OPTIMAL CO2 CONTROL IN A GREENHOUSE MODELED WITH NEURAL NETWORKS

CO, enrichment in warm climates requires a delicate balance between th e need to ventilate and the desire to enrich. Model-based optimization can achieve this balance, but requires reliable models of the greenho use environment and of the crop response. This study assumes that the crop response is known, and focuses on the greenhouse model. Neural ne twork greenhouse models were trained using data collected over two sum mer months in a small greenhouse. The models were reduced to minimum s ize, by predicting separately the temperature and CO2 concentration, a nd by eliminating any unessential input. The resulting models not only fit the data well, they also seem qualitatively correct, and produce reasonable optimization results. Using these models, the effect of eva porative cooling on extending the enrichment duration is demonstrated. (C) 1998 Elsevier Science B.V. All rights reserved.