and dry membrane states.

The membrane's swelling behavior (Eq. (37)) is another critical, influencing its dimensional stability, selectivity, electrical resistance, and hydraulic permeability [209]. This characteristic is dependent on the polymeric material's nature, its IEC, and the degree of cross-linking, typically measured by the variation in water content between the membrane's wet and dry states, indicating the extent of water absorption under specific conditions [210].

### 3.6.1.4. Electrical resistance.
Resistance, measured in Ω, is conventionally referred to as 'electrical resistance'. This term characterizes the capacity of a conductor, such as a resistor, to hinder the flow of electrical current, denoted by I, when a voltage is applied across it. However, in scenarios where a salt solution is partitioned by IEMs, the conveyance of current is facilitated not only by the ions within the solution but also by those within the membrane's pores. In such context, the concept of 'ion transport resistance' might be more aptly applied than the traditional notion of electrical resistance. In the realm of liquids, the concept of resistance is often inverted to discuss conductance (measured in S), which encapsulates the capacity for current transport. The value of a membrane's electrical resistance is influenced by its IEC as well as the mobility $u_i^m$ of ions crossing the membrane's structure [211]. Determination of this parameter can be performed by 2 different methods either (i) direct current, and (ii) EIS with alternating current and frequency variation [212,213].

The electrical resistance exhibited by IEMs plays a crucial role in influencing the energy requirements (especially $\eta_{ohm}^{IEM}$) of ED processes. Nevertheless, in many practical scenarios, the resistance encountered within the membrane is notably less than that of dilute solutions ($\eta_{ohm}^{dlt} > \eta_{ohm}^{IEM}$). This reduced resistance is attributed to the comparatively high concentration of ions within the membrane itself. Typically, this resistance is quantified in terms of specific membrane resistance (expressed in Ω m). However, for engineering applications, expressing membrane resistance as ASR in Ω/m⁻²(-|·|) proves to be more practical and is commonly referenced in commercial product specifications. It often ranges from 1 to 100 Ω cm⁻² depending on the characteristics of the membranes [214].

### 3.6.2. Permselectivity

The permselectivity PS of an IEM (dimensionless), indicative of its selectivity for counter-ion transport over co-ion transport, is defined through transport numbers as Eq. (38). The transport number t (dimensionless) reflects the proportion of ionic current carried by an ion i as presented in Eq. (39).

$$PS = \frac{t_{ct}^{m} - t_{ct}^{s}}{1 - t_{ct}^{s}} \qquad (38)$$

$$t_i = \frac{|z_i| J_i}{\sum_i |z_i| J_i} \qquad (39)$$

A membrane achieves perfect permselectivity PS = 1 when only counter-ions contribute to ion transport $t_{ct}^m = 1$, whereas it exhibits no selectivity PS = 0 when the transport behavior mimics that in the solution phase $t_{ct}^s = t_{ct}^{s} [215]$.

IEMs traditionally differentiate between counterions and co-ions, but they also facilitate selective ion separations. These membranes can distinguish ions with similar charges but different valencies, such as separating Li⁺ from Mg²⁺ in lithium-rich brines. Further, IEMs capable of identifying ions of the same valence allow for specific separations, such as extracting Co²⁺ from Ni²⁺ in battery recycling fields. As presented in Fig. 11, several recent reviews [23,80,84,85,216] provide extensive insights into the different types of IEM selectivity, namely charge, valence, and specific ion selectivities:

(i) Charge selectivity: this selectivity is facilitated by the inherent negatively or positively charged functional groups present in IEMs. The underlying mechanism of selectivity is fundamentally based on the principles of charge attraction and repulsion as depicted in Fig. 11 – a. For example, in CEMs, the negatively charged functional groups draw cationic counterions towards the membrane while simultaneously repelling anionic co-ions. Enhanced charge selectivity is primarily achieved by increasing the densities of fixed charges within the membrane (X⁰). This augmentation increases the magnitude of the Donnan potential difference, $\Delta\psi_D$, which serves to exclude co-ions more effectively from the membrane.

(ii) Valence selectivity: the separation of similarly charged ions that differ in valence is also facilitated in IEMs. This differentiation is influenced by ion valence, as demonstrated in Eqs. (20), (21) and (32), which show that valence plays a crucial role in both sorption and migration selectivities. Essentially, the valency of ions provides a dual pathway—via sorption and migration mechanisms—to modulate the relative transportation of different counterions. The examination of sorption selectivity (K), indicates that in mixed electrolyte solutions, counterions with a higher valence, z, are preferentially concentrated within the membrane matrix as observed in Fig. 11 – b. This effect can be intuitively understood considering the stronger coulombic attraction between the fixed functional groups of the IEM and species with a larger absolute value of z. There are primarily two strategies commonly used to selectively transport monovalent counterions over their polyvalent counterparts: (i) the first method leverages the increased exclusion of co-ions with higher valences, effectively reducing their mobility through the membrane, while (ii) the second method involves the application of dense coating layers on the membrane, which enhances the steric hindrance encountered by polyvalent counterions, thereby impeding their passage.

(iii) Specific ion selectivity: IEMs could also differentiate between counterions of the same valence which would revolutionize specific ion selectivity. However, since the valence sign and magnitude are the same for the ions intended for separation, ion charge does not serve as a viable criterion for distinguishing between them, unlike in cases of charge and valence-based selectivities. Consequently, achieving specific ion selectivity presents significant technical challenges, and currently, no such membranes are commercially available. To overcome this, coordination chemistry has been explored to facilitate the selective transport of targeted species. By employing ligands that preferentially form reversible complexes with specific ions, these ions can effectively “hop” from one site to another, as illustrated in Fig. 11 – c. This approach utilizes specific coordination interactions to discriminate between counterions with identical valences and selectively transport certain ions over others. It is notable that ions with the same z value typically exhibit similar sizes in their hydrated states, whether they are mono- or polyatomic, which limits the effectiveness of steric methods in enhancing ion discrimination as depicted in Fig. 10.

Moreover, phenomena affecting membrane selectivity (i.e., electroosmotic and diffusive phenomena) will be detailed in a dedicated Section 4.6 below.

### 3.6.3. Chemical stability

The expenses associated with IEMs represent a notable portion of both the initial setup costs and the ongoing costs for ED [217]. Consequently, there is a push towards not only lowering the cost of these membranes but also prolonging their lifespan. However, the performance and structural integrity of membranes during ED may be compromised by several factors, such as the accumulation of organic
and dry membrane states.

The membrane's swelling behavior (Eq. (37)) is another critical, influencing its dimensional stability, selectivity, electrical resistance, and hydraulic permeability [209]. This characteristic is dependent on the polymeric material's nature, its IEC, and the degree of cross-linking, typically measured by the variation in water content between the membrane's wet and dry states, indicating the extent of water absorption under specific conditions [210].

### 3.6.1.4. Electrical resistance.
Resistance, measured in Ω, is conventionally referred to as 'electrical resistance'. This term characterizes the capacity of a conductor, such as a resistor, to hinder the flow of electrical current, denoted by I, when a voltage is applied across it. However, in scenarios where a salt solution is partitioned by IEMs, the conveyance of current is facilitated not only by the ions within the solution but also by those within the membrane's pores. In such context, the concept of 'ion transport resistance' might be more aptly applied than the traditional notion of electrical resistance. In the realm of liquids, the concept of resistance is often inverted to discuss conductance (measured in S), which encapsulates the capacity for current transport. The value of a membrane's electrical resistance is influenced by its IEC as well as the mobility $u_i^m$ of ions crossing the membrane's structure [211]. Determination of this parameter can be performed by 2 different methods either (i) direct current, and (ii) EIS with alternating current and frequency variation [212,213].

The electrical resistance exhibited by IEMs plays a crucial role in influencing the energy requirements (especially $\eta_{ohm}^{IEM}$) of ED processes. Nevertheless, in many practical scenarios, the resistance encountered within the membrane is notably less than that of dilute solutions ($\eta_{ohm}^{dlt} > \eta_{ohm}^{IEM}$). This reduced resistance is attributed to the comparatively high concentration of ions within the membrane itself. Typically, this resistance is quantified in terms of specific membrane resistance (expressed in Ω m). However, for engineering applications, expressing membrane resistance as ASR in Ω/m⁻²(-|·|) proves to be more practical and is commonly referenced in commercial product specifications. It often ranges from 1 to 100 Ω cm⁻² depending on the characteristics of the membranes [214].

### 3.6.2. Permselectivity

The permselectivity PS of an IEM (dimensionless), indicative of its selectivity for counter-ion transport over co-ion transport, is defined through transport numbers as Eq. (38). The transport number t (dimensionless) reflects the proportion of ionic current carried by an ion i as presented in Eq. (39).

$$PS = \frac{t_{ct}^{m} - t_{ct}^{s}}{1 - t_{ct}^{s}} \qquad (38)$$

$$t_i = \frac{|z_i| J_i}{\sum_j |z_j| J_j} \qquad (39)$$

A membrane achieves perfect permselectivity PS = 1 when only counter-ions contribute to ion transport $t_{ct}^m = 1$, whereas it exhibits no selectivity PS = 0 when the transport behavior mimics that in the solution phase $t_{ct}^s = t_{ct}^{s} [215]$.

IEMs traditionally differentiate between counterions and co-ions, but they also facilitate selective ion separations. These membranes can distinguish ions with similar charges but different valencies, such as separating Li⁺ from Mg²⁺ in lithium-rich brines. Further, IEMs capable of identifying ions of the same valence allow for specific separations, such as extracting Co²⁺ from Ni²⁺ in battery recycling fields. As presented in Fig. 11, several recent reviews [23,80,84,85,216] provide extensive insights into the different types of IEM selectivity, namely charge, valence, and specific ion selectivities:

(i) Charge selectivity: this selectivity is facilitated by the inherent negatively or positively charged functional groups present in IEMs. The underlying mechanism of selectivity is fundamentally based on the principles of charge attraction and repulsion as depicted in Fig. 11 – a. For example, in CEMs, the negatively charged functional groups draw cationic counterions towards the membrane while simultaneously repelling anionic co-ions. Enhanced charge selectivity is primarily achieved by increasing the densities of fixed charges within the membrane (X⁰). This augmentation increases the magnitude of the Donnan potential difference, $\Delta\psi_D$, which serves to exclude co-ions more effectively from the membrane.

(ii) Valence selectivity: the separation of similarly charged ions that differ in valence is also facilitated in IEMs. This differentiation is influenced by ion valence, as demonstrated in Eqs. (20), (21) and (32), which show that valence plays a crucial role in both sorption and migration selectivities. Essentially, the valency of ions provides a dual pathway—via sorption and migration mechanisms—to modulate the relative transportation of different counterions. The examination of sorption selectivity (K), indicates that in mixed electrolyte solutions, counterions with a higher valence, z, are preferentially concentrated within the membrane matrix as observed in Fig. 11 – b. This effect can be intuitively understood considering the stronger coulombic attraction between the fixed functional groups of the IEM and species with a larger absolute value of z. There are primarily two strategies commonly used to selectively transport monovalent counterions over their polyvalent counterparts: (i) the first method leverages the increased exclusion of co-ions with higher valences, effectively reducing their mobility through the membrane, while (ii) the second method involves the application of dense coating layers on the membrane, which enhances the steric hindrance encountered by polyvalent counterions, thereby impeding their passage.

(iii) Specific ion selectivity: IEMs could also differentiate between counterions of the same valence which would revolutionize specific ion selectivity. However, since the valence sign and magnitude are the same for the ions intended for separation, ion charge does not serve as a viable criterion for distinguishing between them, unlike in cases of charge and valence-based selectivities. Consequently, achieving specific ion selectivity presents significant technical challenges, and currently, no such membranes are commercially available. To overcome this, coordination chemistry has been explored to facilitate the selective transport of targeted species. By employing ligands that preferentially form reversible complexes with specific ions, these ions can effectively “hop” from one site to another, as illustrated in Fig. 11 – c. This approach utilizes specific coordination interactions to discriminate between counterions with identical valences and selectively transport certain ions over others. It is notable that ions with the same z value typically exhibit similar sizes in their hydrated states, whether they are mono- or polyatomic, which limits the effectiveness of steric methods in enhancing ion discrimination as depicted in Fig. 10.

Moreover, phenomena affecting membrane selectivity (i.e., electroosmotic and diffusive phenomena) will be detailed in a dedicated Section 4.6 below.

### 3.6.3. Chemical stability

The expenses associated with IEMs represent a notable portion of both the initial setup costs and the ongoing costs for ED [217]. Consequently, there is a push towards not only lowering the cost of these membranes but also prolonging their lifespan. However, the performance and structural integrity of membranes during ED may be compromised by several factors, such as the accumulation of organic
and inorganic deposits, pH fluctuations, and interactions with treated solution components [218], leading to their deterioration by several mechanisms.

### 3.6.3.1. Fouling.
This phenomenon involves the chemical interaction between the IEM and the components of the solution being treated, leading to compound deposition and membrane property deterioration. This degradation often manifests as reduced electrical conductivity (blockage of pores, ionic sites blocked by multivalent ions) or a decline in limiting current density [219], with models available to describe these effects based on the type of fouling film formed [220,221]. Fouling in IEMs can be categorized (Fig. 9) by its causes as: inorganic substance fouling (also known as scaling due to the formation of a 'scaly' precipitate) [222,223], fouling due to organic molecules and colloids (especially in the food industry) [224,225], and biofouling [226,227].

### 3.6.3.2. Chemical reactions and physical interactions.
IEM degradation encompasses not just fouling but also involves mechanisms unrelated to substance deposition. These include interactions with the physicochemical properties of the solution, such as high ionic strength and reactions between membrane components and solution ions, including $H^+$ and $OH^-$ ions. These reactions, catalyzed by the membrane's polar groups, extend beyond simple sediment formation, affecting the membrane significantly [217]. The concentration of $H^+$ and $OH^-$ ions in solutions can alter due to inherent solution properties or generated during ED, impacting IEMs in applications like alkaline fuel cells [229-232], where this issue is extensively discussed. The presence of these ions can lead to protonation/deprotonation reactions and irreversible reactions with membrane materials (such as the cation groups attack via Hofmann elimination, nucleophilic substitution to cleave the cationic group from the tethered bond to the polymer backbone, and nucleophilic substitution of dealkylation [233,234]), highlighting the complex influence of physicochemical interactions on membrane longevity and functionality. The degradation of the membrane is influenced by two additional parameters: the elevated ionic strength of the solution [235], and the stretching of the membrane matrix caused by ions with bulky hydration shells [236]. Hydration shell can increase the ions radius by a factor of 14 in the most severe cases, as illustrated in Fig. 10. This significant expansion results in increased mechanical stress on the membrane, further contributing to its degradation.

## 4. Operational parameters and their impacts on efficiency and selectivity

The performance of the ED process, along with its product quality and economic viability, is significantly influenced by the operating conditions. Specifically, the factors affecting these outcomes can be divided into four main categories: (i) intensity of the electric field; (ii) concentration of the initial solution; (iii) presence of co-ions; and (iv) rate of solution feed. The various operating parameters that can influence the performance of the reactor, as well as the phenomena impacted, are synthesized in Fig. 12, and studies focusing more specifically on each phenomenon are provided as examples. To facilitate a comprehensive

<figure><img src="image_11.png" /><figcaption>**Fig. 9.** Types of fouling. Reprinted from [228], Copyright (2016), with permission from Elsevier.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>**Fig. 10.** Relationship between the radius of ions and their hydration status. Ions of smaller dimensions exhibit a greater propensity for hydration, attributed to their higher charge density. Reprinted with permission from [237].</figcaption></figure>

understanding of ED and to foster enhancements in process efficiency, this section will present a detailed examination of each influencing factor and highlight the limiting factors of the process.

### 4.1. Electric field intensity

In ED, the primary force propelling ion migration is the electric field intensity as previously presented in Eq. (21). The influence of this electric field intensity on the process is examined through two key parameters: current density $j$ and cell voltage $U_{stack}$ which are intrinsically combined. In ED processes, the practice of maintaining a constant current is widespread. Here, the current density is the applied current divided by the effective surface area of the membranes in use. The same types of conclusions presented in this section are therefore identical concerning process behavior in response to cell voltage. The literature on this topic suggests that the impact on the process encompasses several key dimensions: (i) ion migration, (ii) concentration of reaction products, (iii) energy consumption / current efficiency.

An elevation in current density is associated with enhanced ion migration, which contributes to a reduction in the operational duration of the ED process for the same conversion time by increasing the flux across the membrane. For example, the increase in current density was observed to elevate the potassium average flux, illustrating the promotion of ion migration during separation between $K^+$ and $Mg^{2+}$ [238]. Furthermore, Liu et al. conducted research on the ED process for obtaining $Ni^{2+}$, $NH_4^+$, total phosphorus and $NO_3^-$ from electroless nickel-plating wastewater. Their findings revealed that an increase in current density (from 20 to 25 mA cm$^{-2}$) reduced the operation time from 12 to 10 h for the same removal rate [239] (which is consistent with theory: one monovalent ion exchanged = one electron exchanged in an ideal situation). Leveraging the enhanced ion migration, Peng et al. explored the continuous production of 2-mercapto-5-methyl-1,3,4-thiadiazole (MMTD, intermediate of antibiotic) from MMTD wastewater (containing MTTD and $K_2SO_4$), observing an increase in the $OH^-$ concentration of the base compartment from 0.4 to 0.8 mol/L as the current density was raised from 30 to 70 mA cm$^{-2}$ [240]. Nevertheless, this elevation of current density was also associated with a noticeable increase in voltage drop, which in turn contributed to a higher electrical resistance and thus augmented energy SEC demands [241]. For instance, an investigation by Doornbusch et al. highlighted that the energy required for the seawater desalination surged from 0.2 to 0.9 kWh/m$^{-3}$(–|–) when the current density was increased from 2.5 to 7.5 mA cm$^{-2}$ [242], under similar
and inorganic deposits, pH fluctuations, and interactions with treated solution components [218], leading to their deterioration by several mechanisms.

### 3.6.3.1. Fouling.
This phenomenon involves the chemical interaction between the IEM and the components of the solution being treated, leading to compound deposition and membrane property deterioration. This degradation often manifests as reduced electrical conductivity (blockage of pores, ionic sites blocked by multivalent ions) or a decline in limiting current density [219], with models available to describe these effects based on the type of fouling film formed [220,221]. Fouling in IEMs can be categorized (Fig. 9) by its causes as: inorganic substance fouling (also known as scaling due to the formation of a 'scaly' precipitate) [222,223], fouling due to organic molecules and colloids (especially in the food industry) [224,225], and biofouling [226,227].

### 3.6.3.2. Chemical reactions and physical interactions.
IEM degradation encompasses not just fouling but also involves mechanisms unrelated to substance deposition. These include interactions with the physicochemical properties of the solution, such as high ionic strength and reactions between membrane components and solution ions, including $H^+$ and $OH^-$ ions. These reactions, catalyzed by the membrane's polar groups, extend beyond simple sediment formation, affecting the membrane significantly [217]. The concentration of $H^+$ and $OH^-$ ions in solutions can alter due to inherent solution properties or generated during ED, impacting IEMs in applications like alkaline fuel cells [229-232], where this issue is extensively discussed. The presence of these ions can lead to protonation/deprotonation reactions and irreversible reactions with membrane materials (such as the cation groups attack via Hofmann elimination, nucleophilic substitution to cleave the cationic group from the tethered bond to the polymer backbone, and nucleophilic substitution of dealkylation [233,234]), highlighting the complex influence of physicochemical interactions on membrane longevity and functionality. The degradation of the membrane is influenced by two additional parameters: the elevated ionic strength of the solution [235], and the stretching of the membrane matrix caused by ions with bulky hydration shells [236]. Hydration shell can increase the ions radius by a factor of 14 in the most severe cases, as illustrated in Fig. 10. This significant expansion results in increased mechanical stress on the membrane, further contributing to its degradation.

## 4. Operational parameters and their impacts on efficiency and selectivity

The performance of the ED process, along with its product quality and economic viability, is significantly influenced by the operating conditions. Specifically, the factors affecting these outcomes can be divided into four main categories: (i) intensity of the electric field; (ii) concentration of the initial solution; (iii) presence of co-ions; and (iv) rate of solution feed. The various operating parameters that can influence the performance of the reactor, as well as the phenomena impacted, are synthesized in Fig. 12, and studies focusing more specifically on each phenomenon are provided as examples. To facilitate a comprehensive

<figure><img src="image_12.png" /><figcaption>**Fig. 9.** Types of fouling. Reprinted from [228], Copyright (2016), with permission from Elsevier.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>**Fig. 10.** Relationship between the radius of ions and their hydration status. Ions of smaller dimensions exhibit a greater propensity for hydration, attributed to their higher charge density. Reprinted with permission from [237].</figcaption></figure>

understanding of ED and to foster enhancements in process efficiency, this section will present a detailed examination of each influencing factor and highlight the limiting factors of the process.

