Desiccant air conditioning systems are considered as better alternatives to the conventional air conditioning system because of the independent control of temperature and humidity and being environment friendly. An artificial neural network (ANN) model has been developed to predict the performance of a rotary desiccant dehumidifier for different process air inlet conditions. Dry bulb temperature, humidity ratio and flow rate of the process as well as regeneration air streams of dehumidifier and regeneration temperatures are used as inputs to the model. The outputs of the model are outlet dry bulb temperature and humidity ratio of process as well as regeneration air streams of dehumidifier, the moisture removal rate and the effectiveness of the dehumidifier. Moisture removal rate and effectiveness of the dehumidifier are considered as the performance indicators of the system. Experiments are also conducted to investigate the performance of the desiccant wheel and the test results are used as target data to train the ANN model. Performance predictions through ANN are compared with the experimental test results and a close agreement is observed. © 2015 Elsevier Ltd. All rights reserved.
|Number of pages||13|
|Journal||Applied Thermal Engineering|
|Publication status||Published - 2016|