(480a) Techno-Economic Assessment of Boron Removal: Integrating Complex Chemistry Surrogate Modeling for Cost Optimization

Boron contamination in water sources presents significant environmental and public health risks, highlighting the need for regulatory concentration limits. Its unique aqueous chemistry makes boron challenging to remove with conventional seawater desalination techniques, increasing the demand for cost-effective solutions. For this purpose, traditional methods, such as two-pass reverse osmosis (RO) with pH swing, are common; however, few studies have evaluated their economic implications for meeting regulatory compliance. This study integrates surrogate modeling with process simulation to enhance the techno-economic analysis (TEA) of boron removal strategies. It establishes cost benchmarks and guidelines while defining economic targets for new treatment technologies.

The methodology uses surrogate modeling to address complex chemistry and optimize removal strategies. Chemical equilibrium modeling with PHREEQC (software for performing aqueous geochemical calculations) generates data for surrogate model development in PySMO (a Python-based toolbox for generating surrogate models), capturing nonlinear interactions in acidification, pH swing, and boron rejection. These surrogates are incorporated into WaterTAP for process simulation and cost optimization. A baseline single-pass seawater RO system, without specific boron treatment, achieves a levelized cost of water (LCOW) of approximately $0.50/m³. With the addition of boron removal, the two-pass RO system with pH swing experiences an increase in this cost.

Preliminary findings indicate that surrogate modeling of complex chemistry delivers accurate predictions of boron rejection performance and cost trade-offs with reduced computational effort, thereby improving optimization strategies. The analysis identifies rejection requirements based on inlet boron concentrations and regulatory limits, providing insights into cost-effective removal strategies. This research highlights the potential of machine learning in modeling complex chemistry for process design, guiding policy decisions, and advancing next-generation treatment solutions.

Acknowledgements

This material is based upon work supported by the National Alliance for Water Innovation (NAWI), funded by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE), Industrial Efficiency & Decarbonization Office, under Funding Opportunity Announcement Number DE-FOA-0001905. The version of IPOPT implemented in this work was compiled using HSL, a collection of Fortran codes for large-scale scientific computation. See http://www.hsl.rl.ac.uk.

Disclaimer

This project was funded by the Department of Energy, National Energy Technology Laboratory an agency of the United States Government, through a support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.