### 4.1. Electric field intensity

In ED, the primary force propelling ion migration is the electric field intensity as previously presented in Eq. (21). The influence of this electric field intensity on the process is examined through two key parameters: current density $j$ and cell voltage $U_{stack}$ which are intrinsically combined. In ED processes, the practice of maintaining a constant current is widespread. Here, the current density is the applied current divided by the effective surface area of the membranes in use. The same types of conclusions presented in this section are therefore identical concerning process behavior in response to cell voltage. The literature on this topic suggests that the impact on the process encompasses several key dimensions: (i) ion migration, (ii) concentration of reaction products, (iii) energy consumption / current efficiency.

An elevation in current density is associated with enhanced ion migration, which contributes to a reduction in the operational duration of the ED process for the same conversion time by increasing the flux across the membrane. For example, the increase in current density was observed to elevate the potassium average flux, illustrating the promotion of ion migration during separation between $K^+$ and $Mg^{2+}$ [238]. Furthermore, Liu et al. conducted research on the ED process for obtaining $Ni^{2+}$, $NH_4^+$, total phosphorus and $NO_3^-$ from electroless nickel-plating wastewater. Their findings revealed that an increase in current density (from 20 to 25 mA cm$^{-2}$) reduced the operation time from 12 to 10 h for the same removal rate [239] (which is consistent with theory: one monovalent ion exchanged = one electron exchanged in an ideal situation). Leveraging the enhanced ion migration, Peng et al. explored the continuous production of 2-mercapto-5-methyl-1,3,4-thiadiazole (MMTD, intermediate of antibiotic) from MMTD wastewater (containing MTTD and $K_2SO_4$), observing an increase in the $OH^-$ concentration of the base compartment from 0.4 to 0.8 mol/L as the current density was raised from 30 to 70 mA cm$^{-2}$ [240]. Nevertheless, this elevation of current density was also associated with a noticeable increase in voltage drop, which in turn contributed to a higher electrical resistance and thus augmented energy SEC demands [241]. For instance, an investigation by Doornbusch et al. highlighted that the energy required for the seawater desalination surged from 0.2 to 0.9 kWh/m$^{-3}$(–|–) when the current density was increased from 2.5 to 7.5 mA cm$^{-2}$ [242], under similar
<figure><img src="image_13.png" /><figcaption>**Fig. 11.** Summary of the different types of IEM selectivity organized according to a) selectivity according to charge, b) selectivity according to valence, c) selectivity according to specific ion, and selectivities subjected to IEMs according to d) electro-osmotic phenomena and e) diffusion phenomena (via osmosis or gas separation). Adapted with permission from [80] (CC-BY 4.0 Springer).</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>**Fig. 12.** Summary of the influence of each operating/limiting parameter on the ED process. Phenomena that are impacted are represented in italic and bold, operational parameters or limiting factors in italic, and the measurable parameter in plain text.</figcaption></figure>

flow conditions. Additionally, Ibáñez et al. demonstrated, during the extraction of sulfuric acid from copper metallurgical plant effluents using ED, a bifurcated pattern in the energy consumption related to

current density. Initially, at lower current densities, there is a sharp escalation in the SEC, culminating at $3.60 \text{ kWh kg}^{-1}$ of $\text{H}_2\text{SO}_4$ when the current density hits $30 \text{ mA cm}^{-2}$. Subsequently, the trend shifts towards
<figure><img src="image_14.png" /><figcaption>**Fig. 11.** Summary of the different types of IEM selectivity organized according to a) selectivity according to charge, b) selectivity according to valence, c) selectivity according to specific ion, and selectivities subjected to IEMs according to d) electro-osmotic phenomena and e) diffusion phenomena (via osmosis or gas separation). Adapted with permission from [80] (CC-BY 4.0 Springer).</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>**Fig. 12.** Summary of the influence of each operating/limiting parameter on the ED process. Phenomena that are impacted are represented in italic and bold, operational parameters or limiting factors in italic, and the measurable parameter in plain text.</figcaption></figure>

flow conditions. Additionally, Ibáñez et al. demonstrated, during the extraction of sulfuric acid from copper metallurgical plant effluents using ED, a bifurcated pattern in the energy consumption related to

current density. Initially, at lower current densities, there is a sharp escalation in the SEC, culminating at $3.60 \text{ kWh kg}^{-1}$ of $\text{H}_2\text{SO}_4$ when the current density hits $30 \text{ mA cm}^{-2}$. Subsequently, the trend shifts towards
a more gradual ascent in SEC as current density increases, with the SEC reaching approximately 4.11 kWh kg⁻¹ at a current density of 70 mA cm⁻². This represents an approximate growth of 12.5 % in the SEC upon augmenting the current density by a factor of 2.3 [243]. The relationship between current efficiency and current density was examined in a study of Xu et al. reported a decline in current efficiency from 5.56 % to 1.84 % as current density increased from 16.1 to 483 mA cm⁻² during the continuous production of LiBr from NH₃–H₂O–LiBr ternary solution in ammonia absorption refrigeration system [244].

## 4.2. Initial solution concentration

The initial concentrations of solutions also affect the final product concentrations (Eqs. (40) – (41)), the conductivity, current efficiency, and the conversion rates (Eqs. (42) – (43)) throughout the ED process which is very classic and unsurprising.

Batch reactor:

$$C_{f,i} = C_{ini,i} \pm \frac{\xi_i \times \int_0^{t_i} I \times dt}{z_i F} \quad (40)$$

Continuous reactor (1 pass):

$$C_{out,i} = C_{in,i} \pm \frac{\xi_i \times I \times N_{cp}}{\dot{q} z_i F} \quad (41)$$

where $\xi_i$ is the current efficiency (dimensionless) and $\dot{q}$ is the volumetric stack flow rate (m³/s).

Batch reactor:

$$\chi_i = \frac{C_{ini,i} - C_{f,i}}{C_{ini,i}} \quad (42)$$

Continuous reactor (1 pass):

$$\chi_i = \frac{C_{in,i} - C_{out,i}}{C_{in,i}} \quad (43)$$

where $\chi_i$ is the conversion rate of the ion i (dimensionless).

Typically, elevating the concentration of the initial feed solution results in a higher concentration of the final product. The study of Wang et al. involved synthesizing NaOH from NaCl by using BED to achieve a rigorous comparison of caustic soda production by electrolysis. They demonstrated that the concentration of NaOH increased with the feed solutions' concentration (from 12 to 26 % NaCl concentration) rising from 1.81 to 4.35 mol/L [245]. In a parallel investigation, Kirmizi et al. separated chromium(VI) and nickel(II) ions from effluent and noted a comparable trend [246]. Notably, in these instances, the enhanced initial concentration underscores the principle that an increase in the initial concentration of ions contributes to a more significant ion flux. However, it is crucial to establish an equilibrium between the enhancement of solute concentration and the decline in ion selectivity. As the initial solute concentration increases, the observed decrease in ion selectivity is often attributed to phenomena such as back diffusion [98] and the diminished charge-exclusion capacity of the membrane when exposed to elevated solute concentrations, a consequence of charge screening (observed in several cases such sulfate ions and lead from battery manufacturing effluents [247] or seawater desalination [248]). Moreover, the current efficiency is improved with the rise in concentrations of ions. This enhancement in current efficiency is often attributed to a reduction in the electrical resistance of the solutions (ηohm) [249].

## 4.3. Feeding rate

Numerous of studies claims that increasing the feeding rate leads to higher product concentration and lower energy usage in ED processes [246,250,251] because the ion flux is increasing. For example, Zhao

et al. observed an increase in the concentration of ions within the solutions of the product chamber, correlating with a rise in the feeding rate from 10 mL min⁻¹ to 20 mL min⁻¹, during the treatment of ammonia and saline wastewater [252].

Furthermore, adjusting the feeding rate can mitigate the risk of membrane fouling that arises from high product concentrations and solvent diffusion. It is worth noting that in an ED setup, the energy required for pumping a fluid rises with an increase in the Reynolds number (i.e., the feeding rate). Indeed, a balance must be found between the energy consumption associated with pumping and the improvement of performance in terms of mass transfer, which is often itself limited by the resistive transfer of ions across the membrane. It is indeed possible that the reduction in overpotential does not offset the increase in pumping costs.

## 4.4. Temperature

The influence of temperature on the ED system manifests many aspects on the ED process as illustrated in Fig. 13, but mainly through two mechanisms: (i) the dissociation of water in the case of BM, and (ii) the velocity at which ions traverse the membranes.

It has been observed that an increase in temperature accelerates the water dissociation kinetics within BMs [253,254]. In fact, part of the energy is supplied in the form of thermal energy, reducing the cell voltage required to carry out the same reaction. As with electrolysis, the thermoneutral voltage (Eth, in V) of the water dissociation reaction into H⁺ and OH⁻ ions decreases as the temperature increases, according to Eq. (44).

$$\Delta H = \Delta G + T\Delta S = -E_{th} \times n \times F \quad (44)$$

Where ΔG is the change in the Gibbs free energy (J), ΔH is change in enthalpy (J), T is temperature (K) and ΔS is the change in entropy (J/K).

The decrease in the DBL's thickness with temperature is not necessarily intuitive. In fact, by providing more energy to the molecules, their diffusion velocity (i.e., their diffusion coefficient) and therefore the diffusion gradient will increase. In addition, the decrease in viscosity with an increase in temperature will enable the liquid in the compartments to move at a higher flow rate for the same pumping pressure differential (or hydraulic power ph). Furthermore, an increase in the conductivity of IEM is observed with an increase in temperature [255,256]. This enhanced conductivity will allow for reduced energy losses for ion flow within the membrane.

Nonetheless, the operation at elevated temperatures is constrained by the thermal stability of commercial membranes, which cannot endure temperatures beyond 40–60 °C for extended periods for BMs [41], 60–80 °C for AEM [257] and up to 250 °C for some CEM [258], leading to a scarcity of research into BM performance at high temperatures. In terms of ion migration across membranes, a temperature rise from 24 °C to 41 °C has been shown to enhance ion migration rates, thereby increasing conductivity in the acid and alkali chambers. This enhancement potentially improves the current efficiency of the process [259] by increasing the diffusion and decreasing the viscosity of the solution at the same type.

## 4.5. Initial volume ratio

In the context of industrial applications, the implementation of multistage or batch operations in BED is commonly adopted to showcase scalability and minimize manufacturing costs [260]. Such operational modalities present challenges for replication in laboratory environments. Therefore, to simulate multistage and batch processes, adjustments in the volume ratios (or flow rate ratio in the case of continuous reactor) of the individual chambers can be employed as a practical approach. Adjusting the initial volume ratio of solutions is also a proven strategy for enhancing product concentration [261]. Note that there is
a more gradual ascent in SEC as current density increases, with the SEC reaching approximately 4.11 kWh kg⁻¹ at a current density of 70 mA cm⁻². This represents an approximate growth of 12.5 % in the SEC upon augmenting the current density by a factor of 2.3 [243]. The relationship between current efficiency and current density was examined in a study of Xu et al. reported a decline in current efficiency from 5.56 % to 1.84 % as current density increased from 16.1 to 483 mA cm⁻² during the continuous production of LiBr from NH₃–H₂O–LiBr ternary solution in ammonia absorption refrigeration system [244].

## 4.2. Initial solution concentration

The initial concentrations of solutions also affect the final product concentrations (Eqs. (40) – (41)), the conductivity, current efficiency, and the conversion rates (Eqs. (42) – (43)) throughout the ED process which is very classic and unsurprising.

Batch reactor:

$$C_{f,i} = C_{ini,i} \pm \frac{\xi_i \times \int_0^{t_i} I \times dt}{z_i F} \quad (40)$$

Continuous reactor (1 pass):

$$C_{out,i} = C_{in,i} \pm \frac{\xi_i \times I \times N_{cp}}{\dot{q} z_i F} \quad (41)$$

where $\xi_i$ is the current efficiency (dimensionless) and $\dot{q}$ is the volumetric stack flow rate (m³/s).

Batch reactor:

$$\chi_i = \frac{C_{ini,i} - C_{f,i}}{C_{ini,i}} \quad (42)$$

Continuous reactor (1 pass):

$$\chi_i = \frac{C_{in,i} - C_{out,i}}{C_{in,i}} \quad (43)$$

where $\chi_i$ is the conversion rate of the ion i (dimensionless).

Typically, elevating the concentration of the initial feed solution results in a higher concentration of the final product. The study of Wang et al. involved synthesizing NaOH from NaCl by using BED to achieve a rigorous comparison of caustic soda production by electrolysis. They demonstrated that the concentration of NaOH increased with the feed solutions' concentration (from 12 to 26 % NaCl concentration) rising from 1.81 to 4.35 mol/L [245]. In a parallel investigation, Kirmizi et al. separated chromium(VI) and nickel(II) ions from effluent and noted a comparable trend [246]. Notably, in these instances, the enhanced initial concentration underscores the principle that an increase in the initial concentration of ions contributes to a more significant ion flux. However, it is crucial to establish an equilibrium between the enhancement of solute concentration and the decline in ion selectivity. As the initial solute concentration increases, the observed decrease in ion selectivity is often attributed to phenomena such as back diffusion [98] and the diminished charge-exclusion capacity of the membrane when exposed to elevated solute concentrations, a consequence of charge screening (observed in several cases such sulfate ions and lead from battery manufacturing effluents [247] or seawater desalination [248]). Moreover, the current efficiency is improved with the rise in concentrations of ions. This enhancement in current efficiency is often attributed to a reduction in the electrical resistance of the solutions ($\eta_{ohm}$) [249].

## 4.3. Feeding rate

Numerous of studies claims that increasing the feeding rate leads to higher product concentration and lower energy usage in ED processes [246,250,251] because the ion flux is increasing. For example, Zhao

et al. observed an increase in the concentration of ions within the solutions of the product chamber, correlating with a rise in the feeding rate from 10 mL min⁻¹ to 20 mL min⁻¹, during the treatment of ammonia and saline wastewater [252].

Furthermore, adjusting the feeding rate can mitigate the risk of membrane fouling that arises from high product concentrations and solvent diffusion. It is worth noting that in an ED setup, the energy required for pumping a fluid rises with an increase in the Reynolds number (i.e., the feeding rate). Indeed, a balance must be found between the energy consumption associated with pumping and the improvement of performance in terms of mass transfer, which is often itself limited by the resistive transfer of ions across the membrane. It is indeed possible that the reduction in overpotential does not offset the increase in pumping costs.

## 4.4. Temperature

The influence of temperature on the ED system manifests many aspects on the ED process as illustrated in Fig. 13, but mainly through two mechanisms: (i) the dissociation of water in the case of BM, and (ii) the velocity at which ions traverse the membranes.

It has been observed that an increase in temperature accelerates the water dissociation kinetics within BMs [253,254]. In fact, part of the energy is supplied in the form of thermal energy, reducing the cell voltage required to carry out the same reaction. As with electrolysis, the thermoneutral voltage ($E_{th}$, in V) of the water dissociation reaction into $H^+$ and $OH^-$ ions decreases as the temperature increases, according to Eq. (44).

$$\Delta H = \Delta G + T\Delta S = -E_{th} \times n \times F \quad (44)$$

Where $\Delta G$ is the change in the Gibbs free energy (J), $\Delta H$ is change in enthalpy (J), T is temperature (K) and $\Delta S$ is the change in entropy (J/K).

The decrease in the DBL's thickness with temperature is not necessarily intuitive. In fact, by providing more energy to the molecules, their diffusion velocity (i.e., their diffusion coefficient) and therefore the diffusion gradient will increase. In addition, the decrease in viscosity with an increase in temperature will enable the liquid in the compartments to move at a higher flow rate for the same pumping pressure differential (or hydraulic power $p^h$). Furthermore, an increase in the conductivity of IEM is observed with an increase in temperature [255,256]. This enhanced conductivity will allow for reduced energy losses for ion flow within the membrane.

Nonetheless, the operation at elevated temperatures is constrained by the thermal stability of commercial membranes, which cannot endure temperatures beyond 40–60 °C for extended periods for BMs [41], 60–80 °C for AEM [257] and up to 250 °C for some CEM [258], leading to a scarcity of research into BM performance at high temperatures. In terms of ion migration across membranes, a temperature rise from 24 °C to 41 °C has been shown to enhance ion migration rates, thereby increasing conductivity in the acid and alkali chambers. This enhancement potentially improves the current efficiency of the process [259] by increasing the diffusion and decreasing the viscosity of the solution at the same type.

## 4.5. Initial volume ratio

In the context of industrial applications, the implementation of multistage or batch operations in BED is commonly adopted to showcase scalability and minimize manufacturing costs [260]. Such operational modalities present challenges for replication in laboratory environments. Therefore, to simulate multistage and batch processes, adjustments in the volume ratios (or flow rate ratio in the case of continuous reactor) of the individual chambers can be employed as a practical approach. Adjusting the initial volume ratio of solutions is also a proven strategy for enhancing product concentration [261]. Note that there is
<figure><img src="image_15.png" /><figcaption>Fig. 13. Effect of temperature on various zones of an ED process. Green effects are favored by an increase in temperature. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

also a concentration limit, as described in section 2.4.1 above. There is a concentration limit above which the backflow is of the same order of magnitude as the current-induced migration, and the efficiency decreases.

This parameter's impact can be divided based on modifications to the volume ratios within two-chamber or three-chamber setups.

In the two-chamber scenario, Dong et al. investigated how varying the volume ratios between the acid and alkali chambers affected product concentration during the production of hexacyanocobalic acid. They discovered that increasing the ratio (acid: base) from 1:2 to 2:1 led to an increase in the final concentration of acid from 0.11 to 0.2 mol/L [262]. The approach of modulating volume ratios in a three-chamber system mirrors that in a two-chamber setup. In a similar vein, Delgado et al. reported, using four-chamber setup during the recovery of acid mine drainage effluent, that enhancing the volume ratios of the diluate to concentrate chamber from 1 to 3 reduced SEC energy consumption from 35.01 to 27.44 kWh/m⁻³(-|-) [263].

## 4.6. Limiting factors

The summary of ED applications and their influencing factors highlights benefits in environmental protection and resource conversion. However, ED technology faces challenges, including impurities in the product and the production of low-concentration outputs that require further concentration. These issues are often linked to several limiting factors, such as (i) the co-existing ion competition, (ii) the leakage of co-ions, (iii) the solvent transport, and (iv) the back diffusion. Generally, co-existing ions and leakage of co-ions compromises product purity, whereas solvent transport and back diffusion contribute to the reduced concentration of the product and efficiency losses.

### 4.6.1. Co-existing ion competition

As a reminder, IEMs are polymeric films with charged ionic functional groups covalently bond to their backbone. These structures facilitate the movement of ions carrying charges opposite to those of the membrane's functional groups (referred to as counter-ions), while blocking the passage of ions sharing the same charge as the membrane's functional groups (called co-ions). Typologically, the terms co-ion and

counter-ion thus discriminate the membrane. Conversely, the co-existing ion is the ion that accompanies the ion of interest in its electro-neutrality. For example, in the case of NaCl salt, Na⁺ is the co-existing ion of Cl⁻. When using a CEM, Na⁺ is then a counter-ion, while Cl⁻ is a co-ion.

The term 'co-existing ions' typically denotes the migration of ions within the system that possess the same charge as the targeted ion or acting as counter ions. This phenomenon can be categorized into co-existing cations and anions. Such coexistence primarily affects aspects of (i) ionic strength, (ii) conductivity, and (iii) diffusion reducing the resistance of the overall process. It therefore has an impact on product concentration and energy consumption.

