Cooling of a polyethylene tunnel type greenhouse by means of a rock bed

Kürklü, Ahmet; Bilgin, Sefai

doi:10.1016/j.renene.2004.03.005

Cited by 13 publications

(8 citation statements)

References 13 publications

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“…The advent of glass and plastic greenhouses has significantly advanced agricultural productivity by offering controlled environments that protect plants from extreme weather while managing essential conditions like air temperature, humidity, and CO2 levels [1][2][3][4][5]. These innovations are vital in areas with sub-optimal climate conditions, extending cultivation periods beyond traditional growing seasons.…”

Section: Introductionmentioning

confidence: 99%

“…Enhancing energy efficiency in greenhouses has centered on integrating renewable energy sources such as solar and geothermal systems for heating [7][8][9][10]. Similarly, innovative cooling strategies, such as the utilization of rock beds, have demonstrated the potential to maintain lower temperatures within polyethylene tunnel-type greenhouses, providing a sustainable alternative to conventional cooling methods [4]. These developments highlight the ongoing need for advanced systems capable of dynamic environmental control.…”

Section: Introductionmentioning

confidence: 99%

“…Our methodology builds on previous work by incorporating detailed simulations that consider the intricate energy dynamics within greenhouses, such as solar radiation and energy transfer through construction materials [32][33][34][35]. By integrating insights from established studies on passive and active greenhouse heating in southern Europe, we aim to tailor our model to real-world applications, ensuring its relevance across different climatic conditions [4]. In summary, this research leverages cutting-edge machine learning techniques to tackle the complex challenges of greenhouse climate management.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Greenhouse Microclimatic Parameters Using Building Transient Simulation and Artificial Neural Networks

Ećim-Đurić,

Milanović,

Dimitrijević-Petrović

et al. 2024

Agronomy

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In the realm of agricultural advancement, the relentless quest for agricultural efficiency amidst the vagaries of climate change has positioned greenhouse technology as a linchpin for secure and sustainable food production. The precise management of greenhouse microclimatic conditions i.e., the ability to accurately predict and maintain ideal temperature and relative humidity, is crucial for enhancing plant growth and health, optimizing resource use, and ensuring sustainable agricultural practices. However, maintaining optimal microclimatic conditions is a significant challenge due to the dynamic nature of external environmental influences. This study aims to address the critical need for advanced predictive tools that can enhance the control and management of greenhouse microclimates, thereby supporting sustainable agricultural practices and food security. Our research introduces a novel integration of building transient simulation (TRNSYS) and artificial neural networks (ANNs) to predict temperature and relative humidity inside a greenhouse across the calendar year, based on external atmospheric conditions. The TRNSYS model meticulously simulates the greenhouse’s thermal load, incorporating real-world data to ensure a high level of accuracy in describing the facility’s dynamic behavior. Our ANN model, composed of three layers, underwent optimization to identify the ideal number of neurons, learning rates, and epochs, settling on a model configuration that minimized prediction errors. The evaluation metrics, including root mean square error (RMSE) and mean absolute error (MAE), demonstrated the model’s effectiveness, with an RMSE of 0.3166 °C for temperature and 5.9% for relative humidity, and MAE values of 0.1002° and 3.4%, respectively. These findings underscore the model’s potential as a powerful tool for greenhouse climate control, offering substantial benefits in terms of energy efficiency, resource optimization, and overall sustainability in agriculture. By leveraging detailed dynamic simulations and advanced neural network algorithms, this study contributes significantly to the field of precision agriculture, presenting a novel approach to managing greenhouse environments in the face of changing climatic conditions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of Greenhouse Microclimatic Parameters Using Building Transient Simulation and Artificial Neural Networks

Ećim-Đurić,

Milanović,

Dimitrijević-Petrović

et al. 2024

Agronomy

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“…Thus, when designing a greenhouse, careful selection of appropriate material can positively affect the energy requirements and fuel consumption necessary to maintain optimal growth temperatures (Ponce et al, 2014;Marshall, 2016;Rasheed et al, 2018a) The effort to support a growing energy demand, while simultaneously reducing fossil fuel impacts on the environment, has motivated the scientific community to invest significant time, money, and resources into extensively investigating the application of renewable energy (Adaramola, 2012). As part of that effort, numerous studies have focused on alternative ways to achieve greenhouse heating (Pucar, 2002;Kürklü and Bilgin, 2004;Ghosal and Tiwari, 2006;Abdel-Ghany and Al-Helal, 2011;Joudi and Farhan, 2014). The aim of these studies was minimize greenhouse energy intake by using alternative approaches, such as: increasing shortwave radiation availability inside the greenhouse, reducing energy loss to the outside environment, and increasing the greenhouse's constituent storage ability.…”

Section: Introductionmentioning

confidence: 99%

Determination of Thermal Radiation Emissivity and Absorptivity of Thermal Screens for Greenhouse

Rafiq

Rasheed

et al. 2019

phpf

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Greenhouse farmers often use thermal screens to reduce greenhouse heating expenses during the winter, and for shade during hot, sunny days in the summer, as it is an inexpensive solution to temperature control relative to other available options. However, accurate measurements of their emitted and absorbed radiations are important for the selection of suitable screens that offer maximum performance. Material's ability to save energy is highly dependent on these properties. Limited studies have investigated the measurement of these properties under natural conditions, but they are only applicable to materials having partial porosities. In this work, we describe a new radiation balance method for determining emissive power and absorptive capacity, as well as reflectivity, transmissivity and emissivity of materials having complete and partial transparency by using pyrgeometer and net radiometer. In this study, four materials with zero porosity, were tested. The emissivity value of PE, LD-13, LD-15 and PH-20 was 0.439±0.020, 0.460±0.010, 0.454±0.004, and 0.499±0.006, respectively. All tested samples showed high emitted radiation as compared to absorbed radiation.

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“…However, some research has been done. For instance, Kürklü and Bilgin (2004) conducted an experiment cooling a 15 m 2 greenhouse by recirculating air from the greenhouse through two insulated rock-bed canals of 3×1.25×0.75 m buried in the soil underneath the greenhouse. At night the rock-bed canals were cooled down by circulating fresh outside air through.…”

Section: Cooling Applicationsmentioning

confidence: 99%

Assessing the Impact of Rock-Bed Storage for Tempering Air-to-Air Heat Pumps

Dmytrenko¹

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Over the past decade, improvements have been made in air-source heat pump units; however, performance during periods of extreme high and low temperatures remains poor. In order to increase the COP and thermal capacity of the heat pump during these extreme periods, the unit was coupled to a short-term underground rock-bed thermal store which enables tempering ambient air that is drawn to the outdoor heat exchanger. The system was designed, built, and commissioned at the

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Cooling of a polyethylene tunnel type greenhouse by means of a rock bed

Cited by 13 publications

References 13 publications

Prediction of Greenhouse Microclimatic Parameters Using Building Transient Simulation and Artificial Neural Networks

Prediction of Greenhouse Microclimatic Parameters Using Building Transient Simulation and Artificial Neural Networks

Determination of Thermal Radiation Emissivity and Absorptivity of Thermal Screens for Greenhouse

Assessing the Impact of Rock-Bed Storage for Tempering Air-to-Air Heat Pumps

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