Comparison of Modeling and Simulation Results Management Microclimate of the Greenhouse by Fuzzy Logic Between a Wetland and Arid Region

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Didi Faouzi, N. Bibi-Triki, B. Draoui & A. Abène (2016). Comparison of Modeling and Simulation Results Management Microclimate of the Greenhouse by Fuzzy Logic Between a Wetland and Arid Region. Mechanics, Materials Science & Engineering Vol.6, pp. 205-217, doi: 10.13140/RG.2.2.28996.42880

Authors: Didi Faouzi, N. Bibi-Triki, B. Draoui, A. Abène

ABSTRACT. Currently the climate computer offers many benefits and solves problems related to the regulation, monitoring and controls. Greenhouse growers remain vigilant and attentive, facing this technological development. They ensure competitiveness and optimize their investments / production cost which continues to grow.

The application of artificial intelligence in the industry known for considerable growth, which is not the case in the field of agricultural greenhouses, where enforcement remains timid. It is from this fact, we undertake research work in this area and conduct a simulation based on meteorological data through MATLAB Simulink to finally analyze the thermal behavior -greenhouse microclimate energy.

In this paper, we present comparison of modelling and simulation management of the greenhouse microclimate by fuzzy logic between a wetland (Dar El Beida Algeria) and the other arid (Biskra Algeria).

Keywords: modeling, fuzzy logic controller, optimization, simulation, greenhouse, microclimate

DOI 10.13140/RG.2.2.28996.42880


[1] Bendimerad, S., T. Mahdjoub, N. Bibi-Triki, M.Z. Bessenouci and B. Draoui et al., 2014. Simulation and Interpretation of the BIBI Ratio CB (.), as a Function of Thermal Parameters of the Low Inertia Polyethylene Wall of Greenhouses. Physics Procedia, 55: 157-164. DOI: 10.1016/j.phpro.2014.07.023

[2] Bibi-Triki, N., S. Bendimemerad, A. Chermitti, T. Mahdjoub and B. Draoui et al., 2011. Modeling, characterization and analysis of the dynamic behavior of heat transfers through polyethylene and glass walls of greenhouses. Physics Procedia, 21: 67-74. DOI: 10.1016/j.phpro.2011.10.011

[3] Faouzi Didi , N. Bibi Triki and A. Chermitti, 2016. Optimizing the greenhouse micro-climate management by the introduction of artificial intelligence using fuzzy logic. Int. J. Computer Eng. Technology, 7: 78-92 , Volume 7, Issue 3, May-June 2016, pp. 78–92, Article ID: IJCET_07_03_007.

[4] El Aoud, M.M. and M. Maher, 2014. Intelligent control for a greenhouse climate. Int. J. Advances Eng. Technology, 7: 1191-1205.

[5] Abdelhafid Hasni, B., T. Draoui, Boulard, R. Taibi and A. Hezzab, 2008. Evolutionary algorithms in the optimization of greenhouse climate model parameters. Int. Rev. Comput. Software.

[6] Draoui, B., F. Bounaama, T. Boulard and N. Bibi-Triki, 2013. In-situ modelisation of a greenhouse climate including sensible heat, water vapour and CO2 balances. EPS Web Conferences, 45: 01023-01023. DOI: 10.1051/epjconf/20134501023

[7] Hasni, A., B. Draoui, T. Boulard, R. Taibi and B. Dennai, 2009. A particle swarm optimization of natural ventilation parameters in a greenhouse with continuous roof vents. Sensor Transducers J., 102: 84-93.

[8] Bouaama, F., K. Lammari and B. Draoui, 2008. Greenhouse air temperature control using fuzzy PID+I and Neuron fuzzy hybrid system controller. Proceedings of the International Review of Automatic Control (IRE.A.CO).

[9] Dhamakale, S.D. and S.B. Patil, 2011. Fuzzy logic approach with microcontroller for climate controlling in green house. Int. J. Emerg. Technol., 2: 17-19.

[10] Gurbaoui, M., A. Ed-Dahhak, Y. Elafou, A. Lachhab and L. Belkoura et al., 2013. Implementation of direct fuzzy controller in greenhouse based on labview. Int. J. Electr. Electron. Eng. Stud., 1: 1-13.

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