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An intelligent system for the climate control and energy savings in agricultural greenhouses

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Abstract

A greenhouse for crop production is a complex thermodynamic system where the indoor temperature and the humidity conditions have a great impact on the crop yields. This system can be considered a multivariable input output system MIMO. This paper aims at presenting a physical model of a greenhouse, experimentally validated, in order to propose a fuzzy-based controller to manage the indoor climate of a greenhouse using some actuators (induction motors, heating system etc.…) for ventilation, heating, humidifying, and dehumidifying purposes. In addition, a novel approach is presented for energy management by involving the photovoltaic energy in order to minimize the use of conventional electrical grid and to lower costs of agriculture production. The photovoltaic (PV) generator will serve to power a direct torque control (DTC) controlled induction motor which drive a variable speed fan. The validation of the physical model shows a high agreement with the experimental measurement. The simulation results show the effectiveness of the fuzzy controller as well as the PV generator for saving the energy and lowering the costs of crop production into greenhouses.

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References

  • Abdel-Ghany, A. M., & Al-Helal, I. M. (2011). Solar energy utilization by a greenhouse: general relations. Renewable Energy, 36, 189–196.

    Article  Google Scholar 

  • Bennis, N., Duplaix, J., Enéa, G., Haloua, M., & Youlal, H. (2008). Greenhouse climate modelling and robust control. Computers and Electronics in Agriculture, 61, 96–107.

    Article  Google Scholar 

  • Bernard, D., Coleman, C., & Victor, J. (1963). Thermodynamics and departures from Fourier’s law of heat conduction. Archive for Rational Mechanics and Analysis, 13, 245–261.

    Article  MathSciNet  MATH  Google Scholar 

  • Boulard, T., & Baille, A. (1993). A simple greenhouse climate control model incorporating effects of ventilation and evaporative cooling. Agricultural and Forest Meteorology, 65, 145–157.

    Article  Google Scholar 

  • Chen, J. (2010). Estimation of motor startup speed profile using low-resolution timing signals and motor speed-torque curve. In 20 th International Compressor Engineering Conference, Purdue, Paper 1808.

  • Collotta, M., Messineo, A., Nicolosi, G., & Pau, G. (2014). A dynamic fuzzy controller to meet thermal comfort by using neural network forecasted parameters as the input. Energies, 7, 4727–4756.

    Article  Google Scholar 

  • Dayan, J., Dayan, E., Strassberg, Y., & Presnov, E. (2004). Simulation and control of ventilation rates in greenhouses. Mathematics and Computers in Simulation, 65, 3–17.

    Article  MathSciNet  MATH  Google Scholar 

  • Dhamakale, S. D., & Patil, S. B. (2011). Fuzzy logic approach with microcontroller for climate controlling in green house. International Journal of Emerging Technologies in Learning, 2, 17–19.

    Google Scholar 

  • Echaieb, K., Marouani, R., & Mami, A. (2012). Nonlinear multivariable design control strategy of solar water pumping system. European Journal of Scientific Research, 72, 539–548.

    Google Scholar 

  • Echaieb, K., Azaza, M., & Mami, A. (2013). A new control strategy of indoor air temperature in a photovoltaic greenhouse. International Journal of Soft Computing and Software Engineering, 3, 848–852.

    Google Scholar 

  • Esen, M., & Yuksel, T. (2013). Experimental evaluation of using various renewable energy sources for heating a greenhouse. Energy and Buildings, 6, 340–351.

    Article  Google Scholar 

  • Fabrizio, E. (2012). Energy reduction measures in agricultural greenhouses heating: envelope, systems and solar energy collection. Energy and Buildings, 53, 57–63.

    Article  Google Scholar 

  • Ghosal, M. K., Tiwari, G. N., & Srivastava, N. S. L. (2003). Modeling and experimental validation of a greenhouse with evaporative cooling by moving water film over external shade cloth. Energy and Buildings, 35, 843–850.

    Article  Google Scholar 

  • Gurban, E. H., & Andreescu, G. D. (2012). Comparison study of PID controller tuning for greenhouse climate with feedback feed-forward linearization and decoupling (pp. 1–6). Sinaia: 16th International Conference on System Theory, Control and Computing (ICSTCC).

    Google Scholar 

  • Hatirli, S. A., Ozkan, B., & Fert, C. (2006). Energy inputs and crop yield relationship in greenhouse tomato production. Renewable Energy, 31, 427–438.

    Article  Google Scholar 

  • Heidari, M. D., & Omid, M. (2011). Energy use patterns and econometric models of major greenhouse vegetable productions in Iran. Energy, 36, 220–225.

    Article  Google Scholar 

  • Iliev, L., Zakeri, A., Sazdov, P., & Baytelieva, A. M. (2013). A fuzzy logic based approach for integrated control of protected cultivation. World Applied Sciences Journal, 24, 561–569.

    Google Scholar 

  • Kittas, C., & Bartzanas, T. (2007). Greenhouse microclimate and dehumidification effectiveness under different ventilator configurations. Building and Environment, 42, 3774–3784.

