,et al.  Fuzzy control of bioprocess. , 2000, Journal of bioscience and bioengineering.

[19]  Thomas F. Edgar,et al.  State estimation in high-mix semiconductor manufacturing , 2009 .

[20]  Tao Yu,et al.  Unscented Transformation Based Robust Kalman Filter and Its Applications in Fermentation Process , 2010 .

[21]  Karl Schügerl,et al.  Bioreaction Engineering: Modeling and Control , 1987 .

[22]  Srinivas Karra,et al.  Alternative model structure with simplistic noise model to identify linear time invariant systems subjected to non-stationary disturbances , 2009 .

[23]  George Stephanopoulos,et al.  Wavelet‐based modulation in control‐relevant process identification , 1998 .

[24]  J. D. Stigter,et al.  Towards an adaptive model for greenhouse control , 2009 .

[25]  F. Girio,et al.  Model identification and physiological control of xylitol production using Debaryomyces hansenii , 2003 .

[26]  Reinhard Guthke,et al.  Monitoring of transcriptome and proteome profiles to investigate the cellular response of E. coli towards recombinant protein expression under defined chemostat conditions. , 2008, Journal of biotechnology.

[27]  Dae Sung Lee,et al.  Monitoring of sequencing batch reactor for nitrogen and phosphorus removal using neural networks , 2007 .

[28]  Amiya K. Jana,et al.  Nonlinear adaptive control algorithm for a multicomponent batch distillation column , 2007 .

[29]  Arthur E. Humphrey,et al.  Real‐time estimation of aerobic batch fermentation biomass concentration by component balancing , 1978 .

[30]  Wei Sun,et al.  A method for multiphase batch process monitoring based on auto phase identification , 2011 .

[31]  Rubens Maciel Filho,et al.  Soft sensors development for on-line bioreactor state estimation , 2000 .

[32]  Wang,et al.  On-line monitoring and controlling system for fermentation processes. , 2001, Biochemical engineering journal.

[33]  Sten Bay Jørgensen,et al.  A systematic approach for soft sensor development , 2007, Comput. Chem. Eng..

[34]  Joseph S. Alford,et al.  Bioprocess control: Advances and challenges , 2006, Comput. Chem. Eng..

[35]  Barry Lennox,et al.  Integrated condition monitoring and control of fed-batch fermentation processes , 2004 .

[36]  Xianzhong Dai,et al.  "Assumed inherent sensor" inversion based ANN dynamic soft-sensing method and its application in erythromycin fermentation process , 2006, Comput. Chem. Eng..

[37]  Michimasa Kishimoto,et al.  Application of fuzzy control to industrial bioprocesses in Japan , 2002, Fuzzy Sets Syst..

[38]  Igor Škrjanc,et al.  Self-adaptive supervisory predictive functional control of a hybrid semi-batch reactor with constraints , 2008 .

[39]  Thomas Scheper,et al.  A review of non-invasive optical-based image analysis systems for continuous bioprocess monitoring , 2010, Bioprocess and biosystems engineering.

[40]  T. R. Fortescue,et al.  Implementation of self-tuning regulators with variable forgetting factors , 1981, Autom..

[41]  Bart De Moor,et al.  Optimal adaptive control of fed-batch fermentation processes , 1995 .

[42]  B Lennox,et al.  Process monitoring of an industrial fed-batch fermentation. , 2001, Biotechnology and bioengineering.

[43]  Iztok Golobič,et al.  On‐line estimation of the specific growth rate in the bacitracin fermentation process , 1999 .

[44]  Ivan Bajsić,et al.  SOFTWARE SENSOR FOR BIOMASS CONCENTRATION MONITORING DURING INDUSTRIAL FERMENTATION , 2000 .

[45]  Sanjeev S. Tambe,et al.  Soft-sensor development for fed-batch bioreactors using support vector regression , 2006 .

[46]  Igor Škrjanc,et al.  Predictive functional control based on an adaptive fuzzy model of a hybrid semi-batch reactor , 2010 .