The interaction of co-existing ions through the membrane significantly influences the recovery and transformation of targeted ions, notably through the competitive migration of ions sharing the same charge [264]. For example, Ji et al. examined how co-existing ions impact the synthesis of lithium from salt-lake brine and seawater. They introduced a separation coefficient corresponding to the concentration ratio of Mg²⁺/Li⁺ at a given moment to the initial time of Mg²⁺/Li⁺ in desalting compartment. At the same time, they discovered that the increasing presence of K⁺ and Na⁺ adversely affected the Li⁺ concentration, reducing the separation coefficient of magnesium and lithium from 8.73 to 1.83 when the Na⁺/Li⁺ ratio increases from 1 to 5, and from 8.33 to 2.13 as the K⁺/Li⁺ ratio rises from 1 to 5 [265]. Notably, K⁺ exerted a more pronounced effect than Na⁺ in this ED process, a difference attributed to the ions' hydration radii ($r_{K⁺} < r_{Na⁺} < r_{Li⁺}$, i.e., Fig. 10). During the process, the energy usage is adversely influenced by the competitive migration of co-existing ions alongside the target ions, leading to increased energy consumption and diminished current efficiency. This outcome is attributed to the fact that co-existing ions lower the relative current carried by the target ions, thereby decreasing current efficiency and elevating energy costs. It should be noted that this aspect has also been studied using modeling approaches [101,266–268].

### 4.6.2. Co-ions leakage

The migration of co-ions across the membrane occurs in response to the reverse electric force illustrating the non-ideal behavior of the membrane (i.e., transport number not equal to unity). This force is a
<figure><img src="image_16.png" /><figcaption>Fig. 13. Effect of temperature on various zones of an ED process. Green effects are favored by an increase in temperature. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

also a concentration limit, as described in section 2.4.1 above. There is a concentration limit above which the backflow is of the same order of magnitude as the current-induced migration, and the efficiency decreases.

This parameter's impact can be divided based on modifications to the volume ratios within two-chamber or three-chamber setups.

In the two-chamber scenario, Dong et al. investigated how varying the volume ratios between the acid and alkali chambers affected product concentration during the production of hexacyanocobalic acid. They discovered that increasing the ratio (acid: base) from 1:2 to 2:1 led to an increase in the final concentration of acid from 0.11 to 0.2 mol/L [262]. The approach of modulating volume ratios in a three-chamber system mirrors that in a two-chamber setup. In a similar vein, Delgado et al. reported, using four-chamber setup during the recovery of acid mine drainage effluent, that enhancing the volume ratios of the diluate to concentrate chamber from 1 to 3 reduced SEC energy consumption from 35.01 to 27.44 kWh/m⁻³(-|-) [263].

## 4.6. Limiting factors

The summary of ED applications and their influencing factors highlights benefits in environmental protection and resource conversion. However, ED technology faces challenges, including impurities in the product and the production of low-concentration outputs that require further concentration. These issues are often linked to several limiting factors, such as (i) the co-existing ion competition, (ii) the leakage of co-ions, (iii) the solvent transport, and (iv) the back diffusion. Generally, co-existing ions and leakage of co-ions compromises product purity, whereas solvent transport and back diffusion contribute to the reduced concentration of the product and efficiency losses.

### 4.6.1. Co-existing ion competition

As a reminder, IEMs are polymeric films with charged ionic functional groups covalently bond to their backbone. These structures facilitate the movement of ions carrying charges opposite to those of the membrane's functional groups (referred to as counter-ions), while blocking the passage of ions sharing the same charge as the membrane's functional groups (called co-ions). Typologically, the terms co-ion and

counter-ion thus discriminate the membrane. Conversely, the co-existing ion is the ion that accompanies the ion of interest in its electro-neutrality. For example, in the case of NaCl salt, Na⁺ is the co-existing ion of Cl⁻. When using a CEM, Na⁺ is then a counter-ion, while Cl⁻ is a co-ion.

The term 'co-existing ions' typically denotes the migration of ions within the system that possess the same charge as the targeted ion or acting as counter ions. This phenomenon can be categorized into co-existing cations and anions. Such coexistence primarily affects aspects of (i) ionic strength, (ii) conductivity, and (iii) diffusion reducing the resistance of the overall process. It therefore has an impact on product concentration and energy consumption.

The interaction of co-existing ions through the membrane significantly influences the recovery and transformation of targeted ions, notably through the competitive migration of ions sharing the same charge [264]. For example, Ji et al. examined how co-existing ions impact the synthesis of lithium from salt-lake brine and seawater. They introduced a separation coefficient corresponding to the concentration ratio of Mg²⁺/Li⁺ at a given moment to the initial time of Mg²⁺/Li⁺ in desalting compartment. At the same time, they discovered that the increasing presence of K⁺ and Na⁺ adversely affected the Li⁺ concentration, reducing the separation coefficient of magnesium and lithium from 8.73 to 1.83 when the Na⁺/Li⁺ ratio increases from 1 to 5, and from 8.33 to 2.13 as the K⁺/Li⁺ ratio rises from 1 to 5 [265]. Notably, K⁺ exerted a more pronounced effect than Na⁺ in this ED process, a difference attributed to the ions' hydration radii ($r_{K⁺} < r_{Na⁺} < r_{Li⁺}$, i.e., Fig. 10). During the process, the energy usage is adversely influenced by the competitive migration of co-existing ions alongside the target ions, leading to increased energy consumption and diminished current efficiency. This outcome is attributed to the fact that co-existing ions lower the relative current carried by the target ions, thereby decreasing current efficiency and elevating energy costs. It should be noted that this aspect has also been studied using modeling approaches [101,266–268].

### 4.6.2. Co-ions leakage

The migration of co-ions across the membrane occurs in response to the reverse electric force illustrating the non-ideal behavior of the membrane (i.e., transport number not equal to unity). This force is a
consequence of minor differences in the arrangement of charged groups within the membrane structure leading to transport number different from the unity. The primary impact of co-ions leakage includes (i) a reduction in the product's purity and (ii) a decrease in current efficiency [269].

Typically, the migration of co-ions leads to a diminished concentration of the desired product. For example, Yan et al. reported the detection of H⁺ in the dilute solution, indicating the passage of proton through the AEM, which resulted in reduced product purity [270]. This issue is often linked to the imperfect selectivity of ion exchange membranes, allowing for co-ion leakage [271]. Cherif et al. observed the leakage of NO₃⁻ through the CEM to another chamber in the process of recovering HNO₃ and NaOH from NaNO₃ using BED, thereby compromising the purity of NaOH [272].

### 4.6.3. Solvent transport

An additional constraint within ED technology is the movement of solvent from the dilute chamber to the concentrate chamber ($J_w$, often water as in Eq. (45)). This solvent transfer usually results in an increased volume of the product solution, which can lead to a diluting effect, thereby limiting the attainable maximum concentration of the product, which have been studied experimentally and by modeling [273–275]. More precisely, solvent movement encompasses two distinct categories: (i) electro-osmotic of water $J^{eo}$ (Fig. 11 – d, Eq. (46), mol m⁻² s⁻¹) [276,277] and (ii) osmotic water $J^{os}$ (Fig. 11 – e, Eq. (47), mol m⁻² s⁻¹) [81,278]. Electroosmosis involves the movement of water as it follows hydrated ions, consistently proceeding in the same direction as the ions themselves. On the other hand, osmosis results from the gradient of chemical potential that arises due to variations in salinity between adjacent compartments. In the initial stages of the process, water osmosis takes place in the direction opposite to ion migration, this reversal is due to the greater salinity (i.e., lower water activity) present in the dilute compartment compared to the concentrate compartment [277,279].

$$J_w = J^{eo} + J^{os} \quad (45)$$

$$J^{eo} = n_{drag} * \frac{J}{F} * n_{cell} \quad (46)$$

$$J^{os} = L_p \times (\Delta\pi - \Delta p^h) \quad (47)$$

$$\pi = RT \times \sum_{i} C_{i} \quad (48)$$

where $L_p$ is the water permeability of the IEM (mol m²/s bar⁻¹); $n_{drag}$ is the electroosmotic drag coefficient (dimensionless); $\Delta\pi$ is the osmotic pressure difference between both chambers (Pa); $\Delta p^h$ is the hydraulic pressure difference between each interface of the IEM (Pa).

Concerning water electro-migration, Jaroszek et al. identified a direct linear correlation between the volume of water transported and the ion electromigration flux (K⁺, Na⁺, Cl⁻, SO₄²⁻) as the concentrations of KNO₃ and Na₂SO₄ solutions were increased (mainly attributed to the relation between concentration and hydration number) [280]. The linear regression analysis performed on the collected experimental data yielded average rates of water transport of (2.04 ± 0.01) × 10⁻² mol (A h)⁻¹ and (3.73 ± 0.03) × 10⁻¹ mol (A h)⁻¹ for the KNO₃ and Na₂SO₄ streams, respectively. The quantity of water molecules moved per mole of salt generated in the product stream was found to be 10.5 ± 0.8 (mol mol⁻¹) for KNO₃ and 16.9 ± 1.5 (mol mol⁻¹) for Na₂SO₄. In a similar vein, Zabolotskii et al. discovered a linear linkage between the rate of apparent water flux and the current density, evidenced by a high correlation coefficient ($J_w = 0.013 \times j + 0.206$ with R² ≈ 0.98) during lithium chloride concentration [281].

Conversely, osmotic water movement is triggered by the osmotic pressure difference between neighboring chambers, a result of the

variability in salinity levels between their solutions. At the outset of the ED process, osmotic water flux moves in the direction opposite to that of ion migration, attributed to the higher salinity of the feed solution [282]. When the concentration in the product solution surpasses that of the feed solution, the osmotic flux direction shifts to coincide with that of the electro-migration flux, which results in the dilution of the product solution. Unlike the well-studied electro-migration of water through membranes via hydrated ions, osmotic water flux has received less attention. Wang et al. highlighted the effect of osmotic pressure on the limiting of NaOH concentration during BED. Findings indicate a reduction in current efficiency from 90 % to 73 % and a decrease in the concentration of the base to a certain degree upon enhancing the osmotic pressure [245].

### 4.6.4. Back diffusion

Even if diffusion is not the main transfer phenomena compared to electromigration, back diffusion poses a considerable challenge to reaching high concentrations and purity of the product in ED processes [278], as presented in Eq. (49). This phenomenon tends to manifest more prominently when there is a significant concentration disparity between the concentrate and dilute streams.

$$J_{\text{back, i}}^{\text{m}} = \frac{D_i}{\delta} \times (C_i^{\text{ete}} - C_i^{\text{dlt}}) \quad (49)$$

where $J_{\text{back, i}}^{\text{m}}$ is the surface molar flux caused by the back diffusion of the ion i (mol m⁻² s⁻¹) and $\delta$ is the diffusion boundary layer thickness (m).

As highlighted by Yu et al., the phenomenon of back diffusion, driven by concentration gradients and a competition between migration and diffusion, can limit the current efficiency significantly (~40 % in their case) [283]. Chen et al. further observed that back diffusion is particularly raised by an increase of temperature [271] (as already discussed in section 4.4). To counteract the effects of back diffusion and enhance the final product's concentration during the ED process, a continuous operation mode is advised.

## 5. Process intensification and system optimization

Baldea presents in 2015 a definition of process intensification as ‘any chemical engineering development that leads to substantially smaller, cleaner, safer and more energy efficient technology or that combine[s] multiple operations into fewer devices (or a single apparatus)’ [284]. ED, like any other unit operation in chemical engineering, is not immune to innovations in its field. Thus, the various advances in terms of process intensification in ED can be structured with advances in terms of (i) cell design, (ii) modeling and (iii) operating modes.

### 5.1. Cell design and mixing promotion

The majority of electromembrane processes utilize plate-and-frame configurations (e.g., filter press), where channel—supplied by a manifold and flow distributor—are formed by a pair of membranes serving as the channel walls, with an internal spacer maintaining the separation and enhancing convection within these channels. A balance must be struck between minimizing pressure losses, ensuring proper mixing of the solutions, and decreasing the distance between the membranes to reduce ohmic overvoltage. Consequently, choosing the right spacer geometry and material is critical in the design and optimization of these processes. This significance has led to thorough studies on the hydro-dynamics and mass transport phenomena within the channels of membrane modules, providing detailed insights into their operational dynamics [285–287]. Recent advancements in ED technology have led to significant improvements in cell design, focusing on enhancing efficiency, and scalability (the latter being the subject of particular attention in the Section 6). These advances stand out in terms of (i) mixing promotion and (ii) process simulation.
consequence of minor differences in the arrangement of charged groups within the membrane structure leading to transport number different from the unity. The primary impact of co-ions leakage includes (i) a reduction in the product's purity and (ii) a decrease in current efficiency [269].

Typically, the migration of co-ions leads to a diminished concentration of the desired product. For example, Yan et al. reported the detection of H⁺ in the dilute solution, indicating the passage of proton through the AEM, which resulted in reduced product purity [270]. This issue is often linked to the imperfect selectivity of ion exchange membranes, allowing for co-ion leakage [271]. Cherif et al. observed the leakage of NO₃⁻ through the CEM to another chamber in the process of recovering HNO₃ and NaOH from NaNO₃ using BED, thereby compromising the purity of NaOH [272].

### 4.6.3. Solvent transport

An additional constraint within ED technology is the movement of solvent from the dilute chamber to the concentrate chamber ($J_w$, often water as in Eq. (45)). This solvent transfer usually results in an increased volume of the product solution, which can lead to a diluting effect, thereby limiting the attainable maximum concentration of the product, which have been studied experimentally and by modeling [273–275]. More precisely, solvent movement encompasses two distinct categories: (i) electro-osmotic of water $J^{eo}$ (Fig. 11 – d, Eq. (46), mol m⁻² s⁻¹) [276,277] and (ii) osmotic water $J^{os}$ (Fig. 11 – e, Eq. (47), mol m⁻² s⁻¹) [81,278]. Electroosmosis involves the movement of water as it follows hydrated ions, consistently proceeding in the same direction as the ions themselves. On the other hand, osmosis results from the gradient of chemical potential that arises due to variations in salinity between adjacent compartments. In the initial stages of the process, water osmosis takes place in the direction opposite to ion migration, this reversal is due to the greater salinity (i.e., lower water activity) present in the dilute compartment compared to the concentrate compartment [277,279].

$$J_w = J^{eo} + J^{os} \qquad (45)$$

$$J^{eo} = n_{drag} * \frac{J}{F} * N_{cell} \qquad (46)$$

$$J^{os} = L_p \times (\Delta\pi - \Delta p^h) \qquad (47)$$

$$\pi = RT \times \sum_{i} C_{i} \qquad (48)$$

where $L_p$ is the water permeability of the IEM (mol m²/s bar⁻¹); $n_{drag}$ is the electroosmotic drag coefficient (dimensionless); $\Delta\pi$ is the osmotic pressure difference between both chambers (Pa); $\Delta p^h$ is the hydraulic pressure difference between each interface of the IEM (Pa).

Concerning water electro-migration, Jaroszek et al. identified a direct linear correlation between the volume of water transported and the ion electromigration flux (K⁺, Na⁺, Cl⁻, SO₄²⁻) as the concentrations of KNO₃ and Na₂SO₄ solutions were increased (mainly attributed to the relation between concentration and hydration number) [280]. The linear regression analysis performed on the collected experimental data yielded average rates of water transport of (2.04 ± 0.01) × 10⁻² mol (A h)⁻¹ and (3.73 ± 0.03) × 10⁻¹ mol (A h)⁻¹ for the KNO₃ and Na₂SO₄ streams, respectively. The quantity of water molecules moved per mole of salt generated in the product stream was found to be 10.5 ± 0.8 (mol mol⁻¹) for KNO₃ and 16.9 ± 1.5 (mol mol⁻¹) for Na₂SO₄. In a similar vein, Zabolotskii et al. discovered a linear linkage between the rate of apparent water flux and the current density, evidenced by a high correlation coefficient ($J_w = 0.013 \times j + 0.206$ with R² ≈ 0.98) during lithium chloride concentration [281].

Conversely, osmotic water movement is triggered by the osmotic pressure difference between neighboring chambers, a result of the

variability in salinity levels between their solutions. At the outset of the ED process, osmotic water flux moves in the direction opposite to that of ion migration, attributed to the higher salinity of the feed solution [282]. When the concentration in the product solution surpasses that of the feed solution, the osmotic flux direction shifts to coincide with that of the electro-migration flux, which results in the dilution of the product solution. Unlike the well-studied electro-migration of water through membranes via hydrated ions, osmotic water flux has received less attention. Wang et al. highlighted the effect of osmotic pressure on the limiting of NaOH concentration during BED. Findings indicate a reduction in current efficiency from 90 % to 73 % and a decrease in the concentration of the base to a certain degree upon enhancing the osmotic pressure [245].

### 4.6.4. Back diffusion

Even if diffusion is not the main transfer phenomena compared to electromigration, back diffusion poses a considerable challenge to reaching high concentrations and purity of the product in ED processes [278], as presented in Eq. (49). This phenomenon tends to manifest more prominently when there is a significant concentration disparity between the concentrate and dilute streams.

$$J_{\text{back, i}}^{\text{m}} = \frac{D_i}{\delta} \times (C_i^{\text{cte}} - C_i^{\text{dlt}}) \qquad (49)$$

where $J_{\text{back, i}}^{\text{m}}$ is the surface molar flux caused by the back diffusion of the ion i (mol m⁻² s⁻¹) and $\delta$ is the diffusion boundary layer thickness (m).

As highlighted by Yu et al., the phenomenon of back diffusion, driven by concentration gradients and a competition between migration and diffusion, can limit the current efficiency significantly (~40 % in their case) [283]. Chen et al. further observed that back diffusion is particularly raised by an increase of temperature [271] (as already discussed in section 4.4). To counteract the effects of back diffusion and enhance the final product's concentration during the ED process, a continuous operation mode is advised.

## 5. Process intensification and system optimization

Baldea presents in 2015 a definition of process intensification as ‘any chemical engineering development that leads to substantially smaller, cleaner, safer and more energy efficient technology or that combine[s] multiple operations into fewer devices (or a single apparatus)’ [284]. ED, like any other unit operation in chemical engineering, is not immune to innovations in its field. Thus, the various advances in terms of process intensification in ED can be structured with advances in terms of (i) cell design, (ii) modeling and (iii) operating modes.

### 5.1. Cell design and mixing promotion

The majority of electromembrane processes utilize plate-and-frame configurations (e.g., filter press), where channel—supplied by a manifold and flow distributor—are formed by a pair of membranes serving as the channel walls, with an internal spacer maintaining the separation and enhancing convection within these channels. A balance must be struck between minimizing pressure losses, ensuring proper mixing of the solutions, and decreasing the distance between the membranes to reduce ohmic overvoltage. Consequently, choosing the right spacer geometry and material is critical in the design and optimization of these processes. This significance has led to thorough studies on the hydro-dynamics and mass transport phenomena within the channels of membrane modules, providing detailed insights into their operational dynamics [285–287]. Recent advancements in ED technology have led to significant improvements in cell design, focusing on enhancing efficiency, and scalability (the latter being the subject of particular attention in the Section 6). These advances stand out in terms of (i) mixing promotion and (ii) process simulation.
Two primary configurations for electrolyte channels have been identified: sheet flow and tortuous path configurations. The sheet flow design, illustrated in Fig. 14 – a, features feed channels of a rectangular, through which the solution traverses in a substantially linear regime. Conversely, the tortuous path configuration, depicted in Fig. 14 – b, is characterized by feed channels that follow a narrow, winding route, incorporating multiple baffles and 180°-turns. The flow configuration can be organized into parallel, counter, or crossflow patterns. Counter-flow arrangements are generally favored in theory due to their advantage of avoiding the significant axial escalation in concentration difference that is characteristic of parallel flows. However, this configuration introduces greater pressure disparities across the concentrate and dilute sections, potentially leading to internal leaks and undue deformation of the membranes [288].

### 5.1.1. Non-conductive spacers

Initial research into ED applications has established that spacers vary significantly in their impact on mass transfer and pressure drop. This variation underscores the critical importance of optimizing channel design to enhance overall system performance. These spacers not only maintain a constant distance (usually varies between 0.3 and 2 mm [288]) between the membranes but also enhance mixing within the channels as illustrated in Fig. 15. To ensure the integrity of the channels, net spacers are equipped with gaskets around their edges, effectively sealing the channels and directing the flow of solutions through them [289]. Additionally, the incorporation of holes in spacers forms distinct conduits for the dual hydraulic circuits, serving as distribution and collection manifolds for the solutions entering and exiting the channels [25,290]. The assembly of the ED apparatus is completed by securing it with end plates and applying pressure using bolts and nuts. The design of spacers, crucial to ensure fluid flow and minimize concentration polarization, has evolved. Net spacers are categorized into four principal types: overlapped, woven, twisted, and multi-layer. Innovations include turbulence-promoting spacers and 3D printed spacers with optimized geometries that enhance mass transfer and reduce pressure drops, thereby improving energy efficiency by extensive numerical and experimental studies [291–294].

