Artificial neural network model for performance evaluation of an integrated desiccant air conditioning system activated by solar energy
Ayman A. Aly,
B. Saleh,
M. M. Bassuoni,
M. Alsehli,
A. Elfasakhany,
Khaled I.E. Ahmed
Affiliations
Ayman A. Aly
1 Mechanical Engineering Department, College of Engineering, Taif University, PO Box 888, Taif, Saudi Arabia 2 Mechanical Engineering Department, Faculty of Engineering, Assiut University, PO Box 71516, Assiut, Egypt
B. Saleh
1 Mechanical Engineering Department, College of Engineering, Taif University, PO Box 888, Taif, Saudi Arabia 2 Mechanical Engineering Department, Faculty of Engineering, Assiut University, PO Box 71516, Assiut, Egypt
M. M. Bassuoni
1 Mechanical Engineering Department, College of Engineering, Taif University, PO Box 888, Taif, Saudi Arabia3 Mechanical Power Engineering Department, Faculty of Engineering, Tanta University, Egypt
M. Alsehli
1 Mechanical Engineering Department, College of Engineering, Taif University, PO Box 888, Taif, Saudi Arabia
A. Elfasakhany
1 Mechanical Engineering Department, College of Engineering, Taif University, PO Box 888, Taif, Saudi Arabia
Khaled I.E. Ahmed
4 Dept. of Mech. Engineering, Faculty of Engineering, King AbdulAziz University, PO Box 21589 Jeddah, Saudi Arabia
In this study, the performance of an integrated desiccant air conditioning system (IDACS) activated by solar energy is evaluated by back propagation artificial neural network (BP-ANN). The IDACS consists of 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.
