Modeling Framework for Cost Optimization of Process-Scale Desalination Systems with Mineral Scaling and Precipitation

Area of Science:

• Environmental Engineering
• Chemical Engineering
• Water Treatment Technologies

Background:

• Current cost-optimization models for water treatment lack detailed chemical reaction phenomena, limiting assessments of pretreatment and mineral scaling.
• Novel high-salinity and high-recovery desalination approaches often overlook direct quantification of pretreatment requirements and scaling potential.

Purpose of the Study:

• To present a novel modeling framework integrating complex water chemistry predictions with process-scale optimization for desalination.
• To conduct a technoeconomic assessment of a high-recovery desalination treatment train incorporating chemical pretreatment and membrane processes.

Main Methods:

• Development and integration of multidimensional surrogate models to predict precipitation, pH, and mineral scaling tendencies.
• Technoeconomic assessment of a conceptual high-recovery treatment train including soda ash softening, recarbonation, and reverse osmosis.

Main Results:

• Cost-optimal designs were found to balance pretreatment expenses with reverse osmosis system design.
• Optimizing across 50-90% water recovery revealed multiple cost-optimal configurations varying chemical dosing and reverse osmosis operation.
• Pretreatment costs can exceed primary desalination costs at high recoveries due to scaling control needs.

Conclusions:

• Including detailed chemistry and mineral scaling predictions is crucial for evaluating emerging high-recovery desalination technologies.
• The developed framework provides a robust method for optimizing desalination processes by considering chemical phenomena.
• Technoeconomic assessments must account for the significant impact of pretreatment on overall desalination costs, especially at high recovery rates.