Abstract
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.
Highlights
The liquid desiccant air conditioning systems (LDACS) are alternatives to the traditional vapor compression refrigeration systems (VCRS)
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)
It can be detected from the figure that, the BP-ANN model achieves a very good agreement with the measurements
Summary
The liquid desiccant air conditioning systems (LDACS) are alternatives to the traditional vapor compression refrigeration systems (VCRS) These systems conduct air dehumidification with liquid desiccant (LD) to reduce electrical energy consumption. The LDACS have recently gained growing interest from the stand point of reducing energy consumption during the dehumidification. Tributyl phosphonium dimethyl phosphate presented the greatest dehumidification capacity and had a minimum corrosive impact. They found that a 77% (w/w) aqueous solution of this liquid worked as an effective desiccant liquid for the LDACS. They found that the electric current efficiency correlation of an electrodialysis
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