Error propagation on COP prediction by artificial neural network in a water purification system integrated to an absorption heat transformer

Numerous authors have reported heat transfer prediction using artificial neural network (ANN). However, the precision or accuracy of the calculation is generally unknown. Error propagation from Monte Carlo method is applied to the coefficient of performance (COP) predicted by ANN. This COP permitted us to evaluate a water purification process integrated into a heat transformer. A feedforward network with a hidden layer was used in order to obtain error propagation in COP prediction. This model used the input and output-temperatures for each component (absorber, generator, evaporator, and condenser), as well as two pressure parameters from the absorption heat transformer and LiBr + H2O mixture with different LiBr concentrations. The hyperbolic tangent sigmoid transfer-function and the linear transfer-function were used for the network. A new correlation for calculating relative standard deviation (%RSDCOP) of COP as a function of COPEXP and %RSDinstrument was obtained. This study shows that %RSDCOP of ANN prediction decreased when the experimental COP is increased. The range of COP operations was from 0.21 to 0.39.