Observer-based virtual sensors for microalgae cultures monitoring

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Authors

  • Laura Vélez-Landa Tecnológico Nacional de México/Instituto Tecnológico de Tuxtla Gutiérrez; Tuxtla Gutiérrez, Chiapas, México.
  • Carlos Manuel Astorga-Zaragoza Tecnológico Nacional de México/Centro Nacional de Investigación y Desarrollo Tecnológico; Cuernavaca, Morelos, México. https://orcid.org/0000-0002-4678-9006
  • Héctor Ricardo Hernández de-León Tecnológico Nacional de México/Instituto Tecnológico de Tuxtla Gutiérrez; Tuxtla Gutiérrez, Chiapas, México. https://orcid.org/0000-0003-1204-2251
  • Gloria Lilia Osorio-Gordillo Tecnológico Nacional de México/Centro Nacional de Investigación y Desarrollo Tecnológico; Cuernavaca, Morelos, México. https://orcid.org/0000-0002-3445-8867
  • José Roberto Bermúdez-Hernández Tecnológico Nacional de México/Instituto Tecnológico de Tuxtla Gutiérrez; Tuxtla Gutiérrez, Chiapas, México. https://orcid.org/0000-0002-5364-8949

DOI:

https://doi.org/10.56845/rebs.v1i1.5

Keywords:

microalgae, biodiesel, virtual sensors, mathematical model

Abstract

In this paper, a nonlinear observer design is presented for simultaneous parameter estimation and state variables estimation. The case of study is microalgae cultures for biodiesel generation, where reaction rates, biomass concentration, intracellular quota and nitrogen concentration are critical variables that provide information about the state of the process. However, these variables might be difficult to measure due to the lack of specific instruments, high sensor costs or infeasibility of installation in the process. Therefore, two observer-based virtual sensors are presented in this paper as an analytical alternative to perform estimation of the main important variables or parameters of the process: a nonlinear adaptive observer and a nonlinear high-gain observer. The observers are based on the Droop´s mathematical model that describes the ability of microalgae to store nutrients and the decoupling between substrate uptake and biomass growth. Numerical simulations are made in order to evaluate the performance of the proposed observers.

References

Akash, P., Bharat, G., Pankaj, P., and Beena, P. (2017). Microalgae: antiquity to era of integrated technology. Renewable and Sustainable Energy Reviews, 71, 535-547. DOI: https://doi.org/10.1016/j.rser.2016.12.081

Amalina N., Kee M., Uemura Y., Wei J. and Teong K. (2018). Harvesting and pre-treatment of microalgae cultivated in wastewater for biodiesel production: A review. Energy Conversion and Management, 171, 1416-1429. DOI: https://doi.org/10.1016/j.enconman.2018.06.074

Astorga-Zaragoza, C., Zavala-Río, A., Alvarado, V., Méndez-Ocaña, M. and Reyes-Reyes, J. (2007). Performance monitoring of heat exchangers via adaptive observers. Measurement, 40, 392-405. DOI: https://doi.org/10.1016/j.measurement.2006.06.009

Astorga-Zaragoza, C., Alvarado, V., Zavala-Río, A., Méndez, M., and Guerrero-Ramírez, G. (2008). Observer-based monitoring of heat exchangers. ISA Transaction, 47, 15-24. DOI: https://doi.org/10.1016/j.isatra.2007.05.003

Astorga-Zaragoza, C., Osorio-Gordillo, G., Reyes-Reyes, J., Madrigal-Espinosa, G. and Chadli, M. (2018). Takagi-Sugeno observers as an alternative to nonlinear observers for analytical redundancy. Application to a steam generator of a thermal power plant. International Journal of Fuzzy Systems, 20, 1756:1766. DOI: https://doi.org/10.1007/s40815-018-0481-8

Barreto, R. (2018). Fossil fuels, alternative energy and economic growth. Economic Modelling, 75, 196-220. DOI: https://doi.org/10.1016/j.econmod.2018.06.019

Benavides, M., Coutinho, D., Hantson, A., Van I.J., Vande Wouwer, A. (2015). Robust Luenberguer observers for microalgal cultures. Journal of Process Control, 36, 55-63. DOI: https://doi.org/10.1016/j.jprocont.2015.09.005

Besançon, G. (2000). Remarks on nonlinear adaptive observer design. Systems & Control Letters, 41 (4), 271-280. DOI: https://doi.org/10.1016/S0167-6911(00)00065-7

