Biomass estimation in batch biotechnological processes by Bayesian Gaussian process regression

Computers and Chemical Engineering

Volumn 32, Issue 12, 2008, Pages 3264-3273

  di Sciascio, Fernando ^a   Amicarelli, Adriana N ^a  

^a UNIVERSIDAD NACIONAL DE SAN JUAN   (Argentina)

Author keywords

Bacillus thuringiensis; Bayesian regression; Biomass estimation; Biotechnological process; Extended Kalman filter; Gaussian process

Indexed keywords

BACTERIOLOGY; BAYESIAN NETWORKS; BIOLOGICAL MATERIALS; BIOMASS; CONCENTRATION (PROCESS); FOOD PROCESSING; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); RENEWABLE ENERGY RESOURCES; STAGES; TRELLIS CODES;

BACILLUS THURINGIENSIS; BAYESIAN; BAYESIAN REGRESSION; BIOMASS CONCENTRATIONS; BIOMASS DATA; BIOMASS ESTIMATION; BIOTECHNOLOGICAL PROCESS; BIOTECHNOLOGICAL PROCESSES; BOOTSTRAP TECHNIQUES; CONFIDENCE LEVEL; EXPERIMENTAL DATA; EXTENDED KALMAN FILTER; GAUSSIAN PROCESS; GAUSSIAN PROCESSES; PRODUCTION PROCESSES; TWO STAGES;

ESTIMATION;

EID: 52949129443     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compchemeng.2008.05.015     Document Type: Article

References (42)

