This work proposes an integrated framework to build fast and high-fidelity bi-level reduced-order models (ROMs) for heat and mass transfer processes systems. In the proposed framework, the bi-level ROMs including the ROM for output variables (yROM) and the ROM for state variables (zROM) corresponding to its input variables of the systems are developed by integrating principal component analysis (PCA) with artificial neural networks (ANN), which can closely approximate the results of computational fluid dynamics (CFD). In particular, the CPU time is significantly reduced with the fast bi-level ROMs. A case study is used to demonstrate the benefits of using our proposed bi-level ROMs methodology for simulation and optimisation for heat and mass transfer processes systems, where a bi-level ROMs for the closed wet cooling towers (CWCTs) is built with the help of exergy analysis. The design points served for CFD simulations are randomly sampled by stochastic reduced-order modelling (SROM). The developed yROM is used for stochastic optimisation of exergy efficiency ratio and then the corresponding optimal exergy flux fields are then reconstructed and analysed based on zROM.