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A one-dimensional model for water desalination by flow-through electrode capacitive deionization (1701.09079v1)

Published 28 Jan 2017 in cs.CE and physics.chem-ph

Abstract: Capacitive deionization (CDI) is a fast-emerging water desalination technology in which a small cell voltage of ~1 V across porous carbon electrodes removes salt from feedwaters via electrosorption. In flow-through electrode (FTE) CDI cell architecture, feedwater is pumped through macropores or laser perforated channels in porous electrodes, enabling highly compact cells with parallel flow and electric field, as well as rapid salt removal. We here present a one-dimensional model describing water desalination by FTE CDI, and a comparison to data from a custom-built experimental cell. The model employs simple cell boundary conditions derived via scaling arguments. We show good model-to-data fits with reasonable values for fitting parameters such as the Stern layer capacitance, micropore volume, and attraction energy. Thus, we demonstrate that from an engineering modeling perspective, an FTE CDI cell may be described with simpler one-dimensional models, unlike more typical flow-between electrodes architecture where 2D models are required.

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Summary

  • The paper develops and validates a novel one-dimensional model for flow-through electrode capacitive deionization (FTE CDI), simplifying modeling compared to traditional 2D flow-between systems.
  • The model is validated using experimental data from a custom cell, accurately predicting equilibrium salt adsorption capacity, charge efficiency, current response, and effluent concentration with expected parameter values.
  • This 1D model provides a computationally efficient framework for FTE CDI design and optimization, though further research is needed to incorporate variable pH effects and surface charge groups.

An Examination of a One-Dimensional Model for Desalination via Flow-Through Electrode Capacitive Deionization

This paper details a novel approach to modeling water desalination by capacitive deionization (CDI) using flow-through electrode (FTE) architecture. The authors develop a one-dimensional (1D) model, offering a simplification over the traditional two-dimensional (2D) models generally used for flow-between (FB) electrode systems. This simplification is feasible because, in FTE architecture, electric fields and fluid flows are parallel rather than perpendicular, allowing for different scaling arguments and boundary conditions.

Technological and Theoretical Context

Capacitive deionization is an emergent technology for desalination, particularly applicable to brackish water and wastewater treatment. CDI functions through electrosorption in which ions from feedwater are electrostatically stored in the electric double layers (EDLs) formed in microporous carbon electrodes. This process is facilitated by the application of a low voltage (~1 V) across the electrodes. The paper builds on existing engineering models for CDI but shifts focus onto FTE systems, marking a first in the literature for this architecture.

Methodology

The authors employ a modified Donnan approach to model the EDLs in the micropores. This approach assumes constant potential within micropores, facilitating a mathematical framework that allows for rapid transport modeling aligned with the parallel flow and electric field in FTE systems. They derive closed-form equations for the salt and charge balance, applying them to a simple 1D framework that captures the intricacies of ion transport within the porous medium.

In executing their model, they perform comparisons using empirical data derived from a custom-built CDI cell. This allows them to validate their model parameters, such as Stern layer capacitance and micropore volume, achieving robust approximation for experimental conditions. Notably, the model exhibits close alignment with empirical results for key metrics such as equilibrium salt adsorption capacity and charge efficiency.

Results and Discussion

The paper highlights strong model-to-data congruence, with fitting yielding sterillary expected values for parameters, such as the Stern layer capacitance of 145 F/mL and ion attraction energy of 700 kTĀ·mol. Moreover, the model accurately predicts dynamic behavior, including current response and effluent concentration, during both charge and discharge cycles. Interestingly, the ion attraction energy reported in this paper is significantly higher compared to previous works, suggesting variations could be attributable to micropore structural differences in electrode materials.

An important finding is the ability of the 1D model to predict empirical data without necessitating a 2D approach, marking a simplification with meaningful implications for computational efficiency and accessibility for engineering practices. However, the paper identifies limitations in the model, particularly pertaining to electrolyte diffusion and downstream volume influences, suggesting areas for further refinement.

Implications and Future Directions

The implication of this paper is multifaceted. Practically, this model provides a more accessible framework for the design and optimization of FTE CDI systems, potentially lowering costs and computational requirements associated with desalination technologies. Theoretically, it advances the understanding of transport phenomena in parallel-flow electrode architectures and highlights areas where further experimental and computational work may be needed, particularly in relation to charged surface groups and pH effects in the effluent.

Future research directions could focus on integrating variable pH effects and the incorporation of amphoteric Donnan models to account for charged surface groups within porous electrodes. Furthermore, studying the interplay of varying electrode materials and complex electrolyte solutions under such models may refine optimization strategies for real-world CDI applications. This work stands as a significant contribution, fostering a reduction in the complexity required to model and predict the behavior of desalination systems using CDI technologies.

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