### 5.1.2. Ionic conductive spacers

A notable drawback of using net polymeric spacers in ED is their fabrication from non-conductive materials, which elevates the electrical resistance within the compartments by reducing area of the compartment (mainly through η<sub>ohm</sub>). Alternatively, opting for spacers made from conductive materials presents a viable strategy to decrease energy

<figure><img src="image_16.png" /><figcaption>Fig. 14. (a) Sheet flow vs. (b) tortuous path flow spacers. Reprinted with permission from [54] (CC-BY 4.0 Elsevier).</figcaption></figure>

consumption and thereby enhance the operational efficiency of ED units. In the 1970s, Kedem introduced spacers fabricated from ion exchange materials [296,297]. These benefits included enhanced mass transfer, characterized by diminished polarization (decrease in ohmic resistance attributable to the absence of shadow effect) and an augmented limit current, owing to the expanded active surface area [298,299]. Over two decades later, the industry adopted conductive spacers, either through chemical modification or application of coatings, which reaffirmed the earlier findings [300,301]. Despite the highlighted benefits, conductive spacers have not been adopted in actual industrial ED units, likely due to their higher production costs, reduced durability, and increased complexity.

### 5.1.3. Profiled membranes

It is also possible to construct ED stacks without the need for spacers by employing membranes with specific profiles. In more recent developments, profiled membranes, as illustrated in Fig. 16, have been introduced as a cost-effective and efficient substitute for conductive spacers. These membranes are designed with embossed features such as reliefs, pillars, or ridges on one or both sides, serving the function of spacers [302,303]. The integration of profiled membranes simplifies the assembly process of ED stacks by eliminating the need for separate spacers [304,305], while also offering benefits, illustrated Fig. 16 – e, akin to those of conductive spacers, including reduced ohmic resistance and the potential for increased active surface area [306]. These features affect the fluid mixing and then the mass transfer efficiency. It can vary depending on the specific profile design. The geometry of the profiles also significantly affects pressure drops [307].

Figures (a-b) reprinted from [308], Copyright (2014), with permission from Elsevier. Figures (c-d) reprinted from [307], Copyright (2017), with permission from Elsevier. Figure (e) reprinted from [305], Copyright (2010), with permission from Elsevier.

Although channel characteristics probably play an important role in controlling the performance of ED processes, attention also needs to be paid to hydrodynamic aspects concerning input-output distribution systems, commonly known as manifolds, and much work has been done to improve their performance in terms of flow distribution [294,308–311], with benefit from electrolysers or redox flow batteries [312–314].

## 5.2. Modeling and simulation techniques

A comprehensive understanding and subsequent simulation of the ED process necessitates the incorporation of sophisticated mathematical models. These models are essential for encapsulating various intricate phenomena, encompassing the equilibria between solutions and membranes, the effects of concentration polarization, the dynamics of fluid movement along the channels, the principles governing mass transport and the mass balance within the compartments, alongside electrical interactions among others. The literature to date has introduced a variety of modeling strategies, each uniquely tackling these elements to varying degrees. The primary objective behind these endeavors has typically been to forge potent tools for the design and optimization of ED operations.

The task of modeling ED, along with other electromembrane processes, presents a significant challenge due to the diverse array of phenomena occurring simultaneously across macroscopic, microscopic, and nanoscopic scales, which collectively influence the process's overall efficacy. A broad spectrum of ED modeling approaches is documented within the academic corpus, where the common goal across these methodologies is to predict output metrics such as outlet concentration (or concentration profiles) and current density from controlled input parameters like inlet concentration and voltage application. These models facilitate the derivation of crucial operational metrics including current efficiency and energy consumption SEC. Broadly, ED models fall
Two primary configurations for electrolyte channels have been identified: sheet flow and tortuous path configurations. The sheet flow design, illustrated in Fig. 14 – a, features feed channels of a rectangular, through which the solution traverses in a substantially linear regime. Conversely, the tortuous path configuration, depicted in Fig. 14 – b, is characterized by feed channels that follow a narrow, winding route, incorporating multiple baffles and 180°-turns. The flow configuration can be organized into parallel, counter, or crossflow patterns. Counterflow arrangements are generally favored in theory due to their advantage of avoiding the significant axial escalation in concentration difference that is characteristic of parallel flows. However, this configuration introduces greater pressure disparities across the concentrate and dilute sections, potentially leading to internal leaks and undue deformation of the membranes [288].

### 5.1.1. Non-conductive spacers

Initial research into ED applications has established that spacers vary significantly in their impact on mass transfer and pressure drop. This variation underscores the critical importance of optimizing channel design to enhance overall system performance. These spacers not only maintain a constant distance (usually varies between 0.3 and 2 mm [288]) between the membranes but also enhance mixing within the channels as illustrated in Fig. 15. To ensure the integrity of the channels, net spacers are equipped with gaskets around their edges, effectively sealing the channels and directing the flow of solutions through them [289]. Additionally, the incorporation of holes in spacers forms distinct conduits for the dual hydraulic circuits, serving as distribution and collection manifolds for the solutions entering and exiting the channels [25,290]. The assembly of the ED apparatus is completed by securing it with end plates and applying pressure using bolts and nuts. The design of spacers, crucial to ensure fluid flow and minimize concentration polarization, has evolved. Net spacers are categorized into four principal types: overlapped, woven, twisted, and multi-layer. Innovations include turbulence-promoting spacers and 3D printed spacers with optimized geometries that enhance mass transfer and reduce pressure drops, thereby improving energy efficiency by extensive numerical and experimental studies [291–294].

### 5.1.2. Ionic conductive spacers

A notable drawback of using net polymeric spacers in ED is their fabrication from non-conductive materials, which elevates the electrical resistance within the compartments by reducing area of the compartment (mainly through η<sub>ohm</sub>). Alternatively, opting for spacers made from conductive materials presents a viable strategy to decrease energy

consumption and thereby enhance the operational efficiency of ED units. In the 1970s, Kedem introduced spacers fabricated from ion exchange materials [296,297]. These benefits included enhanced mass transfer, characterized by diminished polarization (decrease in ohmic resistance attributable to the absence of shadow effect) and an augmented limit current, owing to the expanded active surface area [298,299]. Over two decades later, the industry adopted conductive spacers, either through chemical modification or application of coatings, which reaffirmed the earlier findings [300,301]. Despite the highlighted benefits, conductive spacers have not been adopted in actual industrial ED units, likely due to their higher production costs, reduced durability, and increased complexity.

### 5.1.3. Profiled membranes

It is also possible to construct ED stacks without the need for spacers by employing membranes with specific profiles. In more recent developments, profiled membranes, as illustrated in Fig. 16, have been introduced as a cost-effective and efficient substitute for conductive spacers. These membranes are designed with embossed features such as reliefs, pillars, or ridges on one or both sides, serving the function of spacers [302,303]. The integration of profiled membranes simplifies the assembly process of ED stacks by eliminating the need for separate spacers [304,305], while also offering benefits, illustrated Fig. 16 – e, akin to those of conductive spacers, including reduced ohmic resistance and the potential for increased active surface area [306]. These features affect the fluid mixing and then the mass transfer efficiency. It can vary depending on the specific profile design. The geometry of the profiles also significantly affects pressure drops [307].

**Figures (a-b) reprinted from** [308], **Copyright (2014)**, **with permission from Elsevier**. **Figures (c-d) reprinted from** [307], **Copyright (2017)**, **with permission from Elsevier**. **Figure (e) reprinted from** [305], **Copyright (2010)**, **with permission from Elsevier**.

Although channel characteristics probably play an important role in controlling the performance of ED processes, attention also needs to be paid to hydrodynamic aspects concerning input-output distribution systems, commonly known as manifolds, and much work has been done to improve their performance in terms of flow distribution [294,308–311], with benefit from electrolysers or redox flow batteries [312–314].

## 5.2. Modeling and simulation techniques

A comprehensive understanding and subsequent simulation of the ED process necessitates the incorporation of sophisticated mathematical models. These models are essential for encapsulating various intricate phenomena, encompassing the equilibria between solutions and membranes, the effects of concentration polarization, the dynamics of fluid movement along the channels, the principles governing mass transport and the mass balance within the compartments, alongside electrical interactions among others. The literature to date has introduced a variety of modeling strategies, each uniquely tackling these elements to varying degrees. The primary objective behind these endeavors has typically been to forge potent tools for the design and optimization of ED operations.

The task of modeling ED, along with other electromembrane processes, presents a significant challenge due to the diverse array of phenomena occurring simultaneously across macroscopic, microscopic, and nanoscopic scales, which collectively influence the process's overall efficacy. A broad spectrum of ED modeling approaches is documented within the academic corpus, where the common goal across these methodologies is to predict output metrics such as outlet concentration (or concentration profiles) and current density from controlled input parameters like inlet concentration and voltage application. These models facilitate the derivation of crucial operational metrics including current efficiency and energy consumption SEC. Broadly, ED models fall

<figure><img src="image_17.png" /><figcaption>**Fig. 14.** (a) Sheet flow vs. (b) tortuous path flow spacers. Reprinted with permission from [54] (CC-BY 4.0 Elsevier).</figcaption></figure>
<figure><img src="image_17.png" /><figcaption>Fig. 15. Various types of spacers utilized in ED. Reprinted from [295], Copyright (2019), with permission from Elsevier.</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>Fig. 16. Surface morphology of (a) ridges, (b) waves, (c) pillars and (d) chevrons profiled membrane. (e) Results obtained with profiled and flat sheet membranes showing the current density as a function of the voltage applied in a test cell with one cell pair and feed solutions of 350 µS cm⁻¹ tap water.</figcaption></figure>

into three distinct classifications as depicted in Fig. 17.

The first category of process models employs an equivalent circuit model, a streamlined representation of complex electrical systems, utilizing basic elements like resistors (representing resistive losses, typically ohmic ones), capacitors (standing for capacitive behavior), and voltage sources (used for EC potential difference) to emulate system behavior. By abstracting a system’s electrical characteristics into a simplified circuit form, equivalent circuit models facilitate the prediction of system responses to electrical stimuli and the elucidation of internal dynamics. This approach often utilizes equations with lumped parameters. For instance, in electrochemical engineering, these models are key for examining cells and batteries, representing phenomena like ionic transport and charge storage through simple components, thereby aiding in performance assessment and lifespan prediction. Note that these models are empirical and do not directly describe the physics of the phenomena. It is therefore not possible to transpose them from one ED process to another.

The broader second category of process models is segmented into two distinct sub-categories which offers two-dimensional resolutions: (i) detailed models based on the N-P (i.e., Eq. (21)) or M-S (i.e., Eq. (24)) equations [315,316] and (ii) semi-empirical models [317,318]. The primary distinction lies in how they mathematically characterize phenomena occurring across the membrane. Theoretical models offer a

comprehensive mathematical framework capable of describing transport phenomena within the membrane at a microscopic level with a high degree of precision, although reliance on empirical data for certain membrane properties such as ion diffusivity, mobility, and fixed charge density is still necessary. These models often utilize Finite Element Methods or similar computational techniques, integrating the model closely with thermodynamic and mass transfer principles, including fluid dynamics [319]. However, the extensive computational resources required for these models restrict their use to simplified channel geometries or small computational domains, rendering them less practical for simulations encompassing entire ED stacks. Conversely, semi-empirical models adopt a multi-scale approach that blends empirical data and small-scale theoretical analyses, such as computational fluid dynamics (CFD), to describe lower-scale phenomena like mass transfer and fluid flow behavior, which in turn helps in understanding mass transport phenomena in the DBL [320,321].

### 5.2.1. Equivalent circuit models

Studies on equivalent circuit models draw an analogy between ionic transport perpendicular to the direction of flow in ED cells and electron movement in a direct current electrical circuit, where solutions and membranes function as resistive components. These models rely on fundamental theoretical principles such as Ohm’s and Faraday’s laws to
<figure><img src="image_18.png" /><figcaption>Fig. 15. Various types of spacers utilized in ED. Reprinted from [295], Copyright (2019), with permission from Elsevier.</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>Fig. 16. Surface morphology of (a) ridges, (b) waves, (c) pillars and (d) chevrons profiled membrane. (e) Results obtained with profiled and flat sheet membranes showing the current density as a function of the voltage applied in a test cell with one cell pair and feed solutions of 350 µS cm⁻¹ tap water.</figcaption></figure>

into three distinct classifications as depicted in Fig. 17.

The first category of process models employs an equivalent circuit model, a streamlined representation of complex electrical systems, utilizing basic elements like resistors (representing resistive losses, typically ohmic ones), capacitors (standing for capacitive behavior), and voltage sources (used for EC potential difference) to emulate system behavior. By abstracting a system's electrical characteristics into a simplified circuit form, equivalent circuit models facilitate the prediction of system responses to electrical stimuli and the elucidation of internal dynamics. This approach often utilizes equations with lumped parameters. For instance, in electrochemical engineering, these models are key for examining cells and batteries, representing phenomena like ionic transport and charge storage through simple components, thereby aiding in performance assessment and lifespan prediction. Note that these models are empirical and do not directly describe the physics of the phenomena. It is therefore not possible to transpose them from one ED process to another.

The broader second category of process models is segmented into two distinct sub-categories which offers two-dimensional resolutions: (i) detailed models based on the N-P (i.e., Eq. (21)) or M-S (i.e., Eq. (24)) equations [315,316] and (ii) semi-empirical models [317,318]. The primary distinction lies in how they mathematically characterize phenomena occurring across the membrane. Theoretical models offer a

comprehensive mathematical framework capable of describing transport phenomena within the membrane at a microscopic level with a high degree of precision, although reliance on empirical data for certain membrane properties such as ion diffusivity, mobility, and fixed charge density is still necessary. These models often utilize Finite Element Methods or similar computational techniques, integrating the model closely with thermodynamic and mass transfer principles, including fluid dynamics [319]. However, the extensive computational resources required for these models restrict their use to simplified channel geometries or small computational domains, rendering them less practical for simulations encompassing entire ED stacks. Conversely, semi-empirical models adopt a multi-scale approach that blends empirical data and small-scale theoretical analyses, such as computational fluid dynamics (CFD), to describe lower-scale phenomena like mass transfer and fluid flow behavior, which in turn helps in understanding mass transport phenomena in the DBL [320,321].

### 5.2.1. Equivalent circuit models

Studies on equivalent circuit models draw an analogy between ionic transport perpendicular to the direction of flow in ED cells and electron movement in a direct current electrical circuit, where solutions and membranes function as resistive components. These models rely on fundamental theoretical principles such as Ohm's and Faraday's laws to
<figure><img src="image_19.png" /><figcaption>Fig. 17. Multiscale modelling applied to ED processes. Electrical equivalent circuit model figure is adapted from [322] (CC-BY 4.0 Elsevier). Semi-empirical model figure is adapted from [323], Copyright (2021), with permission from Elsevier. N-P model figures are adapted from [324], Copyright (2020), with permission from John Wiley & Sons, Inc. S-M model figure is adapted from [325], Copyright (2014) with permission from Elsevier.</figcaption></figure>

establish relationships between key parameters like ionic flux, current density, electrical resistance, and voltage without any possibility of 2-dimensional modeling. They offer considerable adaptability, allowing for the inclusion of various phenomena by adding additional resistive components and adjustments in mass balances. They enable a comprehensive understanding of ED systems, while providing the capability to incorporate specific small-scale phenomena as needed. Distinguishing features of these models encompass the presumption of either constant voltage or current, the application of either a plug flow reactor model or a continuous stirred-tank reactor model for managing mass balances, the acknowledgment of DBLs adjacent to membranes, considerations of diffusive transport, the integration of water movement, attention to EC reactions and the implications of terminal compartment effects, and the portrayal of membrane selectivity, whether depicted as ideal, fixed, or through a transport number model [322,326–330].

### 5.2.2. Semi-empirical models

The focus in this section is placed on modeling approaches that choose an alternative to solving the theoretical equation, favoring instead the simulation of IEMs using experimentally measurable macroscopic characteristics, such as transport coefficients, electrical resistance, as well as ion and osmotic permeability. These models adopt a simplified approach by focusing on essential variables and incorporating aggregated empirical parameters. These parameters undergo calibration to align the models’ forecasts with empirical data. Semi-empirical models primarily rely on a set of both algebraic and differential equations, organized as follows:

(i) Equations based on the principles of thermodynamics and electrostatics, enabling the derivation of parameters such as the cell potential (Eq. (1)), their resistance (Eq. (2)), and the generated electric current (Eqs. (13) and (25)) [331,332];

(ii) Mass conservation equations that account for variations in flow rates and concentrations in the main flow directions, inherently linked to mass transfer equations (Eqs. (50) – (51)) [333];

$$ \frac{d\dot{q}^{\text{dlt}}(x)C_i^{\text{dlt}}(x)}{dx} = -\frac{d\dot{q}^{\text{cte}}(x)C_i^{\text{cte}}(x)}{dx} = -1^{\text{IEM}} \times J_w(x) \quad (50) $$

$$ \frac{d\dot{q}^{\text{dlt}}(x)}{dx} = -\frac{d\dot{q}^{\text{cte}}(x)}{dx} = -1^{\text{IEM}} \times J_w(x) \quad (51) $$

where $\dot{q}$ is the volumetric flow rate (m³/s); $x$ is the flow direction (m); $1^{\text{IEM}}$ is the IEM length (m); $J_w$ and $J_w'$ are the molar and volumetric flux of solvent, respectively (mol m⁻² s⁻¹ and m³ m⁻² s⁻¹).

(iii) Transport equations calculating the mass flux of ions and water through the membrane from empirical data or autonomously determined values through dimensionless numbers (Sherwood Sh, Reynolds Re, Schmidt Sc; Eq. (52) – (53)) [334];

$$ \mathrm{Sh} = \frac{k \times d_c}{D} = f(\mathrm{Re}; \mathrm{Sc}) \quad (52) $$

$$ J_{\text{lim}} = \frac{z_i \times F \times D_i \times C_i^s}{\delta \times (t_i^m - t_i^s)} = \frac{\text{Sh} \times z_i \times F \times C_i^s}{d_c \times (t_i^m - t_i^s)} \quad (53) $$

where Sh is the Sherwood number (dimensionless); $k$ is the mass transfer coefficient (m/s); $d_c$ is a characteristic length (m) and $D$ is the diffusivity coefficient (m²/s).

(iv) Lastly, equations aimed at assessing macroscopic performance criteria, such as energy needs (Eqs. (30) – (31)), pumping losses (Eq. (29)), and efficiencies (Eqs. (26) – (27)), formulated from empirical data [335].

These modeling approaches frequently employ the segmentation modeling technique (multi-layer system considering the DBLs presence)
<figure><img src="image_20.png" /><figcaption>Fig. 17. Multiscale modelling applied to ED processes. Electrical equivalent circuit model figure is adapted from [322] (CC-BY 4.0 Elsevier). Semi-empirical model figure is adapted from [323], Copyright (2021), with permission from Elsevier. N-P model figures are adapted from [324], Copyright (2020), with permission from John Wiley & Sons, Inc. S-M model figure is adapted from [325], Copyright (2014) with permission from Elsevier.</figcaption></figure>

establish relationships between key parameters like ionic flux, current density, electrical resistance, and voltage without any possibility of 2-dimensional modeling. They offer considerable adaptability, allowing for the inclusion of various phenomena by adding additional resistive components and adjustments in mass balances. They enable a comprehensive understanding of ED systems, while providing the capability to incorporate specific small-scale phenomena as needed. Distinguishing features of these models encompass the presumption of either constant voltage or current, the application of either a plug flow reactor model or a continuous stirred-tank reactor model for managing mass balances, the acknowledgment of DBLs adjacent to membranes, considerations of diffusive transport, the integration of water movement, attention to EC reactions and the implications of terminal compartment effects, and the portrayal of membrane selectivity, whether depicted as ideal, fixed, or through a transport number model [322,326–330].

