Hybrid neural modelling of an anaerobic digester with respect to biological constraints

Water Science and Technology

Volumn 43, Issue 7, 2001, Pages 1-8

  Karama, A ^a   Bernard, O ^b   Gouze J L ^b   Benhammou, A ^a   Dochain, D ^c  

^a FACULTÉ DES SCIENCES SEMLALIA   (Morocco)

^b INRIA   (France)

^c Catholic University of Louvain   (Belgium)

Author keywords

Anaerobic digestion; Hybrid model; Mass balance modelling; Neural network

Indexed keywords

BACTERIA; ENZYME INHIBITION; FERMENTATION; NEURAL NETWORKS;

BIOLOGICAL CONSTRAINTS;

ANAEROBIC DIGESTION;

ANAEROBIC PROCESS; FERMENTATION; FIXED BED; MODELING;

ACID SECRETION; ALGORITHM; ANAEROBIC DIGESTION; ARTIFICIAL NEURAL NETWORK; BACTERIAL GROWTH; BACTERIUM; BIOREACTOR; CONCENTRATION (PARAMETERS); CONFERENCE PAPER; FERMENTATION; FLUIDIZED BED; GROWTH INHIBITION; GROWTH RATE; MATHEMATICAL PARAMETERS; METHANOGENESIS; NONHUMAN;

BACTERIA (MICROORGANISMS);

EID: 0035024096     PISSN: 02731223     EISSN: None     Source Type: Journal    
DOI: 10.2166/wst.2001.0375     Document Type: Conference Paper

References (11)

1. Bastin, G. and Dochain, D. (1990). On-line Estimation and Adaptative Control of Bioreactors. Elsevier.
2. Bernard, O., Hadj-Sadok, Z. and Dochain, D. (1999). Dynamical modelling and state estimation of anaerobic wastewater treatment plants. Proc. ECC99, (CDROM), F1028-1.
3. Bernard, O., Hadj-Sadok, Z. and Dochain, D. (2000). Advanced monitoring and control of aerobic wastewater treatment plants: Ii-dynamical model development and identification. Preprints of Watermatex 2000, pages 3.57-3.64. Gent, Belgium.
4. de Azevedo, F., Dalim, S. and Oliviera, F.R. (1997). Hybrid modelling of biochemical processes: a comparison with the conventional approach. Computers and Chemical Engineering. 21, S751-S756.
5. Hertz, J., Krogh, A. and Palmer, R. (1991). Introduction to the Theory of Neural Computation. Addison-Wesley.
6. Karama, A., Bernard, O., Genovesi, G., Dochain, D., Benhammou, A. and Steyer, J-P. (2001). Hybrid modelling of anaerobic wastewater treatment processes. Wat. Sci. Tech., 43(1), 43-50.
7. Leonard, J. and Kramer, M. (1990). Improvements of the backpropagation algorithm for training neural networks. Computers and Chemical Engineering, 819-835.
8. Psichogios, D. and Ungar, L. (1992). A hybrid neural network-first principles approach to process modelling. AIChe Journal, 10, 1499-1511.
9. Rumelhart, D. and McCelland, J. (1986). Learning Internal Representations by Error Propagation. Parallel Distributed Processing: Explorations in the Microstructure of Cognition. MIT Press, Cambridge, MA.
10. Schubert, J., Simutis, R., Dors, M., Havlik, I. and Lubbert, A. (1994a). Hybrid modelling of yeast production processes - Combination of a prior knowledge on different Levels of Sophistication. Chemical Engineering Technology, 17, 10-20.
11. Schubert, J., Simutis, R., Dors, M., Havlik, I. and Lubbert, A. (1994b). Bioprocess optimisation and control application of hybrid modelling. Journal of Biotechnology, 35, 51-68.