IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i21p4056-d279913.html
   My bibliography  Save this article

Modeling and Simulation of Temperature and Relative Humidity Inside a Growth Chamber

Author

Listed:
  • Germán Díaz-Flórez

    (Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98000, Mexico)

  • Jorge Mendiola-Santibañez

    (Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico)

  • Luis Solís-Sánchez

    (Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98000, Mexico)

  • Domingo Gómez-Meléndez

    (Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98000, Mexico)

  • Ivan Terol-Villalobos

    (Facultad de Informática, Universidad Autónoma de Querétaro, Querétaro 76230, Mexico)

  • Hector Gutiérrez-Bañuelos

    (Unidad Académica de Medicina Veterinaria y Zootecnia, Universidad Autónoma de Zacatecas, Zacatecas 98500, Mexico)

  • Ma. Araiza-Esquivel

    (Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98000, Mexico)

  • Gustavo Espinoza-García

    (Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98000, Mexico)

  • Juan García-Escalante

    (IDGreen Company, Querétaro 76915, Mexico)

  • Carlos Olvera-Olvera

    (Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98000, Mexico)

Abstract

Modeling and simulation of internal variables such as temperature and relative humidity are relevant for designing future climate control systems. In this paper, a mathematical model is proposed to predict the internal variables temperature and relative humidity (RH) of a growth chamber (GCH). Both variables are incorporated in a set of first-order differential equations, considering an energy-mass balance. The results of the model are compared and assessed in terms of the coefficients of determination (R 2 ) and the root mean squared error (RMSE). The R 2 and RMSE computed were R 2 = 0.96, R 2 = 0.94, RMSE = 0.98 °C, and RMSE = 1.08 °C, respectively, for the temperature during two consecutive weeks; and R 2 = 0.83, R 2 = 0.81, RMSE = 5.45%RH, and RMSE = 5.48%RH, respectively, for the relative humidity during the same period. Thanks to the passive systems used to control internal conditions, the growth chamber gives average differences between inside and outside of +0.34 °C for temperature, and +15.7%RH for humidity without any climate control system. Operating, the GCH proposed in this paper produces 3.5 kg of wet hydroponic green forage (HGF) for each kilogram of seed (corn or barley) harvested on average.

