Online filtering of CO2 signals from a bioreactor gas outflow using a committee of constructive neural networks

Bioprocess and Biosystems Engineering

Volumn 31, Issue 2, 2008, Pages 101-109

  Giordano, Roberto C ^a   Bertini Jr , João R ^a   Nicoletti, Maria C ^a   Giordano, Raquel L C ^a  

^a FEDERAL UNIVERSITY OF SÃO CARLOS   (Brazil)

Author keywords

Bacillus megaterium cultivation; Constructive neural networks; Neural network based filters; Online signals filtering

Indexed keywords

ALGORITHMS; CARBON DIOXIDE; FLOW OF GASES; NEURAL NETWORKS; ONLINE SYSTEMS;

BACILLUS MEGATERIUM CULTIVATION; CONSTRUCTIVE NEURAL NETWORKS; NEURAL NETWORK BASED FILTERS; ONLINE SIGNALS FILTERING;

BIOREACTORS;

CARBON DIOXIDE; PENICILLIN AMIDASE;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BACILLUS MEGATERIUM; BACTERIUM CULTURE; BIOREACTOR; ENZYME ACTIVITY; GAS FLOW; NONHUMAN; PRIORITY JOURNAL; REACTOR MONITORING; AUTOMATED PATTERN RECOGNITION; FLOW INJECTION ANALYSIS; ISOLATION AND PURIFICATION; METABOLISM; METHODOLOGY; MICROBIOLOGY; SIGNAL PROCESSING;

BACILLUS MEGATERIUM;

ALGORITHMS; BACILLUS MEGATERIUM; BIOREACTORS; CARBON DIOXIDE; FLOW INJECTION ANALYSIS; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 38349107909     PISSN: 16157591     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00449-007-0152-x     Document Type: Article

References (30)

1. Patnaik PR (2001) Further enhancement of fed-batch streptokinase yield in the presence of inflow noise by coupled neural networks. Process Biochem 37:145-151
2. Patnaik PR (1999) Coupling of a neural filter and a neural controller for improvement of fermentation performance. Biotechnol Techn 12:735-738
3. Patnaik PR (1999) Improvement of the microbial production of streptokinase by controlled filtering of process noise. Process Biochem 35:309-315
4. Patnaik PR (2003) An integrated hybrid neural system for noise filtering, simulation and control of a fed-batch recombinant fermentation. Biochem Eng J 15:165-175
5. Baughman DR, Liu YA (1995) Neural networks in bioprocessing and chemical engineering. Academic, New York
6. Nelles O (2001) Nonlinear system identification. Springer, New York
7. Reed R (1993) Pruning algorithm-a survey. IEEE Trans Neural Netw 4(5):740-747
8. Gallant SI (1986) Three constructive algorithms for network learning. In: Proceedings of the eighth annual conference of the cognitive science society, Amherst, MA, pp 652-660
9. Mézard M, Nadal J (1989) Learning feedforward networks: the tiling algorithm. J Phys A Math Gen 22:2191-2203
10. Frean M (1990) The upstart algorithm: a method for constructing and training feedforward neural networks. Neural Comput 2:198-209
11. Kwok T-Y, Yeung D-Y (1999) Constructive algorithms for structure learning in feedforward neural networks for regression problems. IEEE Trans Neural Netw 8:630-645
12. Fahlman S, Lebiere C (1990) The Cascade Correlation architecture. In: Touretzky DS (eds) Advances in neural information processing systems 2. Morgan Kaufman, Los Altos, pp 524-532
13. Vanek M, Hrncirik P, Vovsik J, Nahlik J (2004) On-line estimation of biomass concentration using a neural network and information about metabolic state. Bioprocess Biosyst Eng 24:9-15
14. Savitzky A, Golay MJE (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36:1627-1639
15. Smith SW (1997) The scientist and engineering guide to digital signal processing. California Technical Production, San Diego
16. Cleveland WS (1979) Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc 74:829-836
17. Cleveland WS, Devlin SJ (1988) Locally-weighted regression: an approach to regression analysis by local fitting. J Am Stat Assoc 83:596-610
18. Wahba G (1990) Spline functions for observational data. SIAM, Philadelphia
19. Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19:1-141
20. Donoho D, Johnstone I (1994) Ideal spatial adaptation by wavelet shrinkage. Biometrika 81:425-455
21. Murata N, Yoshizawa S, Amari SI (1994) Network information criterion-determining the number of hidden units for an artificial neural network model. IEEE Trans Neural Netw 5:865-872
22. Ripley BD (1996) Pattern recognition and neural networks. Cambridge University Press, Cambridge
23. Renner RS (1990) Improving generalization of constructive neural networks using ensembles. Ph.D. Dissertation, Florida State University, Department of Computer Science, Tallahassee
24. Fahlman S (1988) Faster learning variations on backpropagation: an empirical study. In: Proceedings of the 1988 Connectionist Models Summer School, Morgan Kaufmann, Los Altos
25. Fahlman S (1988) An empirical study of learning speed in backpropagation networks. School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, Technical Report CMU-CS-88-162
26. Brigham EO (1988) The fast fourier transform and aplications. Prentice Hall, Englewood Cliffs
27. Silva RG, Souza VR, Nucci ER, Pinotti LM, Cruz AJG, Giordano RC, Giordano RLC (2006) Using a medium of free amino acids to produce penicillin G acylase in fed-batch cultivations of Bacillus megaterium ATCC 14945. Braz J Chem Eng 23:37-43
28. Pinotti LM, Souza VR, Giordano RC, Giordano RLC (2007) The Penicillin G acylase production by B. megaterium is amino acid consumption dependent. Biotechnol Bioeng 97(2):346-353
29. Giordano RC, Ribeiro MPA, Giordano RLC (2006) Kinetics of beta-lactam antibiotics synthesis by penicillin G acylase (PGA) from the viewpoint of the industrial enzymatic reactor optimization. Biotechnol Adv 24:27-41
30. Savidge TA (1984) Enzymatic conversions used in the production of Penicillins and Cephalosporins. In: Vandame EJ (ed) Biotechnology of industrial antibiotics. Marcel Dekker, New York