• Medientyp: E-Artikel
  • Titel: Energy Performance of Water Generators from Gaseous Mixtures by Condensation: Climatic Datasets Choice
  • Beteiligte: Cattani, Lucia; Magrini, Anna; Leoni, Valentina
  • Erschienen: MDPI AG, 2022
  • Erschienen in: Energies
  • Sprache: Englisch
  • DOI: 10.3390/en15207581
  • ISSN: 1996-1073
  • Schlagwörter: Energy (miscellaneous) ; Energy Engineering and Power Technology ; Renewable Energy, Sustainability and the Environment ; Electrical and Electronic Engineering ; Control and Optimization ; Engineering (miscellaneous) ; Building and Construction
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  • Beschreibung: <jats:p>Due to the growing issues related to water scarcity and pollution, water extraction from gaseous mixtures, such as atmospheric air, or fumes from combustion, is acquiring increasing importance. Nevertheless, one of the main concerns is the energy consumption that affects the use of any kind of Air(/Gas) to Water Generator (AWG). Referring specifically to water extraction from humid environmental air, AWG behaviour depends upon the air thermo-dynamic conditions and thus upon weather data. To evaluate the water extraction energy efficiency, two interesting tools can be applied: the WET (Water Energy Transformation) indicator, concerning the specific AWG machine behaviour, and the MHI (Moisture Harvesting Index), focused on climate suitability evaluation. Those tools require the knowledge of weather data to be applied. When hourly data for the entire year are available, the application of these tools leads to reliable results. However, in many cases, only average climatic data are available. Today, there are no indications about the reliability of results coming from the use of those less accurate data sets: the research aims to provide a preliminary assessment of the conditions under which average climatic data can be employed without losing meaning. This target was pursued by calculating WET and MHI with three different data sets and five meaningful climate examples. By comparing results, it was possible to provide indications about the most suitable use of average data.</jats:p>
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