In this study, the performance of an integrated desiccant air conditioning system (IDACS)
driven by solar energy is experimentally tested and predicted by back propagation artificial
neural network (BP-ANN). The IDACS is generally includes a liquid desiccant
dehumidification cycle combined with a vapor compression refrigeration cycle. The integrated
system performance is assessed utilizing the system coefficient of performance (COP), outlet
dry air temperature (Tda-out), and specific moisture removal (SMR). The training of the BP-ANN
is accomplished utilizing experimental results previously published. The results of the BP-ANN
model revealed the high accuracy in predicting system performance parameters compared with
experimental values. The BP-ANN model has shown relative errors in the trained mode for
COP, Tda-out, and SMR within ±0.005%, ±0.006%, and ±0.05%, respectively. On the other side,
the BP-ANN model is inspected in the predictive mode as well. The relative errors of the model
for COP, Tda-out, and SMR in the predictive mode are within ±0.006%, ±0.006%, and ±0.004%,
respectively. The influences of some selected parameters, namely regeneration temperature,
desiccant solution temperature in the condenser and evaporator, and strong solution
concentration on the system performance are examined and discussed as well.