    Article  Google Scholar 

  • Kittas, C., Karamanis, M., & Katsoulas, N. (2005). Air temperature regime in a forced ventilated greenhouse with rose crop. Energy and Buildings, 37, 807–812.

    Article  Google Scholar 

  • Kolokotsa, D., Saridakis, G., Dalamagkidis, K., Dolianitis, S., & Kaliakatsos, I. (2010). Development of an intelligent indoor environment and energy management system for greenhouses. Energy Conversion and Management, 51, 155–168.

    Article  Google Scholar 

  • Lascu, C., Boldea, I., & Blaabjerg, F. (2002). A modified direct torque control for induction motor sensorless drive. IEEE Transansactions on Industry Applications, 36, 122–130.

    Article  Google Scholar 

  • Liu, J., Ren, G., Tian, G., & Qi, Y. (2014). The temperature/humidity control system of equipment warehouse based on fuzzy control algorithm. Mechatronics and Automatic Control Systems Lecture Notes in Electrical Engineering, 237, 127–134.

    Article  Google Scholar 

  • Marvuglia, A., et al. (2014). Coupling a neural network temperature predictor and a fuzzy logic controller to improve thermal comfort regulation in an office building. Building and Environment, 72, 287–299.

    Article  Google Scholar 

  • Michele, S., Faramarzi, S. Z., Saidur, R., & Salam, Z. (2013). The application of solar technologies for sustainable development of agricultural sector. Renewable and Sustainable Energy Reviews, 18, 583–594.

    Article  Google Scholar 

  • Miranda, R. C., Ramos, E. V., Peniche-Vera, R., & Herrera-Ruiz, G. (2006). Fuzzy greenhouse climate control system based on a field programmable gate array. Biosystems Engineering, 94, 165–177.

    Article  Google Scholar 

  • Mohammadi, A., & Omid, M. (2010). Economical analysis and relation between energy inputs and yield of greenhouse cucumber production in Iran. Applied Energy, 87, 191–196.

    Article  Google Scholar 

  • Monmasson, E., Naassani, A., Louis, A., & Paul, J. (2001). Extension of the DTC concept. IEEE Transactions on Industrial Electronics, 48, 715–717.

    Article  Google Scholar 

  • Mortensen, L. M. (1982). Growth responses of some greenhouse plants to environment. III. Design and function of a growth chamber prototype. Scientia Horticulturae, 16, 57–63.

    Article  Google Scholar 

  • Pasgianos, G. D., Arvanitis, K. G., Polycarpou, P., & Sigrimis, N. (2003). A nonlinear feedback technique for greenhouse environmental control. Computers and Electronics in Agriculture, 40, 153–177.

    Article  Google Scholar 

  • Safari, A., & Mekhilef, S. (2011). Simulation and hardware implementation of incremental conductance mppt with direct control method using cuk converter. IEEE Transactions on Industrial Electronics, 58, 1154–1161.

    Article  Google Scholar 

  • Singh, G., Singh, P., Lubana, P., & Singh, K. G. (2006). Formulation and validation of a mathematical model of the microclimate of a greenhouse. Renewable Energy, 31, 1541–1560.

    Article  Google Scholar 

  • Song, Y., Huang, X., & Feng, Y. (2013). A kind of temperature and humidity adaptive predictive decoupling method in wireless greenhouse environmental test simulation system. Advance Journal of Food Science and Technology, 5, 1395–1403.

    Google Scholar 

  • Stanghellini, C., & De Jong, T. (1995). A model of humidity and its applications in a greenhouse. Agricultural and Forest Meteorology, 76, 129–148.

    Article  Google Scholar 

  • Stephenson, D. G., & Mitalas, G. P. (1971). Calculation of heat conduction transfer functions for multi-layer slabs. ASHRAE Transactions, 77, 117–126.

    Google Scholar 

  • Teitel, M., Atias, M., & Barak, M. (2010). Gradients of temperature, humidity and CO2 along a fan-ventilated greenhouse. Biosystems Engineering, 106, 166–174.

    Article  Google Scholar 

  • Udink ten Cate A.J. (1983) Modeling and (adaptive) control of greenhouse climates.Ph.D. thesis. Agricultural University of Wageningen.

  • Villarreal-Guerrero, F., Kacira, M., & Fitz-Rodrıguez, E. (2012). Simulated performance of a greenhouse cooling control strategy with natural ventilation and fog cooling. Biosystems Engineering, 111, 217–228.

    Article  Google Scholar 

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Correspondence to Azaza Maher.

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I testify on behalf of all co-authors that our article submitted to Energy Efficiency: Title: An intelligent system for the climate control and energy savings in agricultural greenhouses.

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Maher, A., Kamel, E., Enrico, F. et al. An intelligent system for the climate control and energy savings in agricultural greenhouses. Energy Efficiency 9, 1241–1255 (2016). https://doi.org/10.1007/s12053-015-9421-8

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  • DOI: https://doi.org/10.1007/s12053-015-9421-8

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