Reduced-order Modelling (ROM) Approach for Optimal Microclimate Control in Agricultural Greenhouses

Recent efforts to boost self-sufficiency for food through the intensification of domestic production has been considered imperative to insulate from the vagaries of the global food markets. The State of Qatar is an example where more local fresh food production is being promoted, including the current expansion of agricultural greenhouse facilities for domestic vegetables production across the country. However, the hyperarid regional climatic conditions and extreme weather volatilities locally has a direct impact on the greenhouse microclimate owing to the physical processes of energy transfer (radiation and heat) and mass balance (wind and humidity). This consequently results in unpredictability of requirements for: (a) energy for cooling and ventilation; and (b) water for irrigation. In view of these challenges, the objective of the research presented in this paper is to develop a simulation-based methodology for greenhouse microclimate control. A 2D computational fluid dynamics (CFD) model for indoor temperature distribution was used to implement low-dimensional reduced order models (ROM) based on the Proper Orthogonal Decomposition (POD) and Galerkin projection techniques. The results obtained demonstrate that ROM representations are computationally efficient for predictive greenhouse microclimate control systems, providing novel opportunities for timely mitigation of high temperature risks, thereby enhancing the resilience of greenhouses productivity.