A methodology for model-based greenhouse design: Part 5, greenhouse design optimisation for southern-Spanish and Dutch conditions

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26 Citations (Scopus)

Abstract

An optimisation algorithm, as an essential part of a model-based method to design greenhouses for a broad range of climatic and economic conditions, was described. This algorithm – a modified controlled random search using parallel computing – maximised the annual Net Financial Return (NFR) for a tomato grower by selecting the best alternative to fulfil eight design elements: type of greenhouse structure, material of the cover, outdoor shade screen, whitewash properties, thermal screen, heating system, cooling system and CO2 enrichment system. As an example, the algorithm was applied to two locations with different climatic and economic conditions, Almeria and The Netherlands. Due to the warm climate with high radiation levels in Almeria, a greenhouse with a relatively large specific ventilation area (20% compared to 14% for Dutch conditions), seasonal whitewash and a low-capacity direct air heater (50 W m-2 compared to 200 W m-2 for Dutch conditions) was selected. In contrast, for the relatively cold climate with low radiation levels of the Netherlands, a 100% aluminium thermal screen and no whitewash would give the best result. The design method produced realistic greenhouses and related annual NFR, indicating that the method performs well. An analysis of the close-to-best greenhouses showed that, for both locations, a structure with high light transmissivity considerably enhanced the greenhouse performance whereas an outdoor shade screen, geothermal heating and mechanical cooling would be not economical. These results demonstrate the feasibility of a model-based design approach that produces suitable greenhouse designs for given climatic and economic conditions. --------------------------------------------------------------------------------
Original languageEnglish
Pages (from-to)350-368
JournalBiosystems Engineering
Volume111
Issue number4
DOIs
Publication statusPublished - 2012

Keywords

  • controlled random search
  • global optimization
  • yield model

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