Sánchez López, Esperanza Paola | Universidad de Guanajuato |
Sánchez Hernández Bernardo, Luis A | Universidad de Guanajuato |
Gutiérrez Villalobos, J. Marcelino | Universidad de Guanajuato |
Zárate Castrejón, , J. Luis | Universidad de Guanajuato |
Vicente, Peña Caballero | Universidad de Guanajuato |
Vicente, Peña Caballero | Universidad Autónoma del Estado de Hidalgo |
https://doi.org/10.58571/CNCA.AMCA.2024.073
Resumen: The estimation of the states of a Saccharomyces cerevisiae (S. cerevisiae) biomass production process using glucose as carbon and energy source in CSTR-type bioreactor cultures was investigated. The biomass estimation was evaluated numerically by implementing the observer in the bioreactor model at a continuous regime. The observer was designed with two terms, one error proportional (Ke) and one sign type (Ksign(e)f(e)). The performance of the proposed observer was compared with the observer published by Bastin and Dochain. For different dilution rates, both observers showed equal performance or the same numerical conditions. Moreover, their response was evaluated for different initial conditions of the model and the estimators with a better performance index of the proposed observer.
¿Cómo citar?
Sánchez López, E.P., Sánchez Hernández Bernardo, L.A., Gutiérrez Villalobos, M., Zárate Castrejón, , J.L., López Pérez, P.A. & Peña Caballero, V. (2024). Observer Design for Saccharomyces Cerevisiae Fermentations. Memorias del Congreso Nacional de Control Automático 2024, pp. 428-433. https://doi.org/10.58571/CNCA.AMCA.2024.073
Palabras clave
Software sensors, Observer design, State estimation, Nonlinear systems, batch fermentation
Referencias
- Alvarado-Santos, E., Mata-Machuca, J. L., López-Pérez, P. A., Garrido-Moctezuma, R. A., Pérez-Guevara, F., & Aguilar-López, R. (2022).
- Comparative Analysis of a Family of Sliding Mode Observers under Real-Time Conditions for the Monitoring in the Bioethanol Production. Fermentation, 8(9), 446. https://doi.org/10.3390/fermentation8090446
- Bangi, M. S. F., Kao, K., & Kwon, J. S.-I. (2022). Physics informed neural networks for hybrid modeling of lab scale batch fermentation for β-carotene production using Saccharomyces cerevisiae. Chemical Engineering Research and Design, 179, 415–423. https://doi.org/10.1016/j.cherd.2022.01.041
- Bárzaga-Martell, L., Duarte-Mermoud, M. A., Ibáñez Espinel, F., Gamboa-Labbé, B., Saa, P. A., & Pérez Correa, J. R. (2021). A robust hybrid observer for monitoring high-cell density cultures exhibiting overflow metabolism. Journal of Process Control, 104, 112–125. https://doi.org/10.1016/j.jprocont.2021.06.006
- Chen, L., Bastin, G., & Dochain, D. (1990a). Parameter Identifiability of a Class of Non Linear Compartmental Models for Bioprocesses. IFAC Proceedings Volumes, 23(8), 259–263. https://doi.org/10.1016/S1474-6670(17)51429-9
- Chen, L., Bastin, G., & Dochain, D. (1990b). Structural identifiability of the yield parameters in nonlinear compartmental models for bioprocesses. 29th IEEE Conference on Decision and Control, 1074–1079 vol.2. https://doi.org/10.1109/CDC.1990.203767
- Dochain, D. (2003). State and parameter estimation in chemical and biochemical processes: a tutorial. Journal of Process Control, 13(8), 801–818. https://doi.org/10.1016/S0959-1524(03)00026-X
- Hu, A., Cong, S., Ding, J., Cheng, Y., & Mpofu, E. (2021). Differential Evolution Algorithm Based Self-adaptive Control Strategy for Fed-batch Cultivation of Yeast. Computer Systems Science and Engineering, 38(1), 65–77. https://doi.org/10.32604/csse.2021.016404
- Jacobus, A. P., Gross, J., Evans, J. H., Ceccato-Antonini, S. R., & Gombert, A. K. (2021). Saccharomyces cerevisiae strains used industrially for bioethanol production. Essays in Biochemistry, 65(2), 147–161. https://doi.org/10.1042/EBC20200160
- Kottelat, J., Freeland, B., & Dabros, M. (2021). Novel Strategy for the Calorimetry-Based Control of FedBatch Cultivations of Saccharomyces cerevisiae. Processes, 9(4), 723. https://doi.org/10.3390/pr9040723
- Lahue, C., Madden, A. A., Dunn, R. R., & Smukowski Heil, C. (2020). History and Domestication of Saccharomyces cerevisiae in Bread Baking. Frontiers in Genetics, 11. https://doi.org/10.3389/fgene.2020.584718
- Ostergaard, S., Olsson, L., & Nielsen, J. (2000). Metabolic Engineering of Saccharomyces cerevisiae. Microbiology and Molecular Biology Reviews, 64(1), 34–50. https://doi.org/10.1128/MMBR.64.1.34-50.2000
- Palomba, E., Tirelli, V., de Alteriis, E., Parascandola, P., Landi, C., Mazzoleni, S., & Sanchez, M. (2021). A cytofluorimetric analysis of a Saccharomyces cerevisiae population cultured in a fed-batch bioreactor. PLOS ONE, 16(6), e0248382. https://doi.org/10.1371/journal.pone.0248382
- Parapouli, M., Vasileiadi, A., Afendra, A.-S., & Hatziloukas, E. (2020). Saccharomyces cerevisiae and its industrial applications. AIMS Microbiology, 6(1), 1–32. https://doi.org/10.3934/microbiol.2020001
- Qin, Y., & Zhai, C. (2024). Global Stabilizing Control of a Continuous Ethanol Fermentation Process Starting from Batch Mode Production. Processes, 12(4), 819. https://doi.org/10.3390/pr12040819
- Spurgeon, S. K. (2008). Sliding mode observers: a survey. International Journal of Systems Science, 39(8), 751–764. https://doi.org/10.1080/00207720701847638
- Yousefi‐Darani, A., Paquet‐Durand, O., Hinrichs, J., & Hitzmann, B. (2021). Parameter and state estimation of backers yeast cultivation with a gas sensor array and unscented Kalman filter. Engineering in Life Sciences, 21(3–4), 170–180. https://doi.org/10.1002/elsc.202000058