DehumReq模型对寒冷地区温室除湿需求影响的敏感性分析

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY Information Processing in Agriculture Pub Date : 2023-06-01 DOI:10.1016/j.inpa.2022.01.004
Md Sazan Rahman , Huiqing Guo
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引用次数: 3

摘要

在本研究中,分析了一种新型除湿需求模型(DehumReq)的敏感性,以评估主要因素对温室除湿需求的影响。采用局部敏感性分析法,估算暖季(7月)、温和季(5月)和寒冷季(11月)的逐时除湿需求和敏感性系数(SC)。基于SC值,太阳辐射、空气交换、叶面积指数(LAI)和室内设定值(温度、相对湿度(RH)和水汽分压(WVPP)对除湿需求有显著影响,且影响因季节而异。大部分参数的SC在夏季较高,而太阳辐射和LAI的SC在温和季节较高。太阳辐射每增加200 W/m2,除湿负荷增加4倍,最大LAI(10)使负荷增加5倍。夏季WVPP由基数(1.5 kPa)变化到最大值(2.9 kPa),使负荷降低70%。空气交换被认为是最关键的参数,因为它是主要的除湿方法,范围大,易于调节任何温室。通过通风或渗透进行充分的空气交换,可将5月和11月的除湿负荷降至零,并将7月的除湿负荷降至最低,仅为夜间负荷。对于其他参数,环境空气相对湿度和室内风速越大,除湿负荷越大;而内表面冷凝量越大,除湿负荷越小。本研究的结果将有助于选择最有效的湿度控制策略和温室湿度控制技术。
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Sensitivity analysis of the DehumReq model to evaluate the impact of predominant factors on dehumidification requirement of greenhouses in cold regions

In this study, the sensitivity of a novel dehumidification requirement model (DehumReq) is analyzed to evaluate the effect of the predominant factors on the dehumidification needs of the greenhouses. The hourly dehumidification demand and sensitivity coefficient (SC) are estimated for three different seasons: warm (July), mild (May), and cold (November), by using the local sensitivity analysis method. Based on SC values, the solar radiation, air exchange, leaf area index (LAI), and indoor setpoints (temperature, relative humidity (RH), and water vapor partial pressure (WVPP)) have significant impact on the dehumidification needs, and the impact varies from season to season. Most parameters have a higher SC in summer, whereas solar radiation and LAI have a higher SC in mild season. The dehumidification load increases 4 times of its base value with increasing solar radiation by 200 W/m2, and the highest LAI (10) caused 5 times increment of the load. The changing of WVPP from its base value (1.5 kPa) to maximum (2.9 kPa) reduces the load 70% in summer. Air exchange was found to be the most crucial parameter because it is the main dehumidification approach that has a large range and is easily adjustable for any greenhouses. Sufficient air exchange by ventilation or infiltration will reduce the dehumidification load to zero in May and November and minimizes it to only nighttime load in July. For the other parameters, higher ambient air RH and indoor air speed will result in higher the dehumidification load; whereas higher inner surface condensation will result in lower dehumidification load. The result of this study will assist in the selection of the most efficient moisture control strategies and techniques for greenhouse humidity control.

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来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
期刊介绍: Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining
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