LAPSE:2024.1535
Published Article

LAPSE:2024.1535
Cost-optimal Selection of pH Control for Mineral Scaling Prevention in High Recovery Reverse Osmosis Desalination
August 16, 2024. Originally submitted on July 9, 2024
Explicitly incorporating the effects of chemical phenomena such as chemical pretreatment and mineral scaling during the design of treatment systems is critical; however, the complexity of these phenomena and limitations on data have historically hindered the incorporation of detailed water chemistry into the modeling and optimization of water desalination systems. Thus, while qualitative assessments and experimental studies on chemical pretreatment and scaling are abundant in the literature, very little has been done to assess the technoeconomic implications of different chemical pretreatment alternatives within the context of end-to-end water treatment train optimization. In this work, we begin to address this challenge by exploring the impact of pH control during pretreatment on the cost and operation of a high-recovery desalination train. We compare three pH control methods used in water treatment (H2SO4, HCl, and CO2) and assess their impact on the operation of a desalination plant for brackish water and seawater. Our results show that the impact of the acid choice on the cost can vary widely depending on the water source, with CO2 found to be up to 11% and 49% more expensive than HCl in the seawater and brackish cases, respectively. We also find that the acid chemistry can significantly influence upstream processes, with use of H2SO4 requiring more calcium removal in the softening step to prevent gypsum scaling in HPRO system. Our work highlights why incorporating water chemistry information is critical when evaluating the key cost and operational drivers for high-recovery desalination treatment trains.
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Amusat OO, Dudchenko AV, Atia AA, Bartholomew T. Cost-optimal Selection of pH Control for Mineral Scaling Prevention in High Recovery Reverse Osmosis Desalination. Systems and Control Transactions 3:253-260 (2024) https://doi.org/10.69997/sct.143335
Author Affiliations
Amusat OO: Lawrence Berkeley National Laboratory (LBNL), 1 Cyclotron Rd, Berkeley, CA 94720, USA
Dudchenko AV: SLAC National Accelerator Laboratory, 2575 Sand Hill Rd, Menlo Park, CA 94025, USA
Atia AA: National Energy Technology Laboratory (NETL), Pittsburgh, PA 15236, USA; NETL Support Contractor, Pittsburgh, PA, 15236, USA
Bartholomew T: National Energy Technology Laboratory (NETL), Pittsburgh, PA 15236, USA
Dudchenko AV: SLAC National Accelerator Laboratory, 2575 Sand Hill Rd, Menlo Park, CA 94025, USA
Atia AA: National Energy Technology Laboratory (NETL), Pittsburgh, PA 15236, USA; NETL Support Contractor, Pittsburgh, PA, 15236, USA
Bartholomew T: National Energy Technology Laboratory (NETL), Pittsburgh, PA 15236, USA
Journal Name
Systems and Control Transactions
Volume
3
First Page
253
Last Page
260
Year
2024
Publication Date
2024-07-10
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PII: 0253-0260-676185-SCT-3-2024, Publication Type: Journal Article
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LAPSE:2024.1535
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https://doi.org/10.69997/sct.143335
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