Bougaran G., Bernard O. and Sciandra A. (2010). Modeling continuous cultures of microalgae colimited by nitrogen and phosphorus. Journal of Theoretical Biology, 265 (3), 443-454. DOI: https://doi.org/10.1016/j.jtbi.2010.04.018

Ching C., Chien H., Kao, H., Ping, H., Meng, W., Jo, C. (2015). Biodiesel production from wet microalgae feedstock using sequential wet extraction/transesterification and direct transesterification process. Bioresource Technology, 194, 179-186. DOI: https://doi.org/10.1016/j.biortech.2015.07.021

Chisti, Y. (2007). Biodiesel from microalgae. Biotechnology Advances, 25 (3), 294-306. DOI: https://doi.org/10.1016/j.biotechadv.2007.02.001

Chun, C., Kuei Y., Rifka A., Duu, L., and Jo, C. (2011). Cultivation, photobioreactor design and harvesting of microalgae for biodiesel production: a critical review. Bioresource Technology, 102 (1), 71-81. DOI: https://doi.org/10.1016/j.biortech.2010.06.159

Coutinho, D., Vargas, A., Feudjio, C., Benavides, M., and Vande W.A. (2019). A robust approach to the design of super-twisting observers-aplication to monitoring microalgae cultures in photo-bioreactors. Computers and Chemical Engineering, 121 (2), 46-56. DOI: https://doi.org/10.1016/j.compchemeng.2018.07.006

De Battista, H., Picó, J., Garelli F., and Vignoni, A. (2011). Specific growth rate estimation in (fed-) batch bioreactors using second-order sliding observers. Journal of Process Control, 21 (7), 1049-1055. DOI: https://doi.org/10.1016/j.jprocont.2011.05.008

Demirbas A. and Fatih D.M. (2011). Importance of algae oil as a source biodiesel. Energy Conversion Management, 52, 163-170. DOI: https://doi.org/10.1016/j.enconman.2010.06.055

Demirbas, A, (2010). Use of algae as biofuel sources. Energy Conversion and Management, 51 (12), 2738-2749. DOI: https://doi.org/10.1016/j.enconman.2010.06.010

Dietzsch, C., Spadiut, O. and Herwig, C. (2011). A dynamic method based on the specific substrate uptake rate to set up a feeding strategy for Pichia pastoris. Microbial Cell Factories, 10 (14). DOI: https://doi.org/10.1186/1475-2859-10-14

Deshmukh S., Kumar R., Bala K. (2019). Microalgae biodiesel: A review oil extraction, fatty acid composition, properties and effect on engine performance and emissions. Fuel Processing Technology, 191, 232-247. DOI: https://doi.org/10.1016/j.fuproc.2019.03.013

Dochain, D. (2003). State and parameter estimation in chemical and biochemical processes: a tutorial. Journal of Process Control, 13 (8), 801-818. DOI: https://doi.org/10.1016/S0959-1524(03)00026-X

Droop, M.R. (1983). 25 Years of algal growth kinetics: a personal view. Botánica Marina, 26 (3), 99-12. DOI: https://doi.org/10.1515/botm.1983.26.3.99

Gauthier, P., Hammouri, H. and Othman, S. (1992). A simple observer for nonlinear systems application to bioreactors. IEEE Transactions on Automatic Control, 37 (6), 875-880. DOI: https://doi.org/10.1109/9.256352

Jenzsch, M., Simutis, R. and Luebbert, A. (2006). Generic model control of the specific growth rate in recombinant Escherichia coli cultivations. Journal of Biotechnology,122 (4), 483-493. DOI: https://doi.org/10.1016/j.jbiotec.2005.09.013

Leonard M., Michaelides E. and Michaelides D. (2020). Energy storage needs for the substitution of fossil fuel power plants with renewables. Renewable Energy, 145, 951-962. DOI: https://doi.org/10.1016/j.renene.2019.06.066

Mohd, A., Ha N., Hussain, A., Dochain, D, (2015). Review and classification of recent observers applied in chemical process systems. Computers and Chemical Engineering, 76, 27-41. DOI: https://doi.org/10.1016/j.compchemeng.2015.01.019

Mohammad, G. (2018). Microalgae: Prospects for Greener future buildings. Renewable and Sustainable Energy Reviews, 81, 1175-1191. DOI: https://doi.org/10.1016/j.rser.2017.08.032

