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Vol. 102, Issue 3, March 2009, pp.84-93

 

 

Bullet

 

A Particle Swarm Optimization of Natural Ventilation Parameters in a Greenhouse with Continuous Roof Vents

 

1Abdelhafid Hasni, 1Belkacem Draoui, 2Thierry Boulard, 1Rachid TAIBI and 1brahim dennai

1 Institut de Génie Mécanique, Centre Universitaire de Béchar

B. P. 417, 08000 Béchar, Algérie

Tel.: 213 49 81 55 81/91, fax: 213 49 81 52 44

2 INRA-URIH 400, route des Chappes, BP 167, 06903 Sophia Antipolis, France

Tel.: (0)492386663, Fax: (0)493653318

E-mail: hasni_haf@yahoo.fr, boulard@sophia.inra.fr

 

 

Received: 15 December 2008   /Accepted: 24 March 2009   /Published: 31 March 2009

 

Abstract: Although natural ventilation plays an important role in the affecting greenhouse climate, as defined by temperature, humidity and CO2 concentration, particularly in Mediterranean countries, little information and data are presently available on full-scale greenhouse ventilation mechanisms. In this paper, we present a new method for selecting the parameters based on a particle swarm optimization (PSO) algorithm which optimize the choice of parameters by minimizing a cost function. The simulator was based on a published model with some minor modifications as we were interested in the parameter of ventilation. The function is defined by a reduced model that could be used to simulate and predict the greenhouse environment, as well as the tuning methods to compute their parameters. This study focuses on the dynamic behavior of the inside air temperature and humidity during ventilation. Our approach is validated by comparison with some experimental results. Various experimental techniques were used to make full-scale measurements of the air exchange rate in a 400 m2 plastic greenhouse. The model which we propose based on natural ventilation parameters optimized by a particle swarm optimization was compared with the measurements results.

 

Keywords: Optimization, Particle swarm optimization, Greenhouses, Temperature, Humidity, Hydric model, Climate models, Cooling fog system, Metaheuristics

 

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