Heat transfer correlation for absorption chillers through investigation of falling film evaporator data

To realize carbon neutrality, the existing system or working fluid are being replaced by ecofriendly alternatives. In particular, various heat exchangers are being continuously investigated for optimization and high efficiency under changing conditions. The falling-film evaporator of the absorption chiller has excellent performance, but the predictive performance of parameter correlations should be further improved and generalized to establish an operating strategy or optimal design. Artificial neural networks are considered as alternatives to existing prediction methods in various fields. However, conventional correlations are still necessary to facilitate the analysis of physical relationships. Instead of developing a model for classification or prediction using an artificial neural network, we use a network to analyze collected experimental data. The proposed method contributes to deriving an accurate correlation. Using the trained artificial neural network, off-trend data organization, key parameter determination, trend investigation per parameter, and similarity analysis between different tube types are performed. The results allow to derive a general correlation for predicting the evaporator heat transfer performance. This correlation, which integrates experimental data from eight studies, is consistent with collected data, with an approximate error of 13.2 %.