Abstract
Reverse Osmosis (RO) is a critical membrane-based process for desalinating seawater and brackish water. However, the selective retention of ions remains inadequately understood, impeding accurate performance predictions across multi-ionic water sources. Despite significant progress in RO engineering, existing models fail to evaluate ion selectivity accurately, complicating the optimization of membranes and the design of RO plants.
Current predictive software for RO performance relies on empirical models that oversimplify complex multi-ionic interactions. Critical ionic phenomena, such as acid-base reactions and ion coupling due to membrane charge constraints and local electroneutrality, are often neglected, limiting the predictive capability for multi-component mixtures.
In this research, we established a novel framework integrating transport theories that account for acid-base reactions and their impacts on ion rejection. Chapter 2 introduces a one-dimensional theoretical mass transport model based on the extended Donnan steric partitioning (ext-DSP) pore model, which examines ion-pair formation and its negative impact on rejection rates in RO membranes. In Chapter 3, the investigation shifts to the influence of cation concentration ratios on ion rejection in various multi-ionic solutions. Chapter 4 delves into the important role of feedwater pH in membrane chemistry and ion rejection, utilizing detailed numerical simulations to assess ion transport across RO membrane thickness. The results indicate that the membrane's slight charge plays a crucial role in determining ion selectivity, challenging previously held assumptions. In Chapter 5, the performance of RO membranes under varying temperatures is characterized using Solution Friction (SF) theory, which reveals temperature-dependent behaviors in both salt and water transport. Finally, Chapter 6 develops a two-dimensional framework for mass transport in spiral wound RO modules, where local axial fluxes and the effects of pressure losses on module performance are analyzed. The study identifies various optimization opportunities related to feed conditions and membrane design.
Collectively, this work lays a robust foundation for accurately predicting ion rejection in multi-ionic RO systems, enhancing the efficiency of desalination practices and water treatment solutions.
Current predictive software for RO performance relies on empirical models that oversimplify complex multi-ionic interactions. Critical ionic phenomena, such as acid-base reactions and ion coupling due to membrane charge constraints and local electroneutrality, are often neglected, limiting the predictive capability for multi-component mixtures.
In this research, we established a novel framework integrating transport theories that account for acid-base reactions and their impacts on ion rejection. Chapter 2 introduces a one-dimensional theoretical mass transport model based on the extended Donnan steric partitioning (ext-DSP) pore model, which examines ion-pair formation and its negative impact on rejection rates in RO membranes. In Chapter 3, the investigation shifts to the influence of cation concentration ratios on ion rejection in various multi-ionic solutions. Chapter 4 delves into the important role of feedwater pH in membrane chemistry and ion rejection, utilizing detailed numerical simulations to assess ion transport across RO membrane thickness. The results indicate that the membrane's slight charge plays a crucial role in determining ion selectivity, challenging previously held assumptions. In Chapter 5, the performance of RO membranes under varying temperatures is characterized using Solution Friction (SF) theory, which reveals temperature-dependent behaviors in both salt and water transport. Finally, Chapter 6 develops a two-dimensional framework for mass transport in spiral wound RO modules, where local axial fluxes and the effects of pressure losses on module performance are analyzed. The study identifies various optimization opportunities related to feed conditions and membrane design.
Collectively, this work lays a robust foundation for accurately predicting ion rejection in multi-ionic RO systems, enhancing the efficiency of desalination practices and water treatment solutions.
| Original language | English |
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| Qualification | Doctor of Philosophy |
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| Award date | 31 Mar 2025 |
| Place of Publication | Enschede |
| Publisher | |
| Print ISBNs | 978-90-365-6535-6 |
| Electronic ISBNs | 978-90-365-6536-3 |
| DOIs | |
| Publication status | Published - 31 Mar 2025 |
