Inside Greenhouse Temperature Research Articles

Low temperature heating system for greenhouses based on enclosed water curtain and liquid foam insulation

A greenhouse water curtain heating system allows heating of the greenhouse by low-temperature water, which can be obtained from residual waste heat sources. The water curtain can be applied on the outside of the greenhouse roof or enclosed between two foils. But also enclosed water curtains suffer from high heat losses, which limits the integration of low temperature waste heat sources. An effective way of reducing the heat losses is to combine the water curtain system with retractable liquid foam enclosed between two foils. But until now, a systematic evaluation of the thermal performance of such system combination is still lacking. Thus, this study aims to fill in this research gap by evaluating the heat transfer characteristics of a double-foil greenhouse roof section installed with a combined water curtain and liquid foam system. Experimental tests have been conducted under a wide range of temperature scenarios in a climate chamber where the heat loss and heat gains from the water curtain is measured. The results have been compared with the data in the existing studies. This study revealed that combining the water curtain system with liquid foam, reduces the heat losses by half compared to using just the water curtain: the heat loss coefficient was reduced from 4.4 W·m−2·K−1 down to 2.0 W·m−2·K−1. The heat losses through the roof using the combined system are also lower than the heat losses from a double foil greenhouse with other heating systems. An average water curtain temperature of 5.1 °C above the inside greenhouse temperature can compensate for the heat losses through the roof at an outdoor temperature of −19 °C. Based on the study results, recommendations for market implantation of this technology are provided. This study confirms energetic benefit of combining water curtains and the liquid foam technology.

Empirical modelling of temperature in fogging greenhouse

The objective of this article is to evaluate the inside temperature of greenhouse and efficiency of fogging system under the influence of solar power. A 50% off sun shading roof was selected to test in this study. Testing is divided to 2 cases. Case I Measuring all of parameters without operate the fogging system and ventilation between 9.00 a.m. to 5.00 p.m. (Thailand’s time zone). Case II Measuring all of parameters with a fogging system that was controlled the relative humidity below 80% all day. The results show that the highest temperature in greenhouse is 50.13oC (no fogging and ventilation). the developed empirical model has an error 6.33% between numerical results and measured air temperature. In case of neglected solar power, the model showed that the fogging system can reduce the temperature in greenhouse in range of 7.05°C (18.44%) and the efficiency of fogging system is quite high (value is 57.36%). The important factor that influents on the air temperature in greenhouse is solar power. If we need to control the atmosphere in greenhouse by fogging system than the reduction of solar power is the most important controlling factor.

Evaluation and estimation of the inside greenhouse temperature, numerical study with thermal and optical aspect

To evaluate the microclimate inside an agricultural greenhouse, the Ansys Fluent R16.0 Computational Fluid Dynamic (CFD) code was used for a 2D simulation of transport phenomena like heat transfer and radiation for three Rayleigh number values using the finite volumes method. The daily variation of external solar radiation was introduced via User Defined Functions (UDF) to modulate solar radiation using the Discrete Ordinates (DO) model. In the current study, the flow was simulated in the steady case and the plants were not considered. During simulation, both cases were tested day and night. The obtained results corroborate with the literature and its confrontation was satisfactory. This work should make it possible to improve the thermal performances of greenhouses by a better positioning of heating and ventilation systems.

Development and Optimization of a Building Energy Simulation Model to Study the Effect of Greenhouse Design Parameters

Energy management of the greenhouse is considered to be one of the most important challenges of greenhouse farming. Energy saving measures need considered, besides applying energy supplying techniques. To address this issue, a model was developed to simulate the thermal environment of a greenhouse using a Transient Systems Simulation Program (TRNSYS 17) as a building energy simulation (BES) platform. The model was calibrated by modifying the input parameters to minimize the uncertainties obtained from the results. Nash-Sutcliffe efficiency coefficients of 0.958 and 0.983 showed good agreement between the computed and experimental results. The proposed model was used to evaluate the effects of greenhouse design parameters, including roof shape, orientation, double-glazing, natural ventilation, coverings and their thickness, on its energy conservation capacity. It was found that the most suitable design for a greenhouse located in Daegu (latitude 35.53° N, longitude 128.36° E) South Korea would be east-west (E-W) oriented, with a gothic-shaped roof and double-glazing of PMMA (Polymethylmethacrylate) covering. Natural ventilation reduced the inside temperature of greenhouse, thereby reducing the energy demand of cooling. The model developed can help greenhouse farmers and researchers make pre-design decisions regarding greenhouse construction, taking their local environment and specific needs into consideration.

