Validation of multitask artificial neural networks to model desiccant wheels activated at low temperature

Abstract

Desiccant wheels (DW) could be a serious alternative to conventional dehumidification systems based on direct expansion units, which depend on electrical energy. The main objective of this work was to evaluate the use of multitask artificial neural networks (ANNs) as a modelling technique for DWs activated at low temperature with low computational load and good accuracy. Two different ANN models were developed to predict two output variables: outlet process air temperature and humidity ratio. The results show that a sigmoid unit neural network obtained 0.390 and 2.987 for MSE and SEP, respectively. These results outline the effective transfer mechanism of multitask ANNs to extract common features of multiple tasks, being useful for modelling a DW activated at low temperature. On the other hand, moisture removal capacity of the DW and its performance were analysed under several inlet air conditions, showing an increase under process air conditions close to saturation air.