1. Abrahamsen, P. (1997). A review of Gaussian random fields and correlation functions. Technical report 917 (2nd ed.). Oslo, Norway: Norwegian Computing Center.
2. Amicarelli A., di Sciascio F., Álvarez H., and Ortiz O. (In Spanish) Estimación de Biomasa en un proceso Batch: Aplicación a la producción de δ-endotoxinas de Bt. XXII Interamerican congress of chemical engineering. Buenos Aires, Argentina (2006)
3. Amicarelli, A., di Sciascio, F., & Álvarez, H. (2008). Including dissolved oxygen dynamics to the Bacillus thuringiensis δ-endotoxins production process. Bioprocess and Biosystems Engineering, submitted for publication.
4. Anderson B.D.O., and Moore J.B. Optimal filtering (1979), Prentice-Hall, Englewood Cliffs, New Jersey
5. Aronson A.I. The two faces of Bacillus thuringiensis: insecticidal proteins and post exponential survival. Molecular Microbiology 7 (1993) 489-496
6. Atehortúa P., Álvarez H., and Orduz S. Comments on: A sporulation kinetic model for batch growth of B. thuringiensis. The Canadian Journal of Chemical Engineering 84 3 (2006)
7. Atehortúa P., Alvarez H., and Orduz S. Modeling of growth and sporulation of Bacillus thuringiensis in an intermittent fed batch culture with total cell retention. Bioprocess and Biosystems Engineering 30 (2007) 447-456
8. Bastin G., and Dochain D. On-line estimation and adaptive control of bioreactors (1990), Elsevier, Amsterdam
9. Bernardo J.M., and Smith A.F. Bayesian theory. 2nd ed. (2006), Wiley, Chichester
10. Bishop, C. M. (1995, reprint 2005). Neural networks for pattern recognition. Oxford University Press.
11. Daum F. Nonlinear filters: Beyond the Kalman filter. IEEE Aerospace and Electronic Systems Magazine 20 8 (2005) 57-69 Part 2
12. Dochain D. State and Parameter Estimation in Chemical and Biochemical Processes: A Tutorial Journal of Process Control 13 (2003) 801-818
13. Ghribi D., Zouari N., Trabelsi H., and Jaoua S. Improvement of Bacillus thuringiensis delta-endotoxin production by overcome of carbon catabolite repression through adequate control of aeration. Enzyme and Microbial Technology 40 (2007) 614-622
14. Gibbs, M. N. (1997). Bayesian Gaussian processes for regression and classification. PhD thesis. University of Cambridge.
15. Golub G.H., and Van Loan C.F. Matrix computation (1990), John Hopkins University Press, Baltimore
16. Haykin S. Neural networks: A comprehensive foundation. 2nd ed. (1999), Prentice-Hall, Inc., New Jersey
17. Hastie T., Tibshirani R., and Friedman J.H. The elements of statistical learning (2001), Springer Verlag
18. Henson M.A. Biochemical reactor modeling and control. Control Systems Magazine, IEEE 26 4 (2006) 54-62
19. Henson M.A., and Seborg D.E. Nonlinear process control (1997), Prentice Hall
20. Jazwinski A.H. Stochastic processes and filtering theory (1970), Academic Press, Inc., New York
21. Kuss, M. (2006). Gaussian process models for robust regression, classification, and reinforcement learning. PhD thesis. Technische Universität Darmstadt.
22. Leal Ascencio R. Artificial neural networks as a biomass virtual sensor for a batch process. Proceedings of the 2001 IEEE interational symposium on intelligent control. September 5, Mexico City, Mexico (2001)
23. Li, B. (2003). Artificial neural network based software sensor for biomass during microorganism cultivation. PhD thesis. South China University of Technology.
24. Lind, I. (2006). Regressor and structure selection uses of ANOVA in system identification. PhD thesis. Sweden; Department of Electrical Engineering, University of Linköping.
25. Little R., and Rubin D. Statistical analysis with missing data (2002), Wiley, New York
26. Liu B.L., and Tzeng Y.M. Caracterization study of the sporulation kinetics of Bacillus thuringiensis. Biotechnology and Bioengineering 68 (2000) 11-17
27. Ljung L. Some aspects on nonlinear system identification. Proceedings of the 4th IFAC symposium on system identification, SYSID-2006. Newcastle, Australia, March 29-31 (2006)
28. MacKay D. Bayesian interpolation. Neural Computation (1992) 4-33
29. Mannale, R. (2006). Comparison of regressor selection methods in system identification. Dip. thesis. Sweden; Department of Electrical Engineering, University of Linköping.
30. Mardia K.V., and Marshall R.J. Maximum likelihood estimation of models for residual covariance in spatial regression. Biometrika (1984) 135-146
31. Narendra K., and Parthasarathy K. Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks (1990)
32. Neal R.M. Bayesian learning for neural networks (1996), Springer, New York
33. Neal, R. M. (1997). Monte Carlo implementation of GP models for bayesian regression and classification. Tech. Rep. University of Toronto.
34. O'Hagan A. Bayesian inference (Kendall's advanced theory of statistics 2B). 2nd ed. (2004), Edward Arnold, London
35. Rasmussen, C. E. (1996). Evaluation of Gaussian processes and other methods for nonlinear regression. PhD thesis. University of Toronto.
36. Rivera D., Margaritis A., and De Lasa H. A sporulation kinetic model for batch growth of B. thuringiensis. Canadian Journal of Chemical Engineering 77 (1999) 903-910
37. ∞, and nonlinear approaches (2006), John Wiley & Sons, Inc., Hoboken, New Jersey
38. Skilling J. Bayesian numerical analysis. In: Grandy W., and Milonni P. (Eds). Physics and probability (1993), Cambridge University Press
39. Starzak M., and Bajpai R. A structured model for vegetative growth and sporulation in Bacillus thuringiensis. Applied Biochemistry and Biotechnology 28/29 (1991) 699-718
40. Vallejo F., González A., Posada A., Restrepo A., and Orduz S. Production of Bacillus thuringiensis subsp. Medellín by batch and fed-batch culture. Biotechnology Techniques 13 (1999) 279-281
41. Williams K.I., and Rasmussen C.E. Gaussian processes for regression (1996), MIT Press
42. Wahba G. Spline models for observational data, Vol. 59. Series in applied mathematics (1990), SIAM, Philadelphia