Dynamic modeling of the environment in a naturally ventilated, fog-cooled greenhouse

Abdel-Ghany, Ahmed M.; Kozai, Toyoki

doi:10.1016/j.renene.2005.07.013

Cited by 70 publications

(24 citation statements)

References 26 publications

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“…Thus it is very important to get as accurate data as possible, i. e. to provide as accurate input weather data as possible, in order to achieve the most accurate modelling [7,8]. Thus it is very important to get as accurate data as possible, i. e. to provide as accurate input weather data as possible, in order to achieve the most accurate modelling [7,8].…”

Section: Modelling Energy Requirements Of the Facilitymentioning

confidence: 99%

Dynamic modeling of а heating system using geothermal energy and storage tank

Milanovic¹,

Ećim

Jelić

et al. 2012

THERM SCI

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This paper analyzes a greenhouse heating system using geothermal energy and storage tank and the possibility of utilization of insufficient amount of heat from geothermal sources during the periods with low outside air temperatures. Crucial for these analyses is modelling of the necessary yearly energy requirements for greenhouse heating. The results of these analyses enable calculation of an appropriate storage tank capacity so that the energy efficiency of greenhouse heating system with geothermal energy could be significantly improved.

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Section: Modelling Energy Requirements Of the Facilitymentioning

confidence: 99%

Dynamic modeling of а heating system using geothermal energy and storage tank

Milanovic¹,

Ećim

Jelić

et al. 2012

THERM SCI

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“…Many researchers have studied control strategies for ventilation and heating systems, for humidification/dehumidification systems, and for the regulation of other agricultural greenhouse parameters. References [14,15] proposed a ventilation system based on an on-off control, Reference [16] studied a natural ventilation system for an agricultural greenhouse, Reference [17] presented an evaluation of the use of various renewable energy sources to heat a greenhouse, and Reference [18] introduced different fields of application for renewable energy in buildings, in particular in the agricultural sector. Reference [19] proposed energy reduction measured through low-cost and solar energy-based sensors.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Control of the Microclimate of an Agricultural Greenhouse Powered by a Supporting PV System

et al. 2020

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An agricultural greenhouse is a complex and Multi-Input Multi-Output MIMO system in which the internal parameters create a favorable microclimate for agricultural production. Temperature and internal humidity are two parameters that have a major impact on greenhouse yield. The objective of this study was to propose a simulated dynamic model in a MATLAB/Simulink environment for experimental validation. Moreover, a fuzzy controller was designed to manage a greenhouse indoor climate by means of an asynchronous motor for ventilation, heating, humidification, etc. An intelligent system to control these actuators for an optimal inside climate was implemented in the model. The dynamic model was validated by comparing the simulation results to experimental measurements. These results showed the effectiveness of the control strategy in regulating the greenhouse indoor climate. Finally, a photovoltaic generator was modeled, with the aim of reducing the costs of agricultural production. It feeds the asynchronous motor with a vector control optimized by fuzzy logic that drives a variable speed fan.

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“…Due to the complex interactions carried out among the variables, such as temperature, humidity, radiation, carbon dioxide, water, nutrients, pests, diseases, and weeds, among others, it is necessary to identify and analyze them in subsystems. Several authors [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] have characterized the subsystems from different perspectives-for example, climate, nutrition, and irrigation. Models are used to achieve detailed knowledge of all interactions and processes.…”

Section: Introductionmentioning

confidence: 99%

“…Although the type of crop is not defined explicitly, its influence is implicit, given its inherent condition in the models. These studies have implemented different approaches to develop these models, such as neural networks, genetic algorithms, and neuro-fuzzy models [12][13][14][15][16], where the object of study centers on natural ventilation, forced ventilation, cooling evaporators, or systems that integrate calefaction [17][18][19][20], to mention a few of them. On the other hand, there are alternative studies not developed for greenhouse environments, and proposed by some authors to model and simulate temperature and humidity [21][22][23][24][25].…”

mentioning

confidence: 99%

Modeling and Simulation of Temperature and Relative Humidity Inside a Growth Chamber

et al. 2019

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Modeling and simulation of internal variables such as temperature and relative humidity are relevant for designing future climate control systems. In this paper, a mathematical model is proposed to predict the internal variables temperature and relative humidity (RH) of a growth chamber (GCH). Both variables are incorporated in a set of first-order differential equations, considering an energy-mass balance. The results of the model are compared and assessed in terms of the coefficients of determination (R 2 ) and the root mean squared error (RMSE). The R 2 and RMSE computed were R 2 = 0.96, R 2 = 0.94, RMSE = 0.98 • C, and RMSE = 1.08 • C, respectively, for the temperature during two consecutive weeks; and R 2 = 0.83, R 2 = 0.81, RMSE = 5.45%RH, and RMSE = 5.48%RH, respectively, for the relative humidity during the same period. Thanks to the passive systems used to control internal conditions, the growth chamber gives average differences between inside and outside of +0.34 • C for temperature, and +15.7%RH for humidity without any climate control system. Operating, the GCH proposed in this paper produces 3.5 kg of wet hydroponic green forage (HGF) for each kilogram of seed (corn or barley) harvested on average. Energies 2019, 12, 4056 2 of 22techniques are analyzed to maintain the following critical variables: Temperature, humidity, lighting, and carbon dioxide concentration [1,2]. Other research projects predict the behavior of one or more variables and provide an appropriate response to keep these variables within the desired limits [3][4][5]. Most of the research on modeling and simulations has mainly been developed for greenhouses [6][7][8][9][10][11] to predict the internal environmental conditions of enclosures utilized for cultivation and plant growth. Several studies from the perspective of different environments of models have also been pursued, with the objective of knowing the behavior of the internal variables, such as temperature and relative humidity, that will serve as support to later define an adequate strategy for controlling the growth conditions within an enclosure. Although the type of crop is not defined explicitly, its influence is implicit, given its inherent condition in the models. These studies have implemented different approaches to develop these models, such as neural networks, genetic algorithms, and neuro-fuzzy models [12][13][14][15][16], where the object of study centers on natural ventilation, forced ventilation, cooling evaporators, or systems that integrate calefaction [17][18][19][20], to mention a few of them. On the other hand, there are alternative studies not developed for greenhouse environments, and proposed by some authors to model and simulate temperature and humidity [21][22][23][24][25].

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Dynamic modeling of the environment in a naturally ventilated, fog-cooled greenhouse

Cited by 70 publications

References 26 publications

Dynamic modeling of а heating system using geothermal energy and storage tank

Dynamic modeling of а heating system using geothermal energy and storage tank

Intelligent Control of the Microclimate of an Agricultural Greenhouse Powered by a Supporting PV System

Modeling and Simulation of Temperature and Relative Humidity Inside a Growth Chamber

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