### 5.2.2. Semi-empirical models

The focus in this section is placed on modeling approaches that choose an alternative to solving the theoretical equation, favoring instead the simulation of IEMs using experimentally measurable macroscopic characteristics, such as transport coefficients, electrical resistance, as well as ion and osmotic permeability. These models adopt a simplified approach by focusing on essential variables and incorporating aggregated empirical parameters. These parameters undergo calibration to align the models’ forecasts with empirical data. Semi-empirical models primarily rely on a set of both algebraic and differential equations, organized as follows:

(i) Equations based on the principles of thermodynamics and electrostatics, enabling the derivation of parameters such as the cell potential (Eq. (1)), their resistance (Eq. (2)), and the generated electric current (Eqs. (13) and (25)) [331,332];

(ii) Mass conservation equations that account for variations in flow rates and concentrations in the main flow directions, inherently linked to mass transfer equations (Eqs. (50) – (51)) [333];

$$ \frac{d\dot{q}^{\text{dlt}}(x)C_i^{\text{dlt}}(x)}{dx} = -\frac{d\dot{q}^{\text{cte}}(x)C_i^{\text{cte}}(x)}{dx} = -1^{\text{IEM}} \times J_w(x) \quad (50) $$

$$ \frac{d\dot{q}^{\text{dlt}}(x)}{dx} = -\frac{d\dot{q}^{\text{cte}}(x)}{dx} = -1^{\text{IEM}} \times J_w(x) \quad (51) $$

where $\dot{q}$ is the volumetric flow rate (m³/s); $x$ is the flow direction (m); $1^{\text{IEM}}$ is the IEM length (m); $J_w$ and $J_w'$ are the molar and volumetric flux of solvent, respectively (mol m⁻² s⁻¹ and m³ m⁻² s⁻¹).

(iii) Transport equations calculating the mass flux of ions and water through the membrane from empirical data or autonomously determined values through dimensionless numbers (Sherwood Sh, Reynolds Re, Schmidt Sc; Eq. (52) – (53)) [334];

$$ Sh = \frac{k \times d_c}{D} = f(\text{Re}; \text{Sc}) \quad (52) $$

$$ J_{\text{lim}} = \frac{z_i \times F \times D_i \times C_i^s}{\delta \times (t_i^m - t_i^s)} = \frac{Sh \times z_i \times F \times C_i^s}{d_c \times (t_i^m - t_i^s)} \quad (53) $$

where $Sh$ is the Sherwood number (dimensionless); $k$ is the mass transfer coefficient (m/s); $d_c$ is a characteristic length (m) and $D$ is the diffusivity coefficient (m²/s).

(iv) Lastly, equations aimed at assessing macroscopic performance criteria, such as energy needs (Eqs. (30) – (31)), pumping losses (Eq. (29)), and efficiencies (Eqs. (26) – (27)), formulated from empirical data [335].

These modeling approaches frequently employ the segmentation modeling technique (multi-layer system considering the DBLs presence)
using one-dimensional equations. Despite their effective representation of process dynamics, semi-empirical models necessitate extensive initial experimental calibration and further experimentation for validation across different operational conditions. One significant limitation of these models is their lack of universality, indicating that validations performed on experimental, lab-scale setups may not directly translate to industrial-scale applications. Additionally, the practice of aggregating empirical parameters into lumped forms masks the discrete effects of individual phenomena on the system's overall performance.

### 5.2.3. Theoretical models based on Nernst-Planck and Stefan-Maxwell equations

The resolution of the N-P equation facilitates the creation of a multi-dimensional concentration profile by integrating contributions from diffusion, convection, and electromigration to the cumulative flux, as indicated in Eq. (21) [336].

To attain a complete model, an additional condition related to charge is necessary. This can be met either by assuming electroneutrality (where the aggregate of all charges at any given point, inclusive of those contributed by the membrane, equates to zero) [324] or by the application of the Poisson equation [337], as described in Eq. (54).

$$ \sum_{i} z_{i} C_{i} = - \epsilon_{r} \epsilon_{0} \nabla^{2} \varphi \qquad (54) $$

where $\epsilon_r$ and $\epsilon_0$ correspond to the relative permittivity of the medium and the permittivity of free space, respectively (F/m).

The complexity of the intertwined partial differential equations necessitates their resolution via numerical methods such as the finite element method or finite volume method within CFD software packages like COMSOL Multiphysics, Ansys Fluent, or OpenFOAM while solving at the same time Navier-Stockes equations for hydrodynamic conditions [319,338]. These approaches enable detailed exploration at a micro-scale, proving invaluable for the examination of localized effects such as concentration polarization and the impact of space charge regions using two-dimensional equations [339,340]. Nevertheless, the granular focus of these analyses renders the extrapolation to full-scale process models as less efficient. In models based on the N-P framework, simplifications are typically made: (i) simulations are conducted either in one dimension (across the membrane) or (ii) two dimensions (along the axis and across the membrane) [341].

Note that the use of the N-P theory encounters specific limitations. Notably, in solutions of high concentration, ions are surrounded not only by solvent molecules but also by other ions, which increases the significance of short-distance interactions. Consequently, additional frictional forces come into play. To accurately model ionic transport in these dense media (specifically in DBLs), it is indispensable to incorporate a greater number of transport coefficients and to adopt a more precise approach, such as that proposed by the S-M equations [342]. Examples of using this method to analyze ion exchange systems remain scarce in scientific publications. Nonetheless, the N-P model remains widely preferred due to its ease of application and reliability across a broad range of operational conditions encountered in ED [315].

## 5.3. Operating modes

Electrodialysis with many hybrid modes allowing for improved performance and efficiency. This section presents them.

### 5.3.1. Electro-electrodialysis or redox-mediated electrodialysis

Electro-electrodialysis, also known as redox-mediated ED, represents an interesting development halfway between electrolysis and ED. In this configuration, a multi-compartment cell facilitates selective ion transport across IEMs, while interesting EC reactions take place at the electrodes, modifying the usual solvent oxidation and reduction reactions. [44,343,344]. This approach has facilitated the production of various

organic and inorganic acids and bases, including acids, quaternary ammonium hydroxide, $H_3BO_3$, and LiOH [44,345–348]. Noticeable efficiency in terms of productivity, rooted in electrode reactions, alongside effective product separation due to the utilization of stacked electro-membrane separation units, has been noted with redox reactions using ferri-/ferrocyanide or viologen solutions for example [349–352].

### 5.3.2. Electrodialysis metathesis

ED stands out in this domain for its notable environmental benefits and operational efficiencies [344,353]. Nonetheless, conventional ED techniques primarily focus on concentrating liquid salts without offering mechanisms for further enhancing the value of these resources [24]. Given the push for environmental conservation and the principles of sustainable development, there's a growing inclination towards transforming low-value saline wastewaters into high-value products [354]. ED metathesis has emerged as a prominent solution, acclaimed for its capacity to convert, and reclaim valuable mineral resources from saline waters. The essence of the ED metathesis process involves the ion exchange between two distinct saline streams, leading to the creation of new salt streams [355], echoing the conventional metathesis reaction as presented in Fig. 18 for the case of tetrapropyl ammonium hydroxide [356]. Thanks to these advantages, ED metathesis has found extensive application in the area of high-value salt conversion, treatment of saline wastewater, and the recovery of valuable resources, marking a significant stride towards resource sustainability and environmental stewardship [357,358].

### 5.3.3. Oscillating regimes

Recent literature emphasizes the improvement in transport phenomena and overall process efficiency within ED systems through the implementation of non-stationary operational conditions. By introducing oscillations, achieved through dynamic alterations in (i) electrical field or (ii) flow rate, an enhancement in the performance of ED systems has been documented. Applying pulsed flows or electric fields at relatively high frequencies, characterized by a time constant significantly smaller than the characteristic diffusion times observed in systems with high Schmidt numbers (Sc, Eq. (55)), disrupts the DBL and leads to a more uniform concentration profile. This departure from the usual sequence of steady states results in transient concentration fields that differ markedly. As a result, the concentration polarization phenomenon and its associated drawbacks can be substantially mitigated, leading to higher limiting currents and reduced electrical resistances. The extent of these benefits is influenced by the frequency, amplitude, and waveform of the applied oscillations [359].

<figure><img src="image_20.png" /><figcaption>Fig. 18. Schematic principles of the ED metathesis process to prepare tetrapropyl ammonium hydroxide (TPAOH).</figcaption></figure>
using one-dimensional equations. Despite their effective representation of process dynamics, semi-empirical models necessitate extensive initial experimental calibration and further experimentation for validation across different operational conditions. One significant limitation of these models is their lack of universality, indicating that validations performed on experimental, lab-scale setups may not directly translate to industrial-scale applications. Additionally, the practice of aggregating empirical parameters into lumped forms masks the discrete effects of individual phenomena on the system's overall performance.

### 5.2.3. Theoretical models based on Nernst-Planck and Stefan-Maxwell equations

The resolution of the N-P equation facilitates the creation of a multi-dimensional concentration profile by integrating contributions from diffusion, convection, and electromigration to the cumulative flux, as indicated in Eq. (21) [336].

To attain a complete model, an additional condition related to charge is necessary. This can be met either by assuming electroneutrality (where the aggregate of all charges at any given point, inclusive of those contributed by the membrane, equates to zero) [324] or by the application of the Poisson equation [337], as described in Eq. (54).

$$ \sum_{i} z_{i} C_{i} = - \epsilon_{r} \epsilon_{0} \nabla^{2} \varphi \qquad (54) $$

where $\epsilon_r$ and $\epsilon_0$ correspond to the relative permittivity of the medium and the permittivity of free space, respectively (F/m).

The complexity of the intertwined partial differential equations necessitates their resolution via numerical methods such as the finite element method or finite volume method within CFD software packages like COMSOL Multiphysics, Ansys Fluent, or OpenFOAM while solving at the same time Navier-Stockes equations for hydrodynamic conditions [319,338]. These approaches enable detailed exploration at a micro-scale, proving invaluable for the examination of localized effects such as concentration polarization and the impact of space charge regions using two-dimensional equations [339,340]. Nevertheless, the granular focus of these analyses renders the extrapolation to full-scale process models as less efficient. In models based on the N-P framework, simplifications are typically made: (i) simulations are conducted either in one dimension (across the membrane) or (ii) two dimensions (along the axis and across the membrane) [341].

Note that the use of the N-P theory encounters specific limitations. Notably, in solutions of high concentration, ions are surrounded not only by solvent molecules but also by other ions, which increases the significance of short-distance interactions. Consequently, additional frictional forces come into play. To accurately model ionic transport in these dense media (specifically in DBLs), it is indispensable to incorporate a greater number of transport coefficients and to adopt a more precise approach, such as that proposed by the S-M equations [342]. Examples of using this method to analyze ion exchange systems remain scarce in scientific publications. Nonetheless, the N-P model remains widely preferred due to its ease of application and reliability across a broad range of operational conditions encountered in ED [315].

## 5.3. Operating modes

Electrodialysis with many hybrid modes allowing for improved performance and efficiency. This section presents them.

### 5.3.1. Electro-electrodialysis or redox-mediated electrodialysis

Electro-electrodialysis, also known as redox-mediated ED, represents an interesting development halfway between electrolysis and ED. In this configuration, a multi-compartment cell facilitates selective ion transport across IEMs, while interesting EC reactions take place at the electrodes, modifying the usual solvent oxidation and reduction reactions. [44,343,344]. This approach has facilitated the production of various

organic and inorganic acids and bases, including acids, quaternary ammonium hydroxide, $H_3BO_3$, and LiOH [44,345–348]. Noticeable efficiency in terms of productivity, rooted in electrode reactions, alongside effective product separation due to the utilization of stacked electro-membrane separation units, has been noted with redox reactions using ferri-/ferrocyanide or viologen solutions for example [349–352].

### 5.3.2. Electrodialysis metathesis

ED stands out in this domain for its notable environmental benefits and operational efficiencies [344,353]. Nonetheless, conventional ED techniques primarily focus on concentrating liquid salts without offering mechanisms for further enhancing the value of these resources [24]. Given the push for environmental conservation and the principles of sustainable development, there's a growing inclination towards transforming low-value saline wastewaters into high-value products [354]. ED metathesis has emerged as a prominent solution, acclaimed for its capacity to convert, and reclaim valuable mineral resources from saline waters. The essence of the ED metathesis process involves the ion exchange between two distinct saline streams, leading to the creation of new salt streams [355], echoing the conventional metathesis reaction as presented in Fig. 18 for the case of tetrapropyl ammonium hydroxide [356]. Thanks to these advantages, ED metathesis has found extensive application in the area of high-value salt conversion, treatment of saline wastewater, and the recovery of valuable resources, marking a significant stride towards resource sustainability and environmental stewardship [357,358].

### 5.3.3. Oscillating regimes

Recent literature emphasizes the improvement in transport phenomena and overall process efficiency within ED systems through the implementation of non-stationary operational conditions. By introducing oscillations, achieved through dynamic alterations in (i) electrical field or (ii) flow rate, an enhancement in the performance of ED systems has been documented. Applying pulsed flows or electric fields at relatively high frequencies, characterized by a time constant significantly smaller than the characteristic diffusion times observed in systems with high Schmidt numbers (Sc, Eq. (55)), disrupts the DBL and leads to a more uniform concentration profile. This departure from the usual sequence of steady states results in transient concentration fields that differ markedly. As a result, the concentration polarization phenomenon and its associated drawbacks can be substantially mitigated, leading to higher limiting currents and reduced electrical resistances. The extent of these benefits is influenced by the frequency, amplitude, and waveform of the applied oscillations [359].

<figure><img src="image_21.png" /><figcaption>Fig. 18. Schematic principles of the ED metathesis process to prepare tetrapropyl ammonium hydroxide (TPAOH).</figcaption></figure>
$$ Sc = \frac{\mu_{\text{electrolyte}}}{\rho_{\text{electrolyte}} \times D_i} \qquad (55) $$

where $\mu_{\text{electrolyte}}$ is the dynamic viscosity of the electrolyte (Pa s) and $\rho_{\text{electrolyte}}$ is the mass density of the electrolyte (g/m$^{-3}$(–|–)).

The pulsed electric field (PEF) operates in a dynamic regime by applying a discontinuous current or voltage, as illustrated in Fig. 19. During a specified period, the system experiences a steady current or voltage, followed by a period where the current or voltage is deactivated. The procedure involving a PEF employs a series of these pulse and pause intervals with uniform lengths [360]. Studies have documented that frequencies between 0.01 and 100 Hz are effective in reducing fouling across diverse fluid streams [361].

The dynamic electric fields in a non-stationary setup can reduce the effects of concentration polarization, as the interval between pulses allows for the continued migration of ions from the bulk solution to the membrane through both diffusion and convective processes, thus diminishing the concentration gradient prior to the next pulse's application [363]. Recent insights into PEF during pauses have shown a reduction in electrical resistance due to ion redistribution at the dilute membrane interface, leading to low electrical resistance. Voltage reapplied after pauses induces electroconvective vortices, causing current surges and affecting turbulence, facilitating ion transfer to membrane surfaces [364–366]. The concept of employing a non-stationary electric field was initially introduced in 1995 as a method to mitigate the phenomenon of concentration polarization and to facilitate the separation of Na$^+$ from Ca$^{2+}$ [367]. Through theoretical analysis, Mishchuk et al. posited that the benefits of ED intensification are most pronounced when the duration of the electrical pulses is significantly shorter than the time it takes for the polarization layer to form [368]. Additionally, the application of PEF has been shown to improve the performance of ED in desalinating solutions containing various components such as whey proteins [369], humate [370], and wastewater effluents [371]. The equipment required for generating pulse-pause sequences, such as a pulse generator linked to the power supply or computer-controlled automatic pulse generation, is relatively simple and cost-effective, promoting the scalability of PEF technology [362].

Unlike PEFs, inducing oscillations in flow rates entails significant energy consumption [372], and the industrial feasibility of such

application appears to be more distant. Nevertheless, several studies at the laboratory scale have shown substantial benefits in terms of mass transfer and fouling prevention [373–375].

### 5.3.4. Complexation electrodialysis

Complexation ED can be used in two different scenarios:

(i) Due to the inherent limitations in the separation capabilities of IEMs, substances that are neutrally or weakly charged do not move within the current field and, consequently, cannot be extracted from feed solutions. However, these substances can be effectively removed through an electro-membrane process by coupling the separation with chemical modifications such as protonation or complexation, transforming them into strongly charged entities. This strategy offers multiple benefits, including the ability to separate neutral or weakly charged substances via electro-driven forces.

(ii) Specifically, when treating metal ions, the valences of the latter are often similar, and the creation of a complexation reaction can lead to improved selectivity in the ED system [168].

Jiang and colleagues introduced an innovative complexation ED method, where a complexation reaction between functional organic compounds and heavy metal ions occurs. This process was demonstrated by combining Cr$^{3+}$ (from electroplating waste) with acetylacetone (acac, from pharmaceutical waste) in a stirred reactor to form positively charged Cr(acac)$^{(3-n)+}$ complexes. These complexes were then processed in a complexation ED system, which benefits from induced turbulence to augment mass transfer and maximize the limiting current density. The system achieved removal efficiencies of 99.4 %–99.5 % for metal ions and 97.8 %–99.9 % for organics [376]. Additionally, a notable application of complexation ED is the treatment of aniline-containing wastewater through BED, leveraging the protonation interaction between aniline and carbon dioxide. Injecting CO$_2$ into aniline wastewater leads to the protonation of aniline, making it positively charged, while CO$_2$ is converted to bicarbonate/carbonate ions or carbonic acid. The resulting mixture can subsequently be processed in a BED system [377]. Babilas et al. utilized Na$_2$EDT A for complexing with Cu$^{2+}$ and Zn$^{2+}$ ions present in a zinc plating bath, achieving selective

<figure><img src="image_21.png" /><figcaption>Fig. 19. Principle of PEF and effect on concentration polarization. Reprinted with permission from [362] (CC-BY MDPI).</figcaption></figure>
$$ Sc = \frac{\mu_{\text{electrolyte}}}{\rho_{\text{electrolyte}} \times D_i} \qquad (55) $$

where $\mu_{\text{electrolyte}}$ is the dynamic viscosity of the electrolyte (Pa s) and $\rho_{\text{electrolyte}}$ is the mass density of the electrolyte (g/m$^{-3}$(–|–)).

The pulsed electric field (PEF) operates in a dynamic regime by applying a discontinuous current or voltage, as illustrated in Fig. 19. During a specified period, the system experiences a steady current or voltage, followed by a period where the current or voltage is deactivated. The procedure involving a PEF employs a series of these pulse and pause intervals with uniform lengths [360]. Studies have documented that frequencies between 0.01 and 100 Hz are effective in reducing fouling across diverse fluid streams [361].

The dynamic electric fields in a non-stationary setup can reduce the effects of concentration polarization, as the interval between pulses allows for the continued migration of ions from the bulk solution to the membrane through both diffusion and convective processes, thus diminishing the concentration gradient prior to the next pulse's application [363]. Recent insights into PEF during pauses have shown a reduction in electrical resistance due to ion redistribution at the dilute membrane interface, leading to low electrical resistance. Voltage reapplied after pauses induces electroconvective vortices, causing current surges and affecting turbulence, facilitating ion transfer to membrane surfaces [364–366]. The concept of employing a non-stationary electric field was initially introduced in 1995 as a method to mitigate the phenomenon of concentration polarization and to facilitate the separation of Na$^+$ from Ca$^{2+}$ [367]. Through theoretical analysis, Mishchuk et al. posited that the benefits of ED intensification are most pronounced when the duration of the electrical pulses is significantly shorter than the time it takes for the polarization layer to form [368]. Additionally, the application of PEF has been shown to improve the performance of ED in desalinating solutions containing various components such as whey proteins [369], humate [370], and wastewater effluents [371]. The equipment required for generating pulse-pause sequences, such as a pulse generator linked to the power supply or computer-controlled automatic pulse generation, is relatively simple and cost-effective, promoting the scalability of PEF technology [362].

Unlike PEFs, inducing oscillations in flow rates entails significant energy consumption [372], and the industrial feasibility of such

application appears to be more distant. Nevertheless, several studies at the laboratory scale have shown substantial benefits in terms of mass transfer and fouling prevention [373–375].

### 5.3.4. Complexation electrodialysis

Complexation ED can be used in two different scenarios:

(i) Due to the inherent limitations in the separation capabilities of IEMs, substances that are neutrally or weakly charged do not move within the current field and, consequently, cannot be extracted from feed solutions. However, these substances can be effectively removed through an electro-membrane process by coupling the separation with chemical modifications such as protonation or complexation, transforming them into strongly charged entities. This strategy offers multiple benefits, including the ability to separate neutral or weakly charged substances via electro-driven forces.

(ii) Specifically, when treating metal ions, the valences of the latter are often similar, and the creation of a complexation reaction can lead to improved selectivity in the ED system [168].

