Development and evaluation of combustion-type CO2 enrichment system connected to heat pump for greenhouses

Yang, Seung-Hwan; Lee, Chun Gu; Ashtiani-Araghi, Alireza; Kim, Joon Yong; Rhee, Je-Keun

doi:10.1016/j.eaef.2013.12.005

Cited by 10 publications

(2 citation statements)

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“…Greenhouse microclimate control has received considerable attention in recent years due to its great contribution to the improvement of crop yield [13,29,99]. The different factors such as temperature [100,101], relative humidity [102], amount of CO 2 [103,104] are analyzed to predict different events implementing artificial intelligence, statistics and engineering [49,[105][106][107][108].…”

Section: Greenhouse Microclimatementioning

confidence: 99%

Applications of Artificial Neural Networks in Greenhouse Technology and Overview for Smart Agriculture Development

et al. 2020

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This article reviews the applications of artificial neural networks (ANNs) in greenhouse technology, and also presents how this type of model can be developed in the coming years by adapting to new technologies such as the internet of things (IoT) and machine learning (ML). Almost all the analyzed works use the feedforward architecture, while the recurrent and hybrid networks are little exploited in the various tasks of the greenhouses. Throughout the document, different network training techniques are presented, where the feasibility of using optimization models for the learning process is exposed. The advantages and disadvantages of neural networks (NNs) are observed in the different applications in greenhouses, from microclimate prediction, energy expenditure, to more specific tasks such as the control of carbon dioxide. The most important findings in this work can be used as guidelines for developers of smart protected agriculture technology, in which systems involve technologies 4.0.

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Section: Greenhouse Microclimatementioning

confidence: 99%

Applications of Artificial Neural Networks in Greenhouse Technology and Overview for Smart Agriculture Development

et al. 2020

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“…According to the practical conditions of greenhouses, such as intensity of photosynthesis and tightness of the greenhouse, there is a dynamic variation range of CO 2 concentration. However, the reasonable CO 2 concentration for vegetables should be between 500 and 2000 ppm, which is a relatively broad range in consideration of different results provided by related research [4,19]. This means that a real-time measurement and control on CO 2 concentration are desirable.…”

Section: A Design Needs Of the Sensor And Wsnmentioning

confidence: 99%

Mid-infrared absorption-spectroscopy-based carbon dioxide sensor network in greenhouse agriculture: development and deployment

et al. 2016

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A mid-infrared carbon dioxide (CO₂) sensor was experimentally demonstrated for application in a greenhouse farm environment. An optical module was developed using a lamp source, a dual-channel pyre-electrical detector, and a spherical mirror. A multi-pass gas chamber and a dual-channel detection method were adopted to effectively enhance light collection efficiency and suppress environmental influences. The moisture-proof function realized by a breathable waterproof chamber was specially designed for the application of such a sensor in a greenhouse with high humidity. Sensor structure of the optical part and electrical part were described, respectively, and related experiments were carried out to evaluate the sensor performance on CO₂ concentration. The limit of detection of the sensor is 30 ppm with an absorption length of 30 cm. The relative detection error is less than 5% within the measurement range of 30-5000 ppm. The fluctuations for the long-term (10 h) stability measurements on a 500 ppm CO₂ sample and a 2000 ppm CO₂ sample are 1.08% and 3.6%, respectively, indicating a good stability of the sensor. A wireless sensor network-based automatic monitoring system was implemented for greenhouse application using multiple mid-infrared CO₂ sensor nodes. A monitor software based on LabVIEW was realized via a laptop for real-time environmental data display, storage, and website sharing capabilities. A field experiment of the sensor network was carried out in the town of Shelin in Jilin Province, China, which proved that the whole monitoring system possesses stable sensing performance for practical application under the circumstances of a greenhouse.

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CFD analysis of greenhouse heating using flue gas and hot water heat sink pipe networks

Dhiman

Sethi

Singh

et al. 2019

Computers and Electronics in Agriculture

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Development and evaluation of combustion-type CO2 enrichment system connected to heat pump for greenhouses

Cited by 10 publications

References 10 publications

Applications of Artificial Neural Networks in Greenhouse Technology and Overview for Smart Agriculture Development

Applications of Artificial Neural Networks in Greenhouse Technology and Overview for Smart Agriculture Development

Mid-infrared absorption-spectroscopy-based carbon dioxide sensor network in greenhouse agriculture: development and deployment

CFD analysis of greenhouse heating using flue gas and hot water heat sink pipe networks

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