Browse > Article
http://dx.doi.org/10.12791/KSBEC.2014.23.3.181

Predictive Model of Micro-Environment in a Naturally Ventilated Greenhouse for a Model-Based Control Approach  

Hong, Se-Woon (Division of M3-BIORES (Measure, Model & Manage Bioresponses), Department of Biosystems, KU Leuven)
Lee, In-Bok (Department of Rural Systems Engineering, Seoul National University)
Publication Information
Journal of Bio-Environment Control / v.23, no.3, 2014 , pp. 181-191 More about this Journal
Abstract
Modern commercial greenhouse requires the use of advanced climate control system to improve crop production and to reduce energy consumption. As an alternative to classical sensor-based control method, this paper introduces a model-based control method that consists of two models: the predictive model and the evaluation model. As a first step, this paper presents straightforward models to predict the effect of natural ventilation in a greenhouse according to meteorological factors, such as outdoor air temperature, soil temperature, solar radiation and mean wind speed, and structural factor, opening rate of roof ventilators. A multiple regression analysis was conducted to develop the predictive models on the basis of data obtained by computational fluid dynamics (CFD) simulations. The output of the models are air temperature drops due to ventilation at 9 sub-volumes in the greenhouse and individual volumetric ventilation rate through 6 roof ventilators, and showed a good agreement with the CFD-computed results. The resulting predictive models have an advantage of ensuring quick and reasonable predictions and thereby can be used as a part of a real-time model-based control system for a naturally ventilated greenhouse to predict the implications of alternative control operation.
Keywords
air temperature drop; computational fluid dynamics simulation; cooling time; multiple regression; ventilation rate;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Blasco, X., M. Martinez, J.M. Herrero, C. Ramos and J. Sanchis. 2007. Model-based predictive control of greenhouse climate for reducing energy and water consumption. Computers and Electronics in Agriculture 55:49-70.   DOI
2 Bournet, P.E. and T. Boulard. 2010. Effect of ventilator configuration on the distributed climate of greenhouses: A review of experimental and CFD studies. Computers and Electronics in Agriculture 74(2):195-217.   DOI
3 Chung, T.S., Y.B. Min and G.K. Moon. 2001. Temperature control of greenhouse using ventilation window adjustments by a fuzzy algorithm. Journal of Bio-Environment Control 10(1): 43-49. (in Korean).
4 Fisk, W.J. and A.T. De Almeida. 1998, Sensor-based demandcontrolled ventilation: a review, Energy and Buildings 29:35-45.   DOI
5 Mahdavi, A. and C. Proglhof. 2008. A model-based approach to natural ventilation. Building and Environment 43:620-627.   DOI   ScienceOn
6 Hong, S.W., I.B. Lee, H.S. Hwang, I.H. Seo, J.P. Bitog, J.I. Yoo, K.S. Kim, S.H. Lee, K.W. Kim and N.K. Yoon. 2008. Numerical simulation of ventilation efficiencies of naturally ventilated multi-span greenhouses in Korea. Transaction of ASABE 51(4):1417-1432.   DOI
7 Kacira, M., S. Sase and L. Okushima. 2004. Effects of side vents and span numbers on wind-induced natural ventilation of a gothic multi-span greenhouse. Japan Agricultural Research Quarterly 38(4):227-233.   DOI
8 Lee, I.B., N.K. Yun, T. Boulard, J.C. Roy, S.H. Lee, G.W. Kim, S.W. Hong and S.H. Sung. 2006. Development of an aerodynamic simulation for studying microclimate of plant canopy in greenhouse - (2) Development of CFD model to study the effect of tomato plants on internal climate of greenhouse. Journal of Bio-Environment Control 15(4):296-305. (in Korean).
9 Mahdavi, A. and C. Proglhof. 2005. A model-based method for the integration of natural ventilation in indoor climate systems operation. Proceeding of the $9^{th}$ International IBPSA Conference 2005:685-692.
10 Norton, T., D.W. Sun, J. Grant, R. Fallon and V. Dodd. 2007. Applications of computational fluid dynamics in the modelling and design of ventilation systems in the agricultural industry: A review. Bioresource Technology 98(12):2386-2414.   DOI   ScienceOn
11 Ntoula, E., N. Katsoulas, C. Kittas, A. Youssef, V. Exadaktylos and D. Berckmans. 2011. Data based modeling approach for greenhouse air temperature and relative humidity. Acta Horticulturae 952:67-72.
12 Vranken, E., R. Gevers, J.M. Aerts and D. Berckmans. 2005, Performance of model-based predictive control of the ventilation rate with axial fans. Biosystems Engineering 91(1):87-98.   DOI
13 Youssef, A., J. Dekock, N. Katsoulas, S. Eren Ozcan, C. Kittas and D. Berckmans. 2011. Data-based approach to model the dynamic behaviour of greenhouse tempeature. Acta Horticulturae 893:931-938.