In-situ Modelisation of a Greenhouse Climate Including Sensible Heat, Water Vapour and CO₂Balances

Abstract

Abstract.While thermal structures such as greenhouses are complex to model under transient conditions because of coupled heat and mass (H 2 O and CO 2 ) exchanges between their different parts, systems theory allows a simple analysis of the input/output behaviour of the model. In particular, parameters of the reduced dynamic models can be derived from in-situ measurements on these systems. This paper describes an application of the systems approach to a greenhouse climate model which incorporates CO 2 balance, crop transpiration and photosynthesis and the effects of heating, CO 2 enrichment, natural ventilation and evaporative cooling. In a first stage, system parameters such as the equivalent thermal mass, the wind dependent coefficient of natural ventilation and the various heat exchange coefficients were identified in situ from sequences of input-output data concerning the heat and water vapour balance, recorded over the crop production period from December to July. Good agreement between measured and computed values of air temperature, air humidity was observed. In a second stage, we have considered simultaneously the sensible heat, water vapour and CO 2 exchanges and tested the model against experimental data measured using a commercial greenhouse climate control computer. The identified values of the physical and physiological parameters are close the theoretical values and the accuracy of temperature, humidity and CO 2 content is rather good.

Comparative analysis of the management of the results of the modeling and the simulation of the evaluation of the thermal energy of the greenhouse by a fuzzy logic controller between a wet region and an arid region

“…We are interested in this product conditioning of agricultural greenhouse while characterizing the dynamic operation of the complex system that is the greenhouse with its various compartments (S. Bendimerad and T. Mahdjoub, 2014;B. Draoui and F. Bounaama, 2013), develop models to reproduce the essential properties, mechanisms and interactions different compartments and to approach the analysis of thermo-fluid behavior of agricultural greenhouse.…”

Modeling a Fuzzy Logic Controller to Simulate and Optimize the Greenhouse Microclimate Management using MATLAB SIMULINK

“…[8] It is defined by its structural and functional architecture, the optical quality, thermal and mechanical coverage and the accompanying technical means. it is considered as a very confined environment where many components are exchanged between them, and in which the main factor involved in this medium is light, temperature and relative humidity [7][8][9]. to manage the greenhouse microclimate, greenhouse growers often use methods such as passive static ventilation (opening), shade screens, evaporative cooling etc ... and occasionally the active type.…”

Comparison of Modeling and Simulation results Management Micro Climate of the Greenhouse by Fuzzy Logic between a Wetland and Arid region

“…Agricultural greenhouse originally designed as a simple enclosure limited by a transparent wall, as is the case for conventional tunnel greenhouses and largely answered chapel in several countries including those of the Mediterranean basin [21]. They amplify certain characteristics of the surrounding environment, thus involving variations of internal energy and fairly significant heat loss due to the low inertia of the clamp system [7][8][9][10]. The first objective is to improve the thermal capacity of the greenhouse (greenhouse) [17][18].…”

“…They must be both specific enough to obey the dynamic and real behavior of the greenhouse system, and fairly small to be easily adaptable to the phases of the simulation. Good modulation instructions depending on the requirements of the plants to grow under shelter and outdoor climatic conditions [8][9], result in a more rational and efficient use of inputs and equip the best production performance. The greenhouse climate is modified by artificial actuators, thus providing the best conditions in the immediate environment of energy costs and it requires a controller, which minimizes the power consumption while keeping the state variables as close as possible optimal harvest [16].…”