Suggested Citation

  • Germán Díaz-Flórez & Jorge Mendiola-Santibañez & Luis Solís-Sánchez & Domingo Gómez-Meléndez & Ivan Terol-Villalobos & Hector Gutiérrez-Bañuelos & Ma. Araiza-Esquivel & Gustavo Espinoza-García & Juan , 2019. "Modeling and Simulation of Temperature and Relative Humidity Inside a Growth Chamber," Energies, MDPI, vol. 12(21), pages 1-22, October.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:21:p:4056-:d:279913
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/21/4056/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/21/4056/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. van Beveren, P.J.M. & Bontsema, J. & van Straten, G. & van Henten, E.J., 2015. "Optimal control of greenhouse climate using minimal energy and grower defined bounds," Applied Energy, Elsevier, vol. 159(C), pages 509-519.
    2. Olofsson, Thomas & Mahlia, T.M.I., 2012. "Modeling and simulation of the energy use in an occupied residential building in cold climate," Applied Energy, Elsevier, vol. 91(1), pages 432-438.
    3. Nebbali, R. & Roy, J.C. & Boulard, T., 2012. "Dynamic simulation of the distributed radiative and convective climate within a cropped greenhouse," Renewable Energy, Elsevier, vol. 43(C), pages 111-129.
    4. Jiaming Guo & Yanhua Liu & Enli Lü, 2019. "Numerical Simulation of Temperature Decrease in Greenhouses with Summer Water-Sprinkling Roof," Energies, MDPI, vol. 12(12), pages 1-15, June.
    5. Abdel-Ghany, Ahmed M. & Kozai, Toyoki, 2006. "Dynamic modeling of the environment in a naturally ventilated, fog-cooled greenhouse," Renewable Energy, Elsevier, vol. 31(10), pages 1521-1539.
    6. Dayan, J & Dayan, E & Strassberg, Y & Presnov, E, 2004. "Simulation and control of ventilation rates in greenhouses," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 65(1), pages 3-17.
    7. Linker, Raphael & Seginer, Ido, 2004. "Greenhouse temperature modeling: a comparison between sigmoid neural networks and hybrid models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 65(1), pages 19-29.
    8. Yongtao Shen & Ruihua Wei & Lihong Xu, 2018. "Energy Consumption Prediction of a Greenhouse and Optimization of Daily Average Temperature," Energies, MDPI, vol. 11(1), pages 1-17, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gloria Alexandra Ortiz Rocha & Maria Angelica Pichimata & Edwin Villagran, 2021. "Research on the Microclimate of Protected Agriculture Structures Using Numerical Simulation Tools: A Technical and Bibliometric Analysis as a Contribution to the Sustainability of Under-Cover Cropping," Sustainability, MDPI, vol. 13(18), pages 1-40, September.
    2. Zhang, Guanshan & Ding, Xiaoming & Li, Tianhua & Pu, Wenyang & Lou, Wei & Hou, Jialin, 2020. "Dynamic energy balance model of a glass greenhouse: An experimental validation and solar energy analysis," Energy, Elsevier, vol. 198(C).
    3. Zhang, Yue & Henke, Michael & Li, Yiming & Yue, Xiang & Xu, Demin & Liu, Xingan & Li, Tianlai, 2020. "High resolution 3D simulation of light climate and thermal performance of a solar greenhouse model under tomato canopy structure," Renewable Energy, Elsevier, vol. 160(C), pages 730-745.
    4. Morice R. O. Odhiambo & Adnan Abbas & Xiaochan Wang & Ehsan Elahi, 2020. "Thermo-Environmental Assessment of a Heated Venlo-Type Greenhouse in the Yangtze River Delta Region," Sustainability, MDPI, vol. 12(24), pages 1-34, December.
    5. Chen, Han & Huang, Ye & Shen, Huizhong & Chen, Yilin & Ru, Muye & Chen, Yuanchen & Lin, Nan & Su, Shu & Zhuo, Shaojie & Zhong, Qirui & Wang, Xilong & Liu, Junfeng & Li, Bengang & Tao, Shu, 2016. "Modeling temporal variations in global residential energy consumption and pollutant emissions," Applied Energy, Elsevier, vol. 184(C), pages 820-829.
    6. Pisello, Anna Laura & Goretti, Michele & Cotana, Franco, 2012. "A method for assessing buildings’ energy efficiency by dynamic simulation and experimental activity," Applied Energy, Elsevier, vol. 97(C), pages 419-429.
    7. Barkat Rabbi & Zhong-Hua Chen & Subbu Sethuvenkatraman, 2019. "Protected Cropping in Warm Climates: A Review of Humidity Control and Cooling Methods," Energies, MDPI, vol. 12(14), pages 1-24, July.
    8. Chul-Ho Kim & Seung-Eon Lee & Kang-Soo Kim, 2018. "Analysis of Energy Saving Potential in High-Performance Building Technologies under Korean Climatic Conditions," Energies, MDPI, vol. 11(4), pages 1-34, April.
    9. Chiara Terrosi & Sonia Cacini & Gianluca Burchi & Maurizio Cutini & Massimo Brambilla & Carlo Bisaglia & Daniele Massa & Marco Fedrizzi, 2020. "Evaluation of Compressor Heat Pump for Root Zone Heating as an Alternative Heating Source for Leafy Vegetable Cultivation," Energies, MDPI, vol. 13(3), pages 1-15, February.
    10. Szalay, Zsuzsa & Zöld, András, 2014. "Definition of nearly zero-energy building requirements based on a large building sample," Energy Policy, Elsevier, vol. 74(C), pages 510-521.
    11. Jerónimo Ramos-Teodoro & Adrián Giménez-Miralles & Francisco Rodríguez & Manuel Berenguel, 2020. "A Flexible Tool for Modeling and Optimal Dispatch of Resources in Agri-Energy Hubs," Sustainability, MDPI, vol. 12(21), pages 1-24, October.
    12. Nadal, Ana & Llorach-Massana, Pere & Cuerva, Eva & López-Capel, Elisa & Montero, Juan Ignacio & Josa, Alejandro & Rieradevall, Joan & Royapoor, Mohammad, 2017. "Building-integrated rooftop greenhouses: An energy and environmental assessment in the mediterranean context," Applied Energy, Elsevier, vol. 187(C), pages 338-351.
    13. Ascione, Fabrizio & Bianco, Nicola & de’ Rossi, Filippo & Turni, Gianluca & Vanoli, Giuseppe Peter, 2013. "Green roofs in European climates. Are effective solutions for the energy savings in air-conditioning?," Applied Energy, Elsevier, vol. 104(C), pages 845-859.
    14. Ajagekar, Akshay & Decardi-Nelson, Benjamin & You, Fengqi, 2024. "Energy management for demand response in networked greenhouses with multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 355(C).
    15. Gourlis, Georgios & Kovacic, Iva, 2016. "A study on building performance analysis for energy retrofit of existing industrial facilities," Applied Energy, Elsevier, vol. 184(C), pages 1389-1399.
    16. Zilong Fan & Yiming Li & Lingling Jiang & Lu Wang & Tianlai Li & Xingan Liu, 2023. "Analysis of the Effect of Exhaust Configuration and Shape Parameters of Ventilation Windows on Microclimate in Round Arch Solar Greenhouse," Sustainability, MDPI, vol. 15(8), pages 1-30, April.
    17. Theodora Karanisa & Yasmine Achour & Ahmed Ouammi & Sami Sayadi, 2022. "Smart greenhouses as the path towards precision agriculture in the food-energy and water nexus: case study of Qatar," Environment Systems and Decisions, Springer, vol. 42(4), pages 521-546, December.
    18. Dafni Despoina Avgoustaki & George Xydis, 2020. "Plant factories in the water-food-energy Nexus era: a systematic bibliographical review," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 12(2), pages 253-268, April.
    19. Filogamo, Luana & Peri, Giorgia & Rizzo, Gianfranco & Giaccone, Antonino, 2014. "On the classification of large residential buildings stocks by sample typologies for energy planning purposes," Applied Energy, Elsevier, vol. 135(C), pages 825-835.
    20. Katzin, David & van Henten, Eldert J. & van Mourik, Simon, 2022. "Process-based greenhouse climate models: Genealogy, current status, and future directions," Agricultural Systems, Elsevier, vol. 198(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:21:p:4056-:d:279913. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.