In this study, the application of data-driven methods for performance prediction of a heat pump assisted humidification-dehumidification (HDH-HP) desalination system was investigated for the first time. Although HDH-HP desalination systems have been widely studied both theoretically and experimentally, the application of data-driven models as a powerful predictive tool has not yet been investigated in these systems. To fill this gap, three data-driven models (MLPANN, RBFANN, and ANFIS) were applied using 180 experimental samples. The gain output ratio (GOR), heat transfer rates of the evaporator $\left({\dot{\mathrm{Q}}}_{\mathrm{e}}\right)$, and evaporative condenser $\left({\dot{\mathrm{Q}}}_{\mathrm{c}}\right)$, were considered as outputs. The results indicate that the MLPANN model is superior in predicting all target parameters showing R² values of 0.915, 0.995, and 0.988 for GOR, ${\dot{\mathrm{Q}}}_{\mathrm{e}}$, and ${\dot{\mathrm{Q}}}_{\mathrm{c}}$, respectively. Further, the ANFIS model performance was shown to be weak for predicting GOR. Finally, a comparison was made between the experimental heat transfer rates, MLPANN model, and compressor polynomials. The predicted values using the MLPANN model were found to be in excellent agreement with experimental data, possessing a MAPE of 0.48% and 0.77% as compared to predicted values by compressor polynomials with MAPE of 9.53%, and 3.3%, for ${\dot{\mathrm{Q}}}_{\mathrm{e}}$, and ${\dot{\mathrm{Q}}}_{\mathrm{c}}$, respectively.

在这项研究中，首次研究了数据驱动方法在热泵辅助加湿-除湿（HDH-HP）脱盐系统性能预测中的应用。尽管已经在理论和实验上对HDH-HP淡化系统进行了广泛的研究，但尚未在这些系统中研究将数据驱动模型作为强大的预测工具的应用。为了填补这一空白，使用180个实验样本应用了三个数据驱动模型（MLPANN，RBFANN和ANFIS）。增益输出比（GOR），蒸发器的传热率$\left({\dot{\mathrm{问}}}_{\mathrm{Ë}}\right)$和蒸发冷凝器 $\left({\dot{\mathrm{问}}}_{\mathrm{C}}\right)$，被视为输出。结果表明，MLPANN模型在预测所有目标参数方面表现出优异的效果，这些目标参数显示出GOR的R ²值分别为0.915、0.995和0.988。${\dot{\mathrm{问}}}_{\mathrm{Ë}}$， 和 ${\dot{\mathrm{问}}}_{\mathrm{C}}$， 分别。此外，ANFIS模型的性能对于预测GOR较弱。最后，对实验传热速率，MLPANN模型和压缩机多项式进行了比较。发现使用MLPANN模型的预测值与实验数据非常吻合，MAPE分别为0.48％和0.77％，而压缩机多项式的预测值为MAPE的9.53％和3.3％。${\dot{\mathrm{问}}}_{\mathrm{Ë}}$， 和 ${\dot{\mathrm{问}}}_{\mathrm{C}}$， 分别。