Synergizing data-driven and knowledge-based hybrid models for ionic separations

A hybrid modeling framework has been developed for electrodialysis (ED) and resin-wafer electrodeionization (EDI) in brackish water desalination, integrating compositional modeling with machine learning techniques. Initially, a physics-based compositional model is utilized to characterize the behavior of the unit. Synthetic data is then generated to train a machine learning-based surrogate model capable of handling multiple outputs. This model is further refined using a limited set of experimental data. The effectiveness of this approach is demonstrated by its ability to accurately predict experimental results, indicating a faithful representation of the system's behavior. Through analysis of feature importance facilitated by the machine learning model, a nuanced understanding of the interaction between the chosen ion-exchange resin wafer type and ED/EDI operational parameters is obtained. Notably, it is found that the applied cell voltage has a predominant impact on both separation efficiency and energy consumption. By employing multi-objective optimization techniques, experimental conditions are identified that achieve 99% separation efficiency while keeping energy consumption below 1 kWh/kg.