Optimization of Greenhouse Microclimate Parameters Considering the Impact of CO2 and Light | Journal of Engineering Sciences

Optimization of Greenhouse Microclimate Parameters Considering the Impact of CO2 and Light

Author(s): Sokolov S.

Affiliation(s): Sumy State University, 2, Rymskogo-Korsakova St., 40007 Sumy, Ukraine

*Corresponding Author’s Address: [email protected]

Issue: Volume 10, Issue 1 (2023)

Dates:
Submitted: March 9, 2023
Received in revised form: May 5, 2023
Accepted for publication: May 12, 2023
Available online: May 16, 2023

Citation:
Sokolov S. (2023). Optimization of greenhouse microclimate parameters considering the impact of CO2 and light. Journal of Engineering Sciences, Vol. 10(1), pp. G14-G21, doi: 10.21272/jes.2023.10(1).g2

DOI: 10.21272/jes.2023.10(1).g2

Research Area:  CHEMICAL ENGINEERING: Energy Efficient Technologies

Abstract. The most critical parameters of the microclimate in greenhouses are air and soil temperature, air and soil moisture, plant illumination, and carbon dioxide (CO2) concentration in photosynthesis. New energy sources and resource-efficient management of microclimate parameters in greenhouses can be utilized to reduce greenhouse crop cultivation costs and increase profits. As the plant mass increase depends on photosynthesis, which involves the formation of glucose in the plant chloroplasts from water and carbon dioxide under the influence of light radiation, the saturation of greenhouses with carbon dioxide has become popular in recent decades. However, insufficient light slows down the process of glucose formation, while excessive light intensity negatively affects photosynthesis. Based on the experimentally proven Van Henten model of plant growth and using the MATLAB environment, a methodology was proposed, and the dependence between CO2 concentration and leaf lettuce illumination power required for maximum photosynthesis was determined. It is equal to 0.57 ppm/(W/m2). Such dependence should be considered when designing control systems to reduce resource and energy costs for greenhouse crop cultivation while ensuring maximum yield.

Keywords: greenhouse gas, illumination, greenhouse effect, photosynthesis, energy efficiency, process innovation.

References:

  1. Kläring, H. P., Becker, C., Wünsche, J. N., Lenz, R., & Dietrich, P. (2007). Model-based control of CO2 concentration in greenhouses at ambient levels increases cucumber yield. Agricultural and Forest Meteorology, 143(3-4), 208-216. doi: 10.1016/j.agrformet.2006.12.002
  2. Singh, H., Poudel, M. R., Dunn, B., Fontanier, C., & Kakani, G. (2020). Greenhouse carbon dioxide supplementation with irrigation and fertilization management of geranium and fountain grass. HortScience, 55(11), 1772-1780. doi: 10.21273/HORTSCI15327-20
  3. Idso, S. B., & Idso, K. E. (2001). Effects of atmospheric CO2 enrichment on plant constituents related to animal and human health. Environmental and Experimental Botany, 45(2), 179-199. doi: 10.1016/S0098-8472(00)00091-5
  4. Streck, N. A. (2005). Climate change and agroecosystems: The effect of elevated atmospheric CO2 and temperature on crop growth, development, and yield. Ciência Rural, 35(3), 730-740. doi: 10.1590/S0103-84782005000300041
  5. Taub, D. R. (2010). Effects of rising atmospheric concentrations of carbon dioxide on plants. Nature Education Knowledge, 3(10), 21.
  6. Kumari, M., Verma, S. K., Bhardwaj, S., Thakur, A., Gupta, R., & Sharma, R. (2016). Effect of elevated CO2 and temperature on growth parameters of pea (Pisum sativum L.) crop. Journal of Applied and Natural Science, 8(4), 1941-1946. doi: 10.31018/jans.v8i4.1067
  7. Ullah, I., Fayaz, M., Aman, M., Qadir, J., Ali, S., & Ahmad, S. (2022). An optimization scheme for IoT-based smart greenhouse climate control with efficient energy consumption. Computing, 104(1), 433-457. doi: 10.1007/s00607-021-00963-5
  8. Su, Y., Xu, L., & Goodman, E. D. (2017). Nearly dynamic programming NN-approximation-based optimal control for greenhouse climate: A simulation study. Optimal Control Applications and Methods, 39(2), 638–662. doi: 10.1002/oca.2370
  9. Van Henten, E. J. (2003). Sensitivity analysis of an optimal control problem in greenhouse climate management. Biosystems Engineering, 85(3), 355-364. doi: 10.1016/S1537-5110(03)00068-0
  10. Stanghellini, C. (2014). Horticultural production in greenhouses: Efficient use of water. Acta Horticulturae, 1034, 25-32. doi: 10.17660/ActaHortic.2014.1034.1
  11. Van Beveren, P. J. M., Bontsema, J., Van Straten, G., & Van Henten, E. J. (2015). Minimal heating and cooling in a modern rose greenhouse. Applied Energy, 137, 97–109. https://doi.org/10.1016/j.apenergy.2014.09.083
  12. Caponetto, R., Fortuna, L., Nunnari, G., Occhipinti, L., & Xibilia, M. G. (2001). Soft computing for greenhouse climate control. IEEE Transactions on Fuzzy Systems, 9(4), 713-720. https://doi.org/10.1109/91.890333
  13. Ben Ali, R., Aridhi, E., & Mami, A. (2015). Dynamic model of an agricultural greenhouse using Matlab-Simulink environment. In 2015 12th International Multi-Conference on Systems, Signals & Devices (SSD) (pp. 346-350). https://doi.org/10.1109/STA.2015.7505185
  14. Katırcıoğlu, F. (2019). Control and monitoring of greenhouse system with Matlab GUI. International Journal of Scientific and Technological Research, 5(3), 95-100.
  15. Atia, D., & Tolba, H. (2017). Analysis and design of greenhouse temperature control using adaptive neuro-fuzzy inference system. International Journal of Advanced Research in Computer Science and Software Engineering, 7(4), 34-48.
  16. Taki, M., Ajabshirchi, Y., Ranjbar, F., Rohani, A., & Matloobi, M. (2016). Modeling and experimental validation of heat transfer and energy consumption in an innovative greenhouse structure. Information Processing in Agriculture, 3(1), 20-32. https://doi.org/10.1016/j.inpa.2016.06.002
  17. USC. (n.d.). How do increased carbon dioxide levels affect plant growth? https://csef.usc.edu/history/projects/J2321/
  18. O’Carrigan, A., Hinde, E., Lu, N., Xu, X.-Q., Duan, H., Huang, G., Mak, M., Bellotti, W., & Chen, Z.-H. (2014). Effects of light irradiance on stomatal regulation and growth of tomato. Environmental and Experimental Botany, 98, 65-73. https://doi.org/10.1016/j.envexpbot.2013.10.007
  19. Effat, M. B., Shafey, H. M., & Nassib, A. M. (2015). Solar greenhouses can be promising candidate for CO2 capture and utilization: Mathematical modeling. International Journal of Energy and Environmental Engineering, 6(3), 295-308. https://doi.org/10.1007/s40095-015-0175-z
  20. Van Henten, E. J. (1994). Validation of a dynamic lettuce growth model for greenhouse climate control. Agricultural Systems, 45(1), 55–72. https://doi.org/10.1016/S0308-521X(94)90280-1
  21. López-Cruz, I., Fitz-Rodríguez, E., Raquel, S., Rojano-Aguilar, A., & Kacira, M. (2018). Development and analysis of dynamical mathematical models of greenhouse climate: A review. European Journal of Horticultural Science, 83, 269-279. https://doi.org/10.17660/eJHS.2018/83.5.1
  22. Katzin, D., van Henten, E. J., et al. (2022). Process-based greenhouse climate models: Genealogy, current status, and future directions. Agricultural Systems, 198, 104124. https://doi.org/10.1016/j.agsy.2022.104124
  23. Rezvani, S. M.-E.-D., Jafari, A., Ghoosheh, E. Z., et al. (2021). Greenhouse crop simulation models and microclimate control systems, a review. In Next-Generation Greenhouses for Food Security. IntechOpen. https://doi.org/10.5772/intechopen.97361
  24. Blank, D. (2015). Global warming and global change: Facts and myths. International Journal of Earth Science and Geophysics, 1(4), 1-4. https://doi.org/10.15436/2381-0697.15.004
  25. Van Henten, E. J. (1994). Greenhouse climate management: An optimal control approach. Wageningen University and Research.

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