Jiang and colleagues introduced an innovative complexation ED method, where a complexation reaction between functional organic compounds and heavy metal ions occurs. This process was demonstrated by combining Cr$^{3+}$ (from electroplating waste) with acetylacetone (acac, from pharmaceutical waste) in a stirred reactor to form positively charged Cr(acac)$^{(3-n)+}$ complexes. These complexes were then processed in a complexation ED system, which benefits from induced turbulence to augment mass transfer and maximize the limiting current density. The system achieved removal efficiencies of 99.4 %–99.5 % for metal ions and 97.8 %–99.9 % for organics [376]. Additionally, a notable application of complexation ED is the treatment of aniline-containing wastewater through BED, leveraging the protonation interaction between aniline and carbon dioxide. Injecting CO$_2$ into aniline wastewater leads to the protonation of aniline, making it positively charged, while CO$_2$ is converted to bicarbonate/carbonate ions or carbonic acid. The resulting mixture can subsequently be processed in a BED system [377]. Babilas et al. utilized Na$_2$EDT A for complexing with Cu$^{2+}$ and Zn$^{2+}$ ions present in a zinc plating bath, achieving selective

<figure><img src="image_22.png" /><figcaption>Fig. 19. Principle of PEF and effect on concentration polarization. Reprinted with permission from [362] (CC-BY MDPI).</figcaption></figure>
removal of Zn²⁺ salts to the concentrate compartment and retention of Cu²⁺ complexes in the dilute compartment [378] A diversity of complexing agents has been explored to enhance the selectivity in the recovery of metal salts. EDTA stands out as the most frequently used complexing agent, proving its efficacy in the selective extraction of various metal salts including Ni²⁺ and Co²⁺ [379], Sr²⁺ and Cs⁺ [380], Ca²⁺ and Cd²⁺ [168], as well as Ag²⁺ and Zn²⁺ and Cu²⁺ and Cd²⁺ salts [381]. Additionally, other complexing agents such as citric acid – glycine [382], acid and acetylacetone [376] have also been shown to be effective.

## 6. Scaling-up: From laboratory to pilot scale

Scaling up a process is a critical phase in the development of ED technology, bridging the gap between theoretical research and practical application. This section delves into the essential aspects of this transition for ED. First, a practical dimensioning approach is discussed to ensure the successful scale-up of ED processes. Following this, the economics and sustainability of the scaled-up ED process are evaluated, providing insight into the financial and environmental implications through techno-economic assessment (TEA) and life cycle assessment (LCA).

### 6.1. Practical dimensioning approach

Scaling up an ED setup from laboratory to pilot scale requires meticulous planning and optimization across several practical dimensions.

Initially, it's crucial to systematically optimize parameters such as applied voltage, current density, flow rate, and solution pH, which are instrumental in achieving the desired balance between separation efficiency, energy consumption, and membrane longevity. This optimization process, informed by laboratory-scale experiments, guides the selection of suitable membranes and spacers that offer the best performance in terms of ion selectivity, fouling resistance, and mechanical stability, as adjustments may be necessary to accommodate increased flow rates and solution volumes at the pilot scale. The transition from lab to pilot scale involves designing an appropriately scaled ED stack, alongside engineering considerations for pumps, piping, electrical supply, and control systems to handle the increased operational scale with ease of maintenance and monitoring in mind. By keeping dimensionless numbers constant (e.g., Newton, Peclet, Reynolds, Schmidt, Sherwood, Stanton numbers [383] or Thiele modulus [384]) and employing mathematical modeling aids in predicting the performance of the scaled-up system, facilitating adjustments based on pilot results for further optimization. This phase often requires re-tuning of operating conditions, membrane cleaning protocols, or system configurations to align with expected performance levels. There are two principal approaches to enhance the volume capacity treated by an ED system. The first approach focuses on adding more repeating units (N<sub>cp</sub>) to the membrane stack, which, while increasing power consumption due to system over potentials, minimally impacts electrode resistance and cell components. The second involves operating multiple ED units in parallel, which can significantly escalate equipment costs due to the need for additional stack materials. The most crucial criterion to improve during scale-up, regardless of the chosen method, remains the specific surface area criterion (the ratio of the active membrane surface area to the compartment volume) that must be imperatively sought to be maximized.

To date, several studies highlight research work on the ED process at the pilot scale (1000 cell pairs reverse ED with municipal wastewater effluent and seawater, achieving a power production of 95.8 W [385]; ED in a 30-cell pair pilot reactor demonstrated efficient concentration of NH₄⁺ ions from wastewater with a power consumption of 4.9 kWh kgNH₄⁻N [386]; large-scale ED using BMs and exhibiting 19 m² of total area [387]; separation of etc.). However, to the authors' knowledge,

there is no mention anywhere of a developed and complete methodology for a process change of an ED cell from understanding mechanisms at the local scale to the development at an industrial scale of a pilot demonstrator on a specific application.

When scaling up the process, certain phenomena that are very rarely observed at the laboratory scale can be highlighted. This is notably the case for leakage currents (also called shunt currents). In an ED system, a series of IEMs are arranged in an alternating mode, facilitating ion migration based on their respective charges. Two primary sources contribute to the presence of parasitic currents. The first source is attributed to the transport of co-ions in an IEM. While the desired transport involves counter-ions, membranes exhibit less than absolute selectivity, allowing co-ions to pass through as well as depicted in the previous Section 4.6.2. The second source stems from ionic shortcut currents, which are generated by the movement of ions across the feed and drain channels. These channels serve as conduits for ionic transfer between compartments, facilitated by a difference in potential across cells. In a large stack, the potential difference between end cells could reach several hundred volts (e.g., 0.6 V per cell pair [247]). In ED stack, both types of parasitic currents lead to diminished power efficiency. While reducing co-ion transport primarily involves improvements in membrane technology, the impact of ionic shortcut currents is closely linked to the design of the stack and is therefore the focus of this section. In case of current leakage, not only does this current traverse the membranes' active regions, but it also extends into these channels, as depicted in Fig. 20. Such detouring of current is influenced by the stack's geometric arrangement, the count of cell pairs (N<sub>cp</sub>), and the conductivity properties of both the membranes and the solution. Consequently, this leakage current undermines the apparatus's efficiency since a portion of the electric current is diverted away from its primary role of driving ion transport across the membranes [388,389]. It has been highlighted in numerous studies that leakage currents predominantly occur configurations featuring a single hydraulic circuit [390–393]. Several strategies are proposed to mitigate the effects of ionic shortcut currents:

(i) Introducing air bubbles into the feed stream.

(ii) Implementing rotating valves functions as physical obstacles that interrupt the flow of electrolytic currents [394].

(iii) Using serial feeding, by directing concentrate/dilute sequentially through all compartments within the stack, which are interconnected in an alternating top-to-bottom manner, leading

<figure><img src="image_22.png" /><figcaption>**Fig. 20.** Scheme of ED module with leakage currents (red arrows). Probes, designed as platinum wires, were incorporated into non-conductive separators and enabled the measurement of voltage directly inside the module. Used with permission of IOP Publishing, Ltd, from [390]; permission conveyed through Copyright Clearance Center, Inc. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
removal of Zn²⁺ salts to the concentrate compartment and retention of Cu²⁺ complexes in the dilute compartment [378] A diversity of complexing agents has been explored to enhance the selectivity in the recovery of metal salts. EDTA stands out as the most frequently used complexing agent, proving its efficacy in the selective extraction of various metal salts including Ni²⁺ and Co²⁺ [379], Sr²⁺ and Cs⁺ [380], Ca²⁺ and Cd²⁺ [168], as well as Ag²⁺ and Zn²⁺ and Cu²⁺ and Cd²⁺ salts [381]. Additionally, other complexing agents such as citric acid – glycine [382], acid and acetylacetone [376] have also been shown to be effective.

## 6. Scaling-up: From laboratory to pilot scale

Scaling up a process is a critical phase in the development of ED technology, bridging the gap between theoretical research and practical application. This section delves into the essential aspects of this transition for ED. First, a practical dimensioning approach is discussed to ensure the successful scale-up of ED processes. Following this, the economics and sustainability of the scaled-up ED process are evaluated, providing insight into the financial and environmental implications through techno-economic assessment (TEA) and life cycle assessment (LCA).

### 6.1. Practical dimensioning approach

Scaling up an ED setup from laboratory to pilot scale requires meticulous planning and optimization across several practical dimensions.

Initially, it's crucial to systematically optimize parameters such as applied voltage, current density, flow rate, and solution pH, which are instrumental in achieving the desired balance between separation efficiency, energy consumption, and membrane longevity. This optimization process, informed by laboratory-scale experiments, guides the selection of suitable membranes and spacers that offer the best performance in terms of ion selectivity, fouling resistance, and mechanical stability, as adjustments may be necessary to accommodate increased flow rates and solution volumes at the pilot scale. The transition from lab to pilot scale involves designing an appropriately scaled ED stack, alongside engineering considerations for pumps, piping, electrical supply, and control systems to handle the increased operational scale with ease of maintenance and monitoring in mind. By keeping dimensionless numbers constant (e.g., Newton, Peclet, Reynolds, Schmidt, Sherwood, Stanton numbers [383] or Thiele modulus [384]) and employing mathematical modeling aids in predicting the performance of the scaled-up system, facilitating adjustments based on pilot results for further optimization. This phase often requires re-tuning of operating conditions, membrane cleaning protocols, or system configurations to align with expected performance levels. There are two principal approaches to enhance the volume capacity treated by an ED system. The first approach focuses on adding more repeating units (N<sub>cp</sub>) to the membrane stack, which, while increasing power consumption due to system over potentials, minimally impacts electrode resistance and cell components. The second involves operating multiple ED units in parallel, which can significantly escalate equipment costs due to the need for additional stack materials. The most crucial criterion to improve during scale-up, regardless of the chosen method, remains the specific surface area criterion (the ratio of the active membrane surface area to the compartment volume) that must be imperatively sought to be maximized.

To date, several studies highlight research work on the ED process at the pilot scale (1000 cell pairs reverse ED with municipal wastewater effluent and seawater, achieving a power production of 95.8 W [385]; ED in a 30-cell pair pilot reactor demonstrated efficient concentration of NH₄⁺ ions from wastewater with a power consumption of 4.9 kWh kgNH₄⁻N [386]; large-scale ED using BMs and exhibiting 19 m² of total area [387]; separation of etc.). However, to the authors' knowledge,

there is no mention anywhere of a developed and complete methodology for a process change of an ED cell from understanding mechanisms at the local scale to the development at an industrial scale of a pilot demonstrator on a specific application.

When scaling up the process, certain phenomena that are very rarely observed at the laboratory scale can be highlighted. This is notably the case for leakage currents (also called shunt currents). In an ED system, a series of IEMs are arranged in an alternating mode, facilitating ion migration based on their respective charges. Two primary sources contribute to the presence of parasitic currents. The first source is attributed to the transport of co-ions in an IEM. While the desired transport involves counter-ions, membranes exhibit less than absolute selectivity, allowing co-ions to pass through as well as depicted in the previous Section 4.6.2. The second source stems from ionic shortcut currents, which are generated by the movement of ions across the feed and drain channels. These channels serve as conduits for ionic transfer between compartments, facilitated by a difference in potential across cells. In a large stack, the potential difference between end cells could reach several hundred volts (e.g., 0.6 V per cell pair [247]). In ED stack, both types of parasitic currents lead to diminished power efficiency. While reducing co-ion transport primarily involves improvements in membrane technology, the impact of ionic shortcut currents is closely linked to the design of the stack and is therefore the focus of this section. In case of current leakage, not only does this current traverse the membranes' active regions, but it also extends into these channels, as depicted in Fig. 20. Such detouring of current is influenced by the stack's geometric arrangement, the count of cell pairs (N<sub>cp</sub>), and the conductivity properties of both the membranes and the solution. Consequently, this leakage current undermines the apparatus's efficiency since a portion of the electric current is diverted away from its primary role of driving ion transport across the membranes [388,389]. It has been highlighted in numerous studies that leakage currents predominantly occur configurations featuring a single hydraulic circuit [390–393]. Several strategies are proposed to mitigate the effects of ionic shortcut currents:

(i) Introducing air bubbles into the feed stream.

(ii) Implementing rotating valves functions as physical obstacles that interrupt the flow of electrolytic currents [394].

(iii) Using serial feeding, by directing concentrate/dilute sequentially through all compartments within the stack, which are interconnected in an alternating top-to-bottom manner, leading

<figure><img src="image_23.png" /><figcaption>**Fig. 20.** Scheme of ED module with leakage currents (red arrows). Probes, designed as platinum wires, were incorporated into non-conductive separators and enabled the measurement of voltage directly inside the module. Used with permission of IOP Publishing, Ltd, from [390]; permission conveyed through Copyright Clearance Center, Inc. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
to a zigzag flow pattern. Such a configuration forces any potential ionic shortcut currents to traverse a significantly elongated path, leading to a substantial reduction in their intensity. Nonetheless, this setup introduces greater fluid resistance. A hybrid approach, combining elements of both parallel and serial feeding, may offer an optimal solution in terms of stack design.

(iv) Employing spiral wound modules [395].

These strategies have been proposed to minimize the cross-sectional area of the liquid phase in channels and manifolds to enhance the performance, as mentioned in Section 5.1.3. However, these solutions introduce its own set of challenges, notably an increase in the pressure drop across the stacks. This elevation in pressure drop results in higher energy consumption for pumping and imposes more stringent demands on the mechanical durability of the stack's end plates and frame. A review of the literature indicates that adjusting the dimensions of the channels, specifically their length and diameter, presents a relatively straightforward method to enhance the current efficiency of large stacks. Nonetheless, determining the optimal channel dimensions often depends on empirical knowledge, highlighting a gap in the development of

optimization models that balance current efficiency with pressure drop considerations in stack design [396].

## 6.2. Evaluation of sustainability and economics

### 6.2.1. Techno-economic assessment

Towards industrial application of green ED, its success hinges on the optimization of the system's design. Research and development efforts are crucially directed towards enhancing several key dimensions:

(i) The improvement of efficiency at various scales, including the cell, stack, and overall system, is paramount for reducing operational costs as well as the selectivity of the process.

(ii) Extending the lifespan of any developed system to surpass 10 years is another critical goal.

(iii) Reduction in investment costs (through capital expenditure (CAPEX) and operational expenditure (OPEX)), covering both the stack and the entire system, is necessary to make green ED technologies accessible and appealing for industrial applications.

<figure><img src="image_23.png" /><figcaption>**Fig. 21.** Trade-offs between energy efficiency, selectivity, durability, and cost of ED process to meet product concentration and purity specifications. The green frames display the various actions to be taken to improve the associated criterion. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
to a zigzag flow pattern. Such a configuration forces any potential ionic shortcut currents to traverse a significantly elongated path, leading to a substantial reduction in their intensity. Nonetheless, this setup introduces greater fluid resistance. A hybrid approach, combining elements of both parallel and serial feeding, may offer an optimal solution in terms of stack design.

(iv) Employing spiral wound modules [395].

These strategies have been proposed to minimize the cross-sectional area of the liquid phase in channels and manifolds to enhance the performance, as mentioned in Section 5.1.3. However, these solutions introduce its own set of challenges, notably an increase in the pressure drop across the stacks. This elevation in pressure drop results in higher energy consumption for pumping and imposes more stringent demands on the mechanical durability of the stack's end plates and frame. A review of the literature indicates that adjusting the dimensions of the channels, specifically their length and diameter, presents a relatively straightforward method to enhance the current efficiency of large stacks. Nonetheless, determining the optimal channel dimensions often depends on empirical knowledge, highlighting a gap in the development of

optimization models that balance current efficiency with pressure drop considerations in stack design [396].

## 6.2. Evaluation of sustainability and economics

### 6.2.1. Techno-economic assessment

Towards industrial application of green ED, its success hinges on the optimization of the system's design. Research and development efforts are crucially directed towards enhancing several key dimensions:

(i) The improvement of efficiency at various scales, including the cell, stack, and overall system, is paramount for reducing operational costs as well as the selectivity of the process.

(ii) Extending the lifespan of any developed system to surpass 10 years is another critical goal.

(iii) Reduction in investment costs (through capital expenditure (CAPEX) and operational expenditure (OPEX)), covering both the stack and the entire system, is necessary to make green ED technologies accessible and appealing for industrial applications.

<figure><img src="image_24.png" /><figcaption>**Fig. 21.** Trade-offs between energy efficiency, selectivity, durability, and cost of ED process to meet product concentration and purity specifications. The green frames display the various actions to be taken to improve the associated criterion. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
However, advancements in one area often entail trade-offs in others. Thicker membrane may enhance mechanical strength and durability but also increase resistance to charge transport, thereby diminishing efficiency. Similarly, while a longer lifespan reduces the cost attributed to investment by diluting it over time, any decrease in efficiency can lead to elevated operational costs due to increased energy consumption. Fig. 21 illustrates the beneficial or detrimental impacts that specific independent decisions, made during the manufacturing and research phases, exert on each of the aforementioned dimensions. Furthermore, the selection of materials, particularly catalysts and IEMs, plays a vital role in balancing efficiency and durability. More active functional groups of IEMs (see Section 3), can boost efficiency but may face challenges in maintaining stability over time. The pursuit of catalysts devoid of critical raw materials aims at sustaining high efficiency levels; however, it frequently results in materials that are less durable and more susceptible to degradation. Efficiency and selectivity are often conflicting dimensions. An increase in the thickness of the membrane will lead to better selectivity but will increase the ohmic over-potential of the membrane and therefore decrease its efficiency. Once again, it is all a matter of compromise. These dynamics underscore the complex interplay between various factors in the development of green ED technologies at large scale.

Concerning the TEA itself, the economic assessment of the cost per cubic meter produced necessitates calculating both the investment and operating costs. These parameters are derived from factors such as membrane surface area, flow rate, recovery rate, cell velocity, and outlet concentrations. Capital costs encompass process-related expenses (including pre-treatment and treatment), building construction, and auxiliary equipment. Operating costs cover consumables, energy expenses, and maintenance of installations (such as membrane and electrode replacements). Interestingly, few studies have ventured into the TEA of the ED process, which may indicate a certain impediment to the development of this technology on a larger scale. However, it should be noted that among the niche studies addressing TEA, most focus on applications in the field of water treatment [397-399]. For example, the works of Generous et al. [93] (28.6 %, 24.9 %, and 46.5 % of unit product water costs are contributed by power, fixed, and other charges<fnref n="1" />) and Ankoliya et al. [400] (cost of acid and base produced is comparable with the commercially available reagent) have demonstrated the benefits of this technology over conventional process. Studies applied to very specific case studies are emerging, such as those for the production of $H_3Co(CN)_6$ [262], the capture of $CO_2$ [401] or the coupling of BED with photovoltaic energy source [402].

### 6.2.2. Life cycle assessment

The challenges in reducing the environmental impact of production processes, via separation unit operation, present even greater opportunities for physical methods like ED compared to more polluting chemico physical methods (e.g., liquid-liquid extraction, precipitation, etc.). In the same way as TEAs, LCAs are good indicators of the maturity advancement of a technology. These data-intensive analyses enable us to approximate industrial operating conditions as closely as possible (e.g., inputs such as flow rate, chemicals, power, heat demand, and outputs such as product and waste specifications) and necessitate the fixing of selected environmental impacts (e.g., climate change through greenhouse gas emission quantification, discharge through mass balance, etc.) for each specific study.

A similar behavior to that of TEAs is observed for LCAs. Indeed, the majority of LCAs presented in the literature focus more on wastewater treatment applications. LCAs of wastewater treatment by ED primarily estimate carbon and water footprint distribution, with the objective of technology scale-up [4,403,404]. These studies include the work of

Alrashidi et al. [405], which shows that in addition to frequent membrane replacements, membrane costs contribute significantly (50 % of equipment cost). Another study by Akula et al. [406] demonstrates that the use of BED is more eco-efficient than conventional processes for nitrate removal from wastewater.

However, ED is inherently energy-consuming technologies via external bias application. This results in indirect environmental impacts due to the emission of various air pollutants from conventional energy production processes, contributing to the greenhouse effect. For example, estimates indicate that estimated carbon footprint of seawater desalination is around $0.4 - 6.7 \text{ kgCO}_2\text{eq} \text{ m}^{-3}$ [407] (large gap due to variability in location, technologies, life cycle stages, etc.). This impact can be reduced via the implementation of renewable energy sources [408].

