A new greenhouse type has been designed to study ways of decreasing water use by horticulture in semi-arid regions. To control the greenhouse a model-based control design is required. To this end a model is needed to predict the systems behaviour (1 day ahead), without much computational effort. A physics-based model is developed, based on enthalpy and mass balances. The (lumped) key parameters of the model are identified with a controlled random search algorithm. To increase estimation accuracy and reduce computation time, estimation in parts was applied, that is only a part of the whole model was used in combination with measured data for state values of neighbouring compartments. This results in parameter estimates that converge well. In order to keep the model information needs limited, the underlying process details were aggregated into a lumped parameter description, at the expense of time-varying parameters over the seasons. The parameter fluctuation over the year was studied by repeated monthly parameter estimations. Since parameters fluctuate significantly, further research will focus on the use of adaptive mechanisms to facilitate model-based control.
|Publication status||Published - 2010|