Mohammadmatin, H., Mohammad-Hossein, S., Zahra, N., Omid, T., and Hamid, F. (2018). Technical, economic and energy assessment of an alternative strategy for mass production of biomass and lipid from microalgae. Journal of Environmental Chemical Engineering, 6 (1), 866-873. DOI: https://doi.org/10.1016/j.jece.2018.01.008

Nuñez, S., De Battista, H., Garelli, H., Vignoni, A., and Picó, J. (2013). Second-order sliding mode observer for multiple kinetic rates estimation in bioprocesses. Control Engineering Practice, 21 (9), 1259-1265. DOI: https://doi.org/10.1016/j.conengprac.2013.03.003

Ortiz-Torres G., Escobar-Jiménez R., Adam-Medina M., Sánchez-Coronado M., Astorga-Zaragoza C., Olivares-Peregrino V. and Téllez-Anguiano A. (2013). Detección y diagnóstico de fallas en sensores aplicado a una columna de destilación mediante un observador adaptable. En Congreso Nacional de Control Automático, Ensenada, Baja California, México.

Rincón, L., Jaramillo, C. and Cardona, C. (2014). Comparision of feedstocks and technologies for biodiesel production: An environmental and techno-economic evaluation. Renewable Energy, 69, 479-487. DOI: https://doi.org/10.1016/j.renene.2014.03.058

Shih, H., Shu, H., Chun, Y., Tomohisa, H., Akihiko, K., Jo, C. (2013). Characterization and optimization of carbohydrate production from and indigenous microalga Chlorella vulgaris FSP-E. Bioresource Technology, 135, 157-165. DOI: https://doi.org/10.1016/j.biortech.2012.10.100

Solimeno, A., Acién, G. and García J. (2017). Mechanistic model for design, analysis, operation and control of microalgae cultures: Calibration and application to tubular photobioreactors. Algal Research, 21, 236-246. DOI: https://doi.org/10.1016/j.algal.2016.11.023

Spolaore, P., Claire Joannis-Cassan, E., and Arsène, I, (2006). Commercial applications of microalgae. Journal of Bioscience and Bioengineering, 101 (2), 87-96. DOI: https://doi.org/10.1263/jbb.101.87

Téllez-Anguiano A., Astorga-Zaragoza, C., Targui, B., Aguilera-González, A., Reyes-Reyes, J. and Adam-Medina, M. (2009). Experimental validation of a high-gain observer for composition estimation in an etanol-water distillation column. Asia-Pacific Journal Chemical Engineering, 4 (6), 942-952. DOI: https://doi.org/10.1002/apj.345

Uganeeswary, S., Man L., Yoshimitsu, U., Jun, L., Keat, L., and Siew, S. (2019). Insights into the microalgae cultivation technology and harvesting process for biofuel production: a review. Renewable and Sustainable Energy Reviews, 115, 109-361. DOI: https://doi.org/10.1016/j.rser.2019.109361

Velez-Landa, L., Hernández, H., Astorga-Zaragoza, C., Bermúdez, J., López-Estrada, F. and Adam-Medina, M. (2019). Observador adaptable para la estimación simultánea de parámetros en cultivos de microalgas. En 3ª Jornada de Ciencia y Tecnología Aplicada, Cuernavaca Morelos, México.

Vojinovi, V., Cabral, J., and Fonseca, L. (2006). Real-time bioprocess monitoring. Part I: in situ sensors. Sensors and Actuators B: Chemical, 114, 1083–1091. DOI: https://doi.org/10.1016/j.snb.2005.07.059

Yoo J., Kim H. and Lee M. (2014). Dynamic modelling of mixotrophic microalgal photobioreactor systems with time-varying yield coefficient for the lipid consumption. Bioresource Technology, 162, 228-235. DOI: https://doi.org/10.1016/j.biortech.2014.03.128

Yuan S., Zhou X., Chen R. and Song B. (2014). Study on modelling microalgae growth in nitrogen-limited culture system for estimating biomass productivity. Renewable and Sustainable Energy Review, 34, 525-535. DOI: https://doi.org/10.1016/j.rser.2014.03.032

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Published

2019-11-15

How to Cite

Vélez-Landa, L., Astorga-Zaragoza, C. M., Hernández de-León, H. R., Osorio-Gordillo, G. L., & Bermúdez-Hernández, J. R. (2019). Observer-based virtual sensors for microalgae cultures monitoring. Renewable Energy, Biomass & Sustainability, 1(1), 17–27. https://doi.org/10.56845/rebs.v1i1.5

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Vol. 1 No. 1 (2019)

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Original Articles

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