## 7. Diverse applications and case studies

In recent years, numerous developments have been made in the fields of conventional ED and BED. The implementation of these technologies into processes for the purification or production of organic molecules has, among other benefits, led to a reduction in saline effluents typically produced using ion exchange resins, or has minimized the need for solvents by replacing liquid-liquid extraction in certain scenarios. Saline effluents, which are often difficult to process in waste treatment facilities, can be repurposed into acids and bases through BED, which also limits the consumption of these reagents in the process. Numerous recent reviews discuss the advancements in water treatment (advancements in brackish and seawater desalination [54,409,410], energy generation through reverse ED [411-413]) and food (salt adjustment, acidification in sugar, beverages, and whey treatment [218,414-417]) applications, which remain today the major applications of this technology. We have chosen to present only the applications in more niche areas to highlight the potential for industrialization of this technology.

Table 1 summarizes the energy consumption by mass of the various products obtained by ED, depending on the application and compares them with ‘conventional processes’.

### 7.1. Gas desorption

ED could emerge as a significant technological advancement for the separation of gases in various applications, such as extracting $CO_2$ from the atmosphere (~400 ppm), isolating $CO_2$ and $SO_2$ from flue gases (and more recently removing $NH_3$ from ammonium sulfate [444,445]). The conditions under which the process operates frequently lead in gases desorbing within the membrane stack of the ED system. This phenomenon results in the entrapment of gas bubbles on the membrane surfaces, diminishing the available membrane surface area for the process. Consequently, this leads to an increase in resistance and the formation of localized areas of elevated current density. These hot spots can cause damage to the membranes, ultimately shortening their lifespan and compromising the system’s efficiency. In fact, an amount of work is being done to improve these separation processes.

#### 7.1.1. $CO_2$ capture

While numerous pilot projects have been initiated in recent years, amine scrubbing remains the sole technique for capturing $CO_2$ that has seen widespread application at the industrial level. This method, a post-combustion strategy, employs amine-based chemicals (e.g., mono-ethanolamine - MEA) to capture carbon dioxide from flue gases. Subsequently, the absorbed $CO_2$ is separated from the solvent through a regeneration step, which is facilitated either by altering the temperature or by changing the pressure (requiring energy that may emit additional greenhouse gases [446]). The process allows for the reuse of the solvent, and the desorbed $CO_2$ is prepared for transportation and storage. The primary challenges associated with amine scrubbing involve the

<footnote n="1">¹ i.e., labor cost, membrane replacement cost, maintenance cost, chemical cost, and insurance cost.</footnote>
However, advancements in one area often entail trade-offs in others. Thicker membrane may enhance mechanical strength and durability but also increase resistance to charge transport, thereby diminishing efficiency. Similarly, while a longer lifespan reduces the cost attributed to investment by diluting it over time, any decrease in efficiency can lead to elevated operational costs due to increased energy consumption. Fig. 21 illustrates the beneficial or detrimental impacts that specific independent decisions, made during the manufacturing and research phases, exert on each of the aforementioned dimensions. Furthermore, the selection of materials, particularly catalysts and IEMs, plays a vital role in balancing efficiency and durability. More active functional groups of IEMs (see Section 3), can boost efficiency but may face challenges in maintaining stability over time. The pursuit of catalysts devoid of critical raw materials aims at sustaining high efficiency levels; however, it frequently results in materials that are less durable and more susceptible to degradation. Efficiency and selectivity are often conflicting dimensions. An increase in the thickness of the membrane will lead to better selectivity but will increase the ohmic over-potential of the membrane and therefore decrease its efficiency. Once again, it is all a matter of compromise. These dynamics underscore the complex interplay between various factors in the development of green ED technologies at large scale.

Concerning the TEA itself, the economic assessment of the cost per cubic meter produced necessitates calculating both the investment and operating costs. These parameters are derived from factors such as membrane surface area, flow rate, recovery rate, cell velocity, and outlet concentrations. Capital costs encompass process-related expenses (including pre-treatment and treatment), building construction, and auxiliary equipment. Operating costs cover consumables, energy expenses, and maintenance of installations (such as membrane and electrode replacements). Interestingly, few studies have ventured into the TEA of the ED process, which may indicate a certain impediment to the development of this technology on a larger scale. However, it should be noted that among the niche studies addressing TEA, most focus on applications in the field of water treatment [397-399]. For example, the works of Generous et al. [93] (28.6 %, 24.9 %, and 46.5 % of unit product water costs are contributed by power, fixed, and other charges<fnref n="1" />) and Ankoliya et al. [400] (cost of acid and base produced is comparable with the commercially available reagent) have demonstrated the benefits of this technology over conventional process. Studies applied to very specific case studies are emerging, such as those for the production of $H_3Co(CN)_6$ [262], the capture of $CO_2$ [401] or the coupling of BED with photovoltaic energy source [402].

### 6.2.2. Life cycle assessment

The challenges in reducing the environmental impact of production processes, via separation unit operation, present even greater opportunities for physical methods like ED compared to more polluting chemically physical methods (e.g., liquid-liquid extraction, precipitation, etc.). In the same way as TEAs, LCAs are good indicators of the maturity advancement of a technology. These data-intensive analyses enable us to approximate industrial operating conditions as closely as possible (e.g., inputs such as flow rate, chemicals, power, heat demand, and outputs such as product and waste specifications) and necessitate the fixing of selected environmental impacts (e.g., climate change through greenhouse gas emission quantification, discharge through mass balance, etc.) for each specific study.

A similar behavior to that of TEAs is observed for LCAs. Indeed, the majority of LCAs presented in the literature focus more on wastewater treatment applications. LCAs of wastewater treatment by ED primarily estimate carbon and water footprint distribution, with the objective of technology scale-up [4,403,404]. These studies include the work of

Alrashidi et al. [405], which shows that in addition to frequent membrane replacements, membrane costs contribute significantly (50 % of equipment cost). Another study by Akula et al. [406] demonstrates that the use of BED is more eco-efficient than conventional processes for nitrate removal from wastewater.

However, ED is inherently energy-consuming technologies via external bias application. This results in indirect environmental impacts due to the emission of various air pollutants from conventional energy production processes, contributing to the greenhouse effect. For example, estimates indicate that estimated carbon footprint of seawater desalination is around $0.4 - 6.7 \text{ kgCO}_2\text{eq} \text{ m}^{-3}$ [407] (large gap due to variability in location, technologies, life cycle stages, etc.). This impact can be reduced via the implementation of renewable energy sources [408].

## 7. Diverse applications and case studies

In recent years, numerous developments have been made in the fields of conventional ED and BED. The implementation of these technologies into processes for the purification or production of organic molecules has, among other benefits, led to a reduction in saline effluents typically produced using ion exchange resins, or has minimized the need for solvents by replacing liquid-liquid extraction in certain scenarios. Saline effluents, which are often difficult to process in waste treatment facilities, can be repurposed into acids and bases through BED, which also limits the consumption of these reagents in the process. Numerous recent reviews discuss the advancements in water treatment (advancements in brackish and seawater desalination [54,409,410], energy generation through reverse ED [411-413]) and food (salt adjustment, acidification in sugar, beverages, and whey treatment [218,414-417]) applications, which remain today the major applications of this technology. We have chosen to present only the applications in more niche areas to highlight the potential for industrialization of this technology.

Table 1 summarizes the energy consumption by mass of the various products obtained by ED, depending on the application and compares them with 'conventional processes'.

### 7.1. Gas desorption

ED could emerge as a significant technological advancement for the separation of gases in various applications, such as extracting $CO_2$ from the atmosphere (~400 ppm), isolating $CO_2$ and $SO_2$ from flue gases (and more recently removing $NH_3$ from ammonium sulfate [444,445]). The conditions under which the process operates frequently lead in gases desorbing within the membrane stack of the ED system. This phenomenon results in the entrapment of gas bubbles on the membrane surfaces, diminishing the available membrane surface area for the process. Consequently, this leads to an increase in resistance and the formation of localized areas of elevated current density. These hot spots can cause damage to the membranes, ultimately shortening their lifespan and compromising the system's efficiency. In fact, an amount of work is being done to improve these separation processes.

#### 7.1.1. $CO_2$ capture

While numerous pilot projects have been initiated in recent years, amine scrubbing remains the sole technique for capturing $CO_2$ that has seen widespread application at the industrial level. This method, a post-combustion strategy, employs amine-based chemicals (e.g., mono-ethanolamine - MEA) to capture carbon dioxide from flue gases. Subsequently, the absorbed $CO_2$ is separated from the solvent through a regeneration step, which is facilitated either by altering the temperature or by changing the pressure (requiring energy that may emit additional greenhouse gases [446]). The process allows for the reuse of the solvent, and the desorbed $CO_2$ is prepared for transportation and storage. The primary challenges associated with amine scrubbing involve the

<footnote n="1">¹ i.e., labor cost, membrane replacement cost, maintenance cost, chemical cost, and insurance cost.</footnote>
<caption>**Table 1**</caption>

Summary and comparison table of energy consumption in relation to the products of interest *via* ED and ‘conventional process’. Note that the SEC can vary significantly depending on factors such as experimental conditions, process configuration, and inlet/outlet concentration solution.

<table><thead><tr><th>Product obtained<br>via ED</th><th>SEC[kWhkg<sup>-1</sup>product]</th><th>Product<br>specifications</th><th>Ref.</th><th>Product obtained<br>via 'conventional<br>process'</th><th>SEC[kWhkg<sup>-1</sup>product]</th><th>Product<br>specifications</th><th>Ref.</th></tr></thead><tbody><tr><td rowspan="4">CO<sub>2</sub> capture</td><td>in NaOH<br>solution</td><td>~ 0.3 kWh kg<sup>-1</sup><sub>CO<sub>2</sub></sub></td><td rowspan="2">[418]</td><td rowspan="4">CO<sub>2</sub> capture</td><td>in NaOH solution<br>(causticization)</td><td>~ 0.7<br>kWh kg<sup>-1</sup><sub>CO<sub>2</sub></sub></td><td>100 vol%<br>CO<sub>2</sub>[g],<br>from DAC</td><td rowspan="2">[419]</td></tr><tr><td>in KOH<br>solution</td><td>~ 1.5 kWh kg<sup>-1</sup><sub>CO<sub>2</sub></sub></td><td>in KOH solution</td><td>0.5 – 0.9<br>kWh kg<sup>-1</sup><sub>CO<sub>2</sub></sub></td><td>100 vol%<br>CO<sub>2</sub>[g],<br>from flue<br>gas</td></tr><tr><td>in seawater</td><td>~ 1.5 kWh kg<sup>-1</sup><sub>CO<sub>2</sub></sub></td><td>[421]</td><td>in amine solvent</td><td>0.8 – 1.1<br>kWh kg<sup>-1</sup><sub>CO<sub>2</sub></sub></td><td>100 vol%<br>CO<sub>2</sub>[g],<br>from flue<br>gas</td><td>[420]</td></tr><tr><td>in amine<br>solvent</td><td>~ 0.2 kWh kg<sup>-1</sup><sub>CO<sub>2</sub></sub></td><td>[422]</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SO<sub>2</sub> capture<br>(amine solvent)</td><td>~ 1.1 kWh kg<sup>-1</sup><sub>amine</sub></td><td>N.A.<sup>1</sup></td><td>[423]</td><td>SO<sub>2</sub> capture<br>(limestone-gypsum<br>wet desulfurization<br>process)</td><td>~ 3.8 kWh kg<sup>-1</sup><sub>SO<sub>2</sub></sub></td><td>N.A.</td><td>[424]</td></tr><tr><td rowspan="4">Metal<br>recovery</td><td>Lithium</td><td>~ 7.0 kWh kg<sup>-1</sup><sub>Li<sub>2</sub>CO<sub>3</sub></sub></td><td rowspan="4">[346]</td><td rowspan="4">Metal<br>recovery</td><td>Lithium<br>(Keliber process via<br>hard rock deposit)</td><td>~ 21.8<br>kWh kg<sup>-1</sup><sub>LiOH</sub></td><td>~99<br>%LiOH(s)</td><td rowspan="2">[425]</td></tr><tr><td>Silver</td><td>~ 9.2 × 10<sup>-2</sup><br>kWh kg<sup>-1</sup><sub>Ag</sub></td><td>Silver<br>(electro-winning)</td><td>~ 4.6<br>kWh kg<sup>-1</sup><sub>Ag</sub></td><td>99.9 % of<br>silver</td></tr><tr><td>Copper</td><td>~ 5.3 kWh kg<sup>-1</sup><sub>Cu</sub></td><td>Copper<br>(leaching and electro-<br>winning)</td><td>~ 4.5<br>kWh kg<sup>-1</sup><sub>Cu</sub></td><td>100 % of<br>copper</td><td>[427]</td></tr><tr><td>Chromium</td><td>10 – 20 kWh kg<sup>-1</sup><sub>CrO<sub>3</sub></sub></td><td>~ 57 % of CrO<sub>3</sub><br>recovery</td><td>Chromium<br>(leaching)</td><td>~ 0.6<br>kWh kg<sup>-1</sup><sub>Cr</sub></td><td>93 % of<br>leached<br>Cr<sup>(III)</sup></td><td>[429]</td></tr><tr><td rowspan="2">Na<sub>2</sub>SO<sub>4</sub> splitting</td><td></td><td>~ 1.6 kWh kg<sup>-1</sup><sub>Na<sub>2</sub>SO<sub>4</sub></sub></td><td rowspan="2">[432]</td><td rowspan="2">Acid<br>recovery</td><td>Hydrochloric acid<br>(chlor-alkali<br>diaphragm)</td><td>2.2 – 2.8<br>kWh kg<sup>-1</sup><sub>acid</sub></td><td>100 %HCl</td><td rowspan="2">[433]</td></tr><tr><td></td><td>Sulfuric acid<br>(double absorption<br>process)</td><td>~ 3.1<br>kWh kg<sup>-1</sup><sub>acid</sub></td><td>~98 wt%<br>of H<sub>2</sub>SO<sub>4</sub></td></tr><tr><td rowspan="2">Acid<br>recovery</td><td>Hydrochloric<br>acid</td><td>4.4 – 7.3 kWh kg<sup>-1</sup><sub>acid</sub></td><td rowspan="2">[6]</td><td rowspan="2">Organic acid<br>production</td><td>Acetic acid<br>(ethanol oxidation)</td><td>~ 59<br>kWh kg<sup>-1</sup><sub>acid</sub></td><td>~93 % of<br>acid yield</td><td rowspan="2">[436]</td></tr><tr><td>Nitric acid</td><td>~ 1.7 kWh kg<sup>-1</sup><sub>acid</sub></td><td>Oxalic acid<br>(CO<sub>2</sub> reduction)</td><td>~ 6<br>kWh kg<sup>-1</sup><sub>acid</sub></td><td>~75 % of<br>acid</td></tr><tr><td rowspan="5">Organic acid<br>production</td><td>Sulfuric acid</td><td>~ 1.7 kWh kg<sup>-1</sup><sub>acid</sub></td><td>[437]</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Acetic acid</td><td>3.8 – 15.7<br>kWh kg<sup>-1</sup><sub>acid</sub></td><td rowspan="3">[439]</td><td></td><td>Citric acid<br>(fermentation)</td><td>~ 2.0<br>kWh kg<sup>-1</sup><sub>acid</sub></td><td>~98 % of<br>acid</td><td rowspan="2">[440]</td></tr><tr><td>Oxalic acid</td><td>7.3 – 21.1<br>kWh kg<sup>-1</sup><sub>acid</sub></td><td></td><td>Lactic acid<br>(fermentation)</td><td>~ 6.8<br>kWh kg<sup>-1</sup><sub>acid</sub></td><td>99 % of<br>acid</td></tr><tr><td>Citric acid</td><td>8.1 – 22.7<br>kWh kg<sup>-1</sup><sub>acid</sub></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Lactic acid</td><td>~ 1.4 kWh kg<sup>-1</sup><sub>acid</sub></td><td>100 % of acid</td><td>[442]</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Tartaric acid</td><td>~ 8.8 kWh kg<sup>-1</sup><sub>acid</sub></td><td>100 % of acid</td><td>[443]</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

corrosive properties of the solvents and the substantial cost associated with the process, producing at the same time persistent and corrosive heat stable salts decreasing the reversibility of the process [447,448].

Given these limitations, there is a pressing need for research to pave the way for CO₂ capture technologies that are not only more effective but also more economical and decarbonated (~78% reduction in energy consumption in the case of amine solvents according to Table 1). Concerning SEC, it seems that the use of NaOH or amine solvents are the most energetically viable solutions to develop. Typically, EC CO₂ separation involves a two-stage procedure using a liquid electrolyte to absorb and release CO₂, as depicted in Fig. 22, with IEMs facilitating the regeneration of the electrolyte [449].

The capture and release of CO₂ can be effectively managed through the EC modulation of absorbent proton concentration, acting as the central mechanism. This method, often termed as ‘pH-swing’, capitalizes

on the proton-coupled EC reactions (e.g., $X + ne^{-} + nH^{+} \rightarrow XH_{n}$) to adjust the absorbent’s pH. Such approaches exploit the pH-dependent thermodynamic equilibrium of CO₂ speciation in aqueous environments. An applied voltage across a series of monopolar and BM membranes initiates the CO₂ capture by generating OH⁻ ions and facilitates its release by producing H⁺ [451,452]. Nevertheless, the presence of air bubbles within a BM reactor can diminish the usable membrane surface area and elevate the ohmic resistance across the stack, leading to an accumulation of heat on the membrane’s surface. These challenges can be effectively mitigated by funneling the acidic solution through the hydrophobic side of a hollow fiber membrane reactor [453].

The development of the ED system aims at the CO₂ separation from both significant point sources, like power plant flue gases [454], and directly from the atmosphere (direct air capture, DAC) [401,455], with results suggesting the feasibility of scaling up this process (e.g., decrease

<footnote n="1">¹N.A.: Not Applicable.</footnote>
<caption>**Table 1**</caption>

Summary and comparison table of energy consumption in relation to the products of interest *via* ED and ‘conventional process’. Note that the SEC can vary significantly depending on factors such as experimental conditions, process configuration, and inlet/outlet concentration solution.

