(375y) Enhancing Electrochemical Deionization Optimization through Integrated Physics and Machine Learning Models

The chemical industry requires optimization of processes and materials to meet increasing demands for energy and water supply, driven by a growing global population projected to reach 9 billion by 2050¹. To optimize the process and material conditions, traditional experimental approaches encounter challenges in time, cost, and resource constraints. Simulation approach such as mathematical or data-driven models are crucial in this context, as they facilitate the development of new technologies by enabling simulation, sensitivity analysis, techno-economic evaluations, and optimization^2,3.

In this work, a modeling framework integrating compositional modeling and machine learning, is formulated for an electrodialysis (ED) and electrodeionization (EDI) employed in water desalination. The devised approach leverages a physics-based compositional model to characterize the unit’s behavior followed by generating synthetic data to train a machine learning-based, multi-output surrogate model predicting salt removal efficiency, energy consumed and thermodynamic efficiency. This model is fine-tuned using limited experimental data. This approach’s ability to predict experimental data signals its accurate representation of the system's behavior. Through the ML-based model, feature importance analysis is conducted, revealing the intricate interplay between the chosen ion-exchange resin wafer type and ED/EDI operational parameters. Notably, it is determined that the applied cell voltage predominantly influences separation efficiency and energy consumption in both electrodialysis and electrodeionization devices. Utilizing multi-objective optimization, experimental conditions are identified for achieving 99% separation efficiency with energy consumption below 1 kWh/kg.

References

1. Campione, A. et al. Electrodialysis for water desalination: A critical assessment of recent developments on process fundamentals, models and applications. Desalination 434, 121–160 (2018).
2. Lively, R. P. The refinery of today, tomorrow, and the future: A separations perspective. AIChE Journal 67, e17286 (2021).
3. Sansana, J. et al. Recent trends on hybrid modeling for Industry 4.0. Computers & Chemical Engineering 151, 107365 (2021).