A One-Dimensional Reactive Transport Model of Geochemical Scaling in Reverse Osmosis Desalination

Freiburger, Andrew Philip; Molins, Sergi; Buckley, Heather L.

doi:10.2139/ssrn.4124149

Cited by 1 publication

(1 citation statement)

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“…Although there have been great advances in predictive models relevant to mineral scaling in membrane desalination, their application in technoeconomic and process-scale optimization models has been limited, especially with regard to emerging technologies for high-recovery desalination. Many efforts have been focused on incorporating mineral scaling models in bench-scale modules ranging from simple one-dimensional process-scale models to full computational fluid dynamic models relying on either complex mineral scaling models or simple regressions. − Some models have considered scaling limitations on RO systems, but they do not perform full treatment train optimization . Models of mineral scaling have been rarely used for the cost optimization of treatment trains at the full system scale, with some studies estimating pretreatment costs from pilot scale data, − estimating costs from operational data, , and estimating basic pretreatment design from single dimensional regression .…”

Section: Introductionmentioning

confidence: 99%

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

Amusat,

Atia,

Dudchenko

et al. 2024

ACS EST Eng.

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Cost-optimization models are powerful tools for evaluating emerging water treatment processes. However, to date, optimization models do not incorporate detailed chemical reaction phenomena, limiting the assessment of pretreatment and mineral scaling. Moreover, novel approaches for highsalinity and high-recovery desalination are typically proposed without direct quantification of pretreatment needs or mineral scaling. This work addresses a critical gap in the literature by presenting a modeling framework that includes complex water chemistry predictions with process-scale optimization. We use this approach to conduct a technoeconomic assessment on a conceptual highrecovery treatment train that includes chemical pretreatment (i.e., soda ash softening and recarbonation) and membrane-based desalination (i.e., standard and high-pressure reverse osmosis). We demonstrate how to develop and integrate accurate multidimensional surrogate models for predicting precipitation, pH, and mineral scaling tendencies. Our findings show that cost-optimal results balance the costs of pretreatment with reverse osmosis system design. Optimizing across a range of water recoveries (i.e., 50−90%) reveals multiple costoptimal schemas that vary the chemical dosing in pretreatment and the design and operation of reverse osmosis. Our results reveal that pretreatment costs can be more than double the cost of the primary desalination process at high recoveries due to the extensive pretreatment required to control scaling. This work emphasizes the importance of and provides a framework for including chemistry and mineral scaling predictions in the evaluation of emerging technologies in high-recovery desalination.

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