<table><thead><tr><th>Product obtained via ED</th><th>SEC[kWhkg<sup>-1</sup>product]</th><th>Product specifications</th><th>Ref.</th><th>Product obtained via 'conventional process'</th><th>SEC[kWhkg<sup>-1</sup>product]</th><th>Product specifications</th><th>Ref.</th></tr></thead><tbody><tr><td>CO<sub>2</sub> capture</td><td>in NaOH solution</td><td>~ 0.3 kWh kg<sup>-1</sup><sub>CO<sub>2</sub></sub></td><td>[418]</td><td>CO<sub>2</sub> capture</td><td>in NaOH solution (causticization)</td><td>~ 0.7 kWh kg<sup>-1</sup><sub>CO<sub>2</sub></sub></td><td>100 vol% CO<sub>2</sub>[g], from DAC</td></tr><tr><td>in KOH solution</td><td>~ 1.5 kWh kg<sup>-1</sup><sub>CO<sub>2</sub></sub></td><td>100 vol% CO<sub>2</sub>[g], from DAC</td><td>[401]</td><td>in KOH solution</td><td>0.5 – 0.9 kWh kg<sup>-1</sup><sub>CO<sub>2</sub></sub></td><td>100 vol% CO<sub>2</sub>[g], from flue gas</td><td>[420]</td></tr><tr><td>in seawater</td><td>~ 1.5 kWh kg<sup>-1</sup><sub>CO<sub>2</sub></sub></td><td>100 vol% CO<sub>2</sub>[g], from seawater</td><td>[421]</td><td>in amine solvent</td><td>0.8 – 1.1 kWh kg<sup>-1</sup><sub>CO<sub>2</sub></sub></td><td>100 vol% CO<sub>2</sub>[g], from flue gas</td><td>[418]</td></tr><tr><td>in amine solvent</td><td>~ 0.2 kWh kg<sup>-1</sup><sub>CO<sub>2</sub></sub></td><td>100 vol% CO<sub>2</sub>[g], from flue gas</td><td>[422]</td><td></td><td></td><td></td><td></td></tr><tr><td>SO<sub>2</sub> capture (amine solvent)</td><td>~ 1.1 kWh kg<sup>-1</sup><sub>amine</sub></td><td>N.A.<sup>1</sup></td><td>[423]</td><td>SO<sub>2</sub> capture (limestone-gypsum wet desulfurization process)</td><td>~ 3.8 kWh kg<sup>-1</sup><sub>SO<sub>2</sub></sub></td><td>N.A.</td><td>[424]</td></tr><tr><td>Metal recovery</td><td>Lithium</td><td>~ 7.0 kWh kg<sup>-1</sup><sub>Li<sub>2</sub>CO<sub>3</sub></sub></td><td>[346]</td><td>Metal recovery</td><td>Lithium (Keliber process via hard rock deposit)</td><td>~ 21.8 kWh kg<sup>-1</sup><sub>LiOH</sub></td><td>~ 99 %LiOH(s)</td></tr><tr><td></td><td>Silver</td><td>~ 9.2 × 10<sup>-2</sup> kWh kg<sup>-1</sup><sub>Ag</sub></td><td>[426]</td><td></td><td>Silver (electro-winning)</td><td>~ 4.6 kWh kg<sup>-1</sup><sub>Ag</sub></td><td>99.9 % of silver</td></tr><tr><td></td><td>Copper</td><td>~ 5.3 kWh kg<sup>-1</sup><sub>Cu</sub></td><td>[428]</td><td></td><td>Copper (leaching and electro-winning)</td><td>~ 4.5 kWh kg<sup>-1</sup><sub>Cu</sub></td><td>100 % of copper</td></tr><tr><td></td><td>Chromium</td><td>10 – 20 kWh kg<sup>-1</sup><sub>CrO<sub>3</sub></sub></td><td>[430]</td><td></td><td>Chromium (leaching)</td><td>~ 0.6 kWh kg<sup>-1</sup><sub>Cr</sub></td><td>93 % of leached Cr<sup>(III)</sup></td></tr><tr><td>Na<sub>2</sub>SO<sub>4</sub> splitting</td><td>~ 1.6 kWh kg<sup>-1</sup><sub>Na<sub>2</sub>SO<sub>4</sub></sub></td><td>99 % NaOH + 90 % of H<sub>2</sub>SO<sub>4</sub></td><td>[432]</td><td>Acid recovery</td><td>Hydrochloric acid (chlor-alkali diaphragm)</td><td>2.2 – 2.8 kWh kg<sup>-1</sup><sub>acid</sub></td><td>100 %HCl</td></tr><tr><td>Acid recovery</td><td>Hydrochloric acid</td><td>4.4 – 7.3 kWh kg<sup>-1</sup><sub>acid</sub></td><td>[6]</td><td></td><td>Sulfuric acid (double absorption process)</td><td>~ 3.1 kWh kg<sup>-1</sup><sub>acid</sub></td><td>~ 98 wt% of H<sub>2</sub>SO<sub>4</sub></td></tr><tr><td></td><td>Nitric acid</td><td>~ 1.7 kWh kg<sup>-1</sup><sub>acid</sub></td><td>[435]</td><td>Organic acid production</td><td>Acetic acid (ethanol oxidation)</td><td>~ 59 kWh kg<sup>-1</sup><sub>acid</sub></td><td>~ 93 % of acid yield</td></tr><tr><td></td><td>Sulfuric acid</td><td>~ 1.7 kWh kg<sup>-1</sup><sub>acid</sub></td><td>[437]</td><td></td><td>Oxalic acid (CO<sub>2</sub> reduction)</td><td>~ 6 kWh kg<sup>-1</sup><sub>acid</sub></td><td>~ 75 % of acid</td></tr><tr><td>Organic acid production</td><td>Acetic acid</td><td>3.8 – 15.7 kWh kg<sup>-1</sup><sub>acid</sub></td><td>[439]</td><td></td><td>Citric acid (fermentation)</td><td>~ 2.0 kWh kg<sup>-1</sup><sub>acid</sub></td><td>~ 98 % of acid</td></tr><tr><td></td><td>Oxalic acid</td><td>7.3 – 21.1 kWh kg<sup>-1</sup><sub>acid</sub></td><td></td><td></td><td>Lactic acid (fermentation)</td><td>~ 6.8 kWh kg<sup>-1</sup><sub>acid</sub></td><td>99 % of acid</td></tr><tr><td></td><td>Citric acid</td><td>8.1 – 22.7 kWh kg<sup>-1</sup><sub>acid</sub></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>Lactic acid</td><td>~ 1.4 kWh kg<sup>-1</sup><sub>acid</sub></td><td>[442]</td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>Tartaric acid</td><td>~ 8.8 kWh kg<sup>-1</sup><sub>acid</sub></td><td>[443]</td><td></td><td></td><td></td><td></td></tr></tbody></table>

corrosive properties of the solvents and the substantial cost associated with the process, producing at the same time persistent and corrosive heat stable salts decreasing the reversibility of the process [447,448].

Given these limitations, there is a pressing need for research to pave the way for CO₂ capture technologies that are not only more effective but also more economical and decarbonated (~78% reduction in energy consumption in the case of amine solvents according to Table 1). Concerning SEC, it seems that the use of NaOH or amine solvents are the most energetically viable solutions to develop. Typically, EC CO₂ separation involves a two-stage procedure using a liquid electrolyte to absorb and release CO₂, as depicted in Fig. 22, with IEMs facilitating the regeneration of the electrolyte [449].

The capture and release of CO₂ can be effectively managed through the EC modulation of absorbent proton concentration, acting as the central mechanism. This method, often termed as ‘pH-swing’, capitalizes

on the proton-coupled EC reactions (e.g., X + ne⁻ + nH⁺ → XHₐ) to adjust the absorbent’s pH. Such approaches exploit the pH-dependent thermodynamic equilibrium of CO₂ speciation in aqueous environments. An applied voltage across a series of monopolar and BM membranes initiates the CO₂ capture by generating OH⁻ ions and facilitates its release by producing H⁺ [451,452]. Nevertheless, the presence of air bubbles within a BM reactor can diminish the usable membrane surface area and elevate the ohmic resistance across the stack, leading to an accumulation of heat on the membrane’s surface. These challenges can be effectively mitigated by funneling the acidic solution through the hydrophobic side of a hollow fiber membrane reactor [453].

The development of the ED system aims at the CO₂ separation from both significant point sources, like power plant flue gases [454], and directly from the atmosphere (direct air capture, DAC) [401,455], with results suggesting the feasibility of scaling up this process (e.g., decrease

<footnote n="1">¹N.A.: Not Applicable.</footnote>
<figure><img src="image_24.png" /><figcaption>**Fig. 22.** Diagram illustrating the 'pH-swing' via ED for the purpose of carbon capture. Reproduced from [450] with permission from the Royal Society of Chemistry.</figcaption></figure>

of energy consumption [456,457], carbon-neutral liquid fuel synthesis [421], etc.). Despite exploring various membrane configurations and electrolyte choices [458–461], the separation costs associated with ED are anticipated to be considerable, owing primarily to the need for multiple IEMs.

### 7.1.2. SO₂ separation

Analogous to CO₂ capture, flue-gas desulfurization plays crucial role in mitigating SO₂ emissions. BM process has been utilized for the removal and enrichment of SO₂-rich acid gases, achieved by acidifying the solution through water dissociation within the BM, which in turn enhances the rate at which these gases are desorbed via a 'pH-swing' and this application have been studied for the SO₂ desorption [462,463]. Initial studies suggest a reduction of about ~70 % in energy consumption when using the ED process.

Alkanolamines serve as a prevalent choice for the desulfurization (and decarbonization) of various fuel gases, including natural gas and synthesis gases derived from the gasification of coal and heavy oils. Nonetheless, these fuel gases often contain SO₃, which can react with

alkanolamines to produce heat-stable alkanolamine sulfates (—(RH)₂SO₄) that resist simple thermal regeneration, increasing process irreversibility. Without intervention, these compounds accumulate, leading to a reduction in both the efficiency of desulfurization processes and the lifetime of the amine absorbents. Huang et al. demonstrated the regeneration of three alkanolamines through the application of BED. Their investigation focused on how various operational parameters, such as electrolyte concentration, the concentration of alkanolamine sulfate, and current density, influenced the regeneration process [423].

## 7.2. Hydrometallurgy applications

Numerous reviews specific to the application of ED in hydrometallurgy and metal recovery are presented in the literature [59,168,464]. In the hydrometallurgical industry, endeavors utilizing ED typically align with one of two primary objectives: (i) the recovery of desired species or (ii) the elimination of undesirable species. The latter, unwanted species, can interfere with other segments of the process industry or pose concerns from an environmental standpoint.

### 7.2.1. Metal value recovery

Research indicates that ED can achieve recovery rates of 70–99 % for valuable metals, including gold, noble metals, rare-earth elements, copper, and silver [465].

#### 7.2.1.1. Li-ion battery recycling.
While mining remains vital for supplying the raw materials necessary for lithium-ion batteries (LiBs), recycling is poised to play a critical role in addressing the soaring demand for lithium, cobalt, nickel, manganese, and graphite, driven by the anticipated surge in electric vehicle production over the next decade. Currently, LiB recycling is predominantly conducted through pyrometallurgical methods [466]. According to Table 1, the use of ED could lead to a reduction of SEC by about ~67 %.

Incorporating ED into a global hydrometallurgical flowsheet, which also includes solvent extraction and precipitation, could enhance the recycling process of nickel manganese cobalt (NMC) materials, as depicted in Fig. 23. Following the leaching of the black mass with H₂SO₄ and H₂O₂ [26], Ni(II), Mn(II), and Co(II) could be selectively extracted and separated using Cyanex® 272 as the extractant [466,467]. The resulting effluent, containing Li(II) along with residual amounts of Ni(II), Mn(II) and Co(II), could then be further purified through ED to produce high-purity lithium salts [468].

However, several challenges must be addressed before fully implementing ED in the recycling of LiBs. Particularly, the use of highly selective membranes for Li(II) presents difficulties due to the disparate transport properties of Li(II) and divalent cations (Ni(II), Mn(II) and Co(II)). These differences can lead to the precipitation of divalent cations within the membrane's pores, which significantly reduces the limiting current

<figure><img src="image_25.png" /><figcaption>**Fig. 23.** Principles behind the use of ED in hydrometallurgical systems for generating high-grade lithium salts. Reprinted with permission from [466] (CC-BY MDPI).</figcaption></figure>
<figure><img src="image_25.png" /><figcaption>**Fig. 22.** Diagram illustrating the ‘pH-swing’ via ED for the purpose of carbon capture. Reproduced from [450] with permission from the Royal Society of Chemistry.</figcaption></figure>

of energy consumption [456,457], carbon-neutral liquid fuel synthesis [421], etc.). Despite exploring various membrane configurations and electrolyte choices [458–461], the separation costs associated with ED are anticipated to be considerable, owing primarily to the need for multiple IEMs.

### 7.1.2. SO₂ separation

Analogous to CO₂ capture, flue-gas desulfurization plays crucial role in mitigating SO₂ emissions. BM process has been utilized for the removal and enrichment of SO₂-rich acid gases, achieved by acidifying the solution through water dissociation within the BM, which in turn enhances the rate at which these gases are desorbed via a ‘pH-swing’ and this application have been studied for the SO₂ desorption [462,463]. Initial studies suggest a reduction of about ~70 % in energy consumption when using the ED process.

Alkanolamines serve as a prevalent choice for the desulfurization (and decarbonization) of various fuel gases, including natural gas and synthesis gases derived from the gasification of coal and heavy oils. Nonetheless, these fuel gases often contain SO₃, which can react with

alkanolamines to produce heat-stable alkanolamine sulfates (—(RH)₂SO₄) that resist simple thermal regeneration, increasing process irreversibility. Without intervention, these compounds accumulate, leading to a reduction in both the efficiency of desulfurization processes and the lifetime of the amine absorbents. Huang et al. demonstrated the regeneration of three alkanolamines through the application of BED. Their investigation focused on how various operational parameters, such as electrolyte concentration, the concentration of alkanolamine sulfate, and current density, influenced the regeneration process [423].

## 7.2. Hydrometallurgy applications

Numerous reviews specific to the application of ED in hydrometallurgy and metal recovery are presented in the literature [59,168,464]. In the hydrometallurgical industry, endeavors utilizing ED typically align with one of two primary objectives: (i) the recovery of desired species or (ii) the elimination of undesirable species. The latter, unwanted species, can interfere with other segments of the process industry or pose concerns from an environmental standpoint.

### 7.2.1. Metal value recovery

Research indicates that ED can achieve recovery rates of 70–99 % for valuable metals, including gold, noble metals, rare-earth elements, copper, and silver [465].

#### 7.2.1.1. Li-ion battery recycling.
While mining remains vital for supplying the raw materials necessary for lithium-ion batteries (LiBs), recycling is poised to play a critical role in addressing the soaring demand for lithium, cobalt, nickel, manganese, and graphite, driven by the anticipated surge in electric vehicle production over the next decade. Currently, LiB recycling is predominantly conducted through pyrometallurgical methods [466]. According to Table 1, the use of ED could lead to a reduction of SEC by about ~67 %.

Incorporating ED into a global hydrometallurgical flowsheet, which also includes solvent extraction and precipitation, could enhance the recycling process of nickel manganese cobalt (NMC) materials, as depicted in Fig. 23. Following the leaching of the black mass with H₂SO₄ and H₂O₂ [26], Ni(II), Mn(II), and Co(II) could be selectively extracted and separated using Cyanex® 272 as the extractant [466,467]. The resulting effluent, containing Li(II) along with residual amounts of Ni(II), Mn(II) and Co(II), could then be further purified through ED to produce high-purity lithium salts [468].

However, several challenges must be addressed before fully implementing ED in the recycling of LiBs. Particularly, the use of highly selective membranes for Li(II) presents difficulties due to the disparate transport properties of Li(II) and divalent cations (Ni(II), Mn(II) and Co(II)). These differences can lead to the precipitation of divalent cations within the membrane’s pores, which significantly reduces the limiting current

<figure><img src="image_26.png" /><figcaption>**Fig. 23.** Principles behind the use of ED in hydrometallurgical systems for generating high-grade lithium salts. Reprinted with permission from [466] (CC-BY MDPI).</figcaption></figure>
during ED, posing operational challenges (i.e., monitoring of $J_{lim}$ over time during ED operation) [466].

ED has also emerged as a sustainable alternative for lithium recovery from diverse sources such as brines, ores, and spent LiBs [469,470]. A novel development in this field is the application of ED technique that demonstrates high selectivity for lithium ($16 \text{ Wh g}^{-1}$ of Li) over magnesium in high Mg/L ratio brines [471]. However, the efficiency of lithium recovery can be significantly impacted by the presence of co-existing ions in solutions. Research indicates that increasing concentrations of certain co-existing ions ($Na^+$, $K^+$, $SO_4^{2-}$, $HCO_3^-$) can adversely affect the separation process of magnesium and lithium [265], highlighting the competitions occurring during the ED treatment in the presence of diverse ions. Moreover, the combination of precipitation and ED techniques for recovering lithium from spent LiBs presents an effective method for separating lithium and phosphorus ($Li_3PO_4$ precipitation) [472].

### 7.2.1.2. Other metals.
Recent research into the ED process for reclaiming metals such as Cu, Ag [426], Co, Zn, and Fe demonstrates varied outcomes and limitations based on the metal in question and the specific conditions of the process.

For example, Shestakov et al. focused on the recovery of iron, nickel, and copper from printed circuit board manufacture wastewater. The study highlighted the efficient separation of these metals using ion-exchange membranes, though it noted the differential retention efficiencies for $Ni^{2+}$ and $Cu^{2+}$ cations in multicomponent solutions compared to single-salt solutions [473]. Similarly, Zimmermann et al. explored the separation of copper from silver in electrowinning solutions by using monovalent-selective IEMs. They highlighted the potential for continuous ED to remove divalent cations like copper, thus maintaining high purity of the recycled silver. The study also pointed out the significant drawbacks of scaling and fouling [474]. Gomes et al. combined ED with bioleaching (recycling technique that uses bacteria to recover some metals) for enhanced metal recovery from municipal solid waste incineration fly ashes. This approach showed promising results in increasing the performance of metal removal and recovery, including Co and Zn [475].

Despite the promising outcomes, common limitations across these studies include the complexity of separating multicomponent solutions (with often close valence) and challenges related to membrane scaling and fouling.

### 7.2.2. Approaches for treating wastewater with metal contaminants

A multitude of works dealing with ED has shown significant promise in treating wastewater containing various metal contaminants during the past few years [59,476-478].

Separation of metals from electroplating wastewater using ED was explored, demonstrating the effectiveness of ED in treating such wastewater and recovering over 90 % of metals, specifically copper and nickel [479]. The study highlighted a limitation where high voltages led to the formation of white sediments due to high current density, indicating a threshold for operational voltage to avoid precipitation issues. Recently, Cerrillo-Gonzalez et al. explored the broader application of ED in metal removal from both water and wastewater [464]. They highlighted the technique's potential in not only treating wastewater but also in recovering critical metals, thereby contributing to a circular economy. The performance of ED, as noted in their review, depends on various factors including membrane properties, cell configuration, and operational conditions. Moreover, a combined approach using ED and electrocoagulation (i.e., coagulation of particles through electro-generated ions which neutralize the charge of pollutants) for the treatment of wastewater containing arsenic and copper was investigated. This innovative method achieved complete copper removal and substantial arsenic removal through ED, with further arsenic elimination via electrocoagulation [480]. Wang et al. focused on the treatment of

electroplating wastewater with low-concentration nickel, using a combination of ED and electrodeposition. They managed to reduce the nickel concentration to below 0.1 mg/L, meeting the most stringent discharge standards [481].

### 7.2.3. Na₂SO₄ splitting

Base-metal refining processes, such as nickel production, are significant sources of sodium sulfate waste. In these operations, it is common to generate approximately 2 kg of $Na_2SO_4$ for each kg of Ni produced [432]. This waste originates from the neutralization of sulfuric acid with NaOH in the leaching solutions after metal extraction. The production and disposal of $Na_2SO_4$ not only reflect an inefficient conversion of valuable chemicals—imported sulfur is turned into $H_2SO_4$, which is then neutralized with costly NaOH to produce a low-value product—but also pose environmental risks (e.g., soil salinization).

ED has emerged as a significant method for splitting $Na_2SO_4$, with various studies highlighting its potential and the challenges encountered. One of the noteworthy applications is the high-value conversion of $Na_2SO_4$ wastewater into $K_2SO_4$ solution (fertilizer) [482]. This work demonstrated the successful conversion of $Na_2SO_4$ to a $K_2SO_4$ solution with a concentration of 96.9 g/L and a purity of 95.2 % (limited mechanism caused by co-existing ion competitions). However, the presence of co-existing ions ($Cl^-$, $CO_3^{2-}$, $Mg^{2+}$, and $Ca^{2+}$) was found to influence the ED metathesis process, underscoring the complexity of handling mixed ion solutions, especially in terms of fouling and IEM stabilities. Another application involves the use of ED for sodium sulfate splitting in a base metal refinery, aiming to achieve a zero brine solution to prevent salinization [261,432,483]. Studies highlighted the potential of splitting $Na_2SO_4$ into NaOH, $H_2SO_4$, and water via SEC of ~1.6 kWh kg$^{-1}_{Na_2SO_4}$. The challenges identified included non-ideal membrane behavior and the effects of electro-osmosis, particularly at high concentration levels.

### 7.3. Recovery of other valuable products

ED has been effectively utilized in various recovery applications, showcasing its versatility and efficiency across different industries for producing valuable products [484,485].

In the realm of recovery from wastewater treatment, pilot-scale electrodialysis process has shown promise in recovering nutrients, such as N [486,487], P [464,488] and K [238,489,490], from wastewater with high current efficiency and low energy consumption. Furthermore, the recovery of acid (HCl [270,491], $HNO_3$ [272,435,492], $H_2SO_4$ [493,494], etc.) from process or waste streams through ED has been demonstrated to have high accuracy and the potential for energy optimization as highlighted by Table 1.

ED has also been explored for the recovery of dyes from solutions, demonstrating its potential in treating dye-laden wastewater and enabling dye reuse [354,495]. It should be noted that dyes are often not charged but purified of their saline impurities. The separation efficiency in this process is highly dependent on the molecular weight of the dye. Nevertheless, the fouling of IEMs is a significant issue, stemming from the natural electrostatic attraction between anionic (cationic) dyes and their respective anion (cation) exchange membranes. This accumulation notably impedes the transfer of ions, significantly reducing the effectiveness and, consequently, affecting the separation of dyes and inorganic salts in the treatment of dye-rich wastewater [496].

Moreover, ED has expanded its applications to include promising prospects in the pharmaceutical industry. It is particularly effective in treating wastewater containing pharmaceuticals, significantly retaining target micropollutants such as diclofenac, carbamazepine, and furosemide and limiting their transport to the concentrated ED product [497-499]. Additionally, ED has proven beneficial in the production of pharmaceutical substances, enhancing the purification process and increasing the quality of products like taurine, tuberculin, and cobalt