“…Some commonly used energy, throughput, and materials metrics in CDI are: the salt adsorption capacity (SAC, in mg/g of electrodes), average salt adsorption rate (ASAR, in mol/min/cm 2 or in mg/g/min), [13][14][15][16][17] energy consumption per mole of salt removed (in kJ/mol kJ/g, or kT /ion), [18][19][20][21] energy normalized adsorbed salt (ENAS, in µmol/J), 15,16,22 and specific energy consumption (SEC, in mg/J). 17,20 While these metrics may be insightful for the processes relevant to CDI, it would be beneficial to settle upon a minimumnecessary set of measures that reflect economically relevant desalination performance. Additionally, it is important to distinguish between throughput and energy consumption, as these different desalination aspects often trade off with one another.…”
In the growing field of capacitive deionization (CDI), a number of performance metrics have emerged to describe the desalination process. Unfortunately, the separation conditions under which these metrics are measured are often not specified, resulting in optimal performance at minimal removal. Here we outline a system of performance metrics and reporting conditions that resolves this issue. Our proposed system is based on volumetric energy consumption (Wh/m 3 ) and throughput productivity (L/h/m 2 ) reported for a specific average concentration reduction, water recovery, and feed salinity. To facilitate and rationalize comparisons between devices, materials, and operation modes, we propose a nominal standard testing condition of removing 5 mM from a 20 mM NaCl feed solution at 50% water recovery for CDI research. Using this separation, we compare the desalination performance of a flow-through electrode (fte-CDI) cell and a flow between membrane (fb-MCDI) device, showing how significantly different systems can be compared in terms of generally desirable desalination characteristics. In general, we find that performance analysis must be considered carefully so to not allow for ambiguous separation conditions or the maximization of one metric at the expense of another. Additionally, for context we discuss a number of important underlying performance indicators and cell characteristics that are not performance measures in and of themselves but can be examined to better understand differences in performance.
“…Some commonly used energy, throughput, and materials metrics in CDI are: the salt adsorption capacity (SAC, in mg/g of electrodes), average salt adsorption rate (ASAR, in mol/min/cm 2 or in mg/g/min), [13][14][15][16][17] energy consumption per mole of salt removed (in kJ/mol kJ/g, or kT /ion), [18][19][20][21] energy normalized adsorbed salt (ENAS, in µmol/J), 15,16,22 and specific energy consumption (SEC, in mg/J). 17,20 While these metrics may be insightful for the processes relevant to CDI, it would be beneficial to settle upon a minimumnecessary set of measures that reflect economically relevant desalination performance. Additionally, it is important to distinguish between throughput and energy consumption, as these different desalination aspects often trade off with one another.…”
In the growing field of capacitive deionization (CDI), a number of performance metrics have emerged to describe the desalination process. Unfortunately, the separation conditions under which these metrics are measured are often not specified, resulting in optimal performance at minimal removal. Here we outline a system of performance metrics and reporting conditions that resolves this issue. Our proposed system is based on volumetric energy consumption (Wh/m 3 ) and throughput productivity (L/h/m 2 ) reported for a specific average concentration reduction, water recovery, and feed salinity. To facilitate and rationalize comparisons between devices, materials, and operation modes, we propose a nominal standard testing condition of removing 5 mM from a 20 mM NaCl feed solution at 50% water recovery for CDI research. Using this separation, we compare the desalination performance of a flow-through electrode (fte-CDI) cell and a flow between membrane (fb-MCDI) device, showing how significantly different systems can be compared in terms of generally desirable desalination characteristics. In general, we find that performance analysis must be considered carefully so to not allow for ambiguous separation conditions or the maximization of one metric at the expense of another. Additionally, for context we discuss a number of important underlying performance indicators and cell characteristics that are not performance measures in and of themselves but can be examined to better understand differences in performance.
“…The electrical SEC of pervaporation technology indicated a value < 0.3 kWh/m 3 , but a considerable thermal energy was required for heating and maintaining the feed stream [12]. Various models suggested have shown that CDI could operate with a SEC of less than 1 kWh/m 3 for low-salinity brackish water but remains less energy-efficient than RO [13][14][15]. In a pilot 1-kW photovoltaic (PV)-powered membrane CDI system for treating 6700-µS/cm brackish water, the system exhibited a low SEC (the sum of battery, pump and power supply associated with an electrode) in the range of 0.7-1.1 kWh/m 3 for producing 5-m 3 /d potable water [16].…”
The potential for lithium-ion (Li-ion) batteries and supercapacitors (SCs) to overcome long-term (one day) and short-term (a few minutes) solar irradiance fluctuations with high-temporal-resolution (one s) on a photovoltaic-powered reverse osmosis membrane (PV-membrane) system was investigated. Experiments were conducted using synthetic brackish water (5-g/L sodium chloride) with varied battery capacities (100, 70, 50, 40, 30 and 20 Ah) to evaluate the effect of decreasing the energy storage capacities. A comparison was made between SCs and batteries to determine system performance on a “partly cloudyday”. With fully charged batteries, clean drinking water was produced at an average specific energy consumption (SEC) of 4 kWh/m3. The daily water production improved from 663 L to 767 L (16% increase) and average electrical conductivity decreased from 310 µS/cm to 274 μS/cm (12% improvement), compared to the battery-less system. Enhanced water production occurred when the initial battery capacity was >50 Ah. On a “sunny” and “very cloudy” day with fully charged batteries, water production increased by 15% and 80%, while water quality improved by 18% and 21%, respectively. The SCs enabled a 9% increase in water production and 13% improvement in the average SEC on the “partly cloudy day” when compared to the reference system performance (without SCs).
“…It is a critical problem to find the trade‐off relationship between desalination performance and energy efficiency for the multi‐cells in the adsorption/desorption process. [ 30 ] So far, the impacts of the desalination processes with different assembling mode on the desalination performance and energy consumption have not been systematically reported.…”
To achieve a fluid uniform distribution, novel liquid distributors are introduced into a membrane capacitive deionization (MCDI) cell. Herein, three fluid distributors with different structures are proposed, primarily including flow channels and distributed baffles. The effects of each liquid