Particle Swarm Optimization of BP-ANN Based Soft Sensor for Greenhouse Climate

In this article, the authors develop the Particle Swarm Optimization algorithm (PSO) in order to optimise the BP network in order to elaborate an accurate dynamic model that can describe the behavior of the temperature and the relative humidity under an experimental greenhouse system. The PSO algorithm is applied to the Back-Propagation Neural Network (BP-NN) in the training phase to search optimal weights baded on neural networks. This approach consists of minimising the reel function which is the mean squared difference between the real measured values of the outputs of the model and the values estimated by the elaborated neural network model. In order to select the model which possess higher generalization ability, various models of different complexity are examined by the test-error procedure. The best performance is produced by the usage of one hidden layer with fourteen nodes. A comparison of measured and simulated data regarding the generalization ability of the trained BP-NN model for both temperature and relative humidity under greenhouse have been performed and showed that the elaborated model was able to identify the inside greenhouse temperature and humidity with a good accurately.

Geothermal source heat pump performance for a greenhouse heating system: an experimental study

Greenhouses play a significant function in the modern agriculture economy even if require great amount of energy for heating systems. An interesting solution to alleviate the energy costs and environmental problems may be represented by the use of geothermal energy. The aim of this paper, based on measured experimental data, such as the inside greenhouse temperature and the heat pump performance (input and output temperatures of the working fluid, electric consumption), was the evaluation of the suitability of low enthalpy geothermal heat sources for agricultural needs such as greenhouses heating. The study was carried out at the experimental farm of the University of Bari, where a greenhouse was arranged with a heating system connected to a ground-source heat pump (GSHP), which had to cover the thermal energy request. The experimental results of this survey highlight the capability of the geothermal heat source to ensue thermal conditions suitable for cultivation in greenhouses even if the compressor inside the heat pump have operated continuously in a fluctuating state without ever reaching the steady condition. Probably, to increase the performance of the heat pump and then its coefficient of performance within GSHP systems for heating greenhouses, it is important to analyse and maximise the power conductivity of the greenhouse heating system, before to design an expensive borehole ground exchanger. Nevertheless, according to the experimental data obtained, the GSHP systems are effective, efficient and environmental friendly and may be useful to supply the heating energy demand of greenhouses.

Modeling of greenhouse with PCM energy storage

Greenhouses provide a controlled environment that is suitable for plants growth and cultivation. In this paper the maximum temperature change inside the greenhouse is to be reduced by the use of energy storage in a phase change material PCM. A mathematical model is developed for the storage material and for the greenhouse. The coupled models are solved using numerical methods and Java code program. The effect of different parameters on the inside greenhouse temperature is investigated. The temperature swing between maximum and minimum values during 24 h can be reduced by 3–5 °C using the PCM storage. This can be improved further by enhancing the heat transfer between the PCM storage and the air inside the greenhouse.

ROBUST CONTROL OF GREENHOUSE CLIMATE EXPLOITING MEASURABLE DISTURBANCES

This paper presents the development and implementation of robust control techniques based on the quantitative feedback theory (QFT) aimed at achieving adequate values of inside greenhouse temperature in spite of uncertainties and disturbances acting on the system. A modification of classical design approaches has been included to incorporate feedforward action (exploiting the availability of measurements of disturbances, which in the particular case of greenhouses are the main energy source) and an antiwindup action to account for frequent saturations in the control signal. Results obtained with this scheme using a validated nonlinear simulator of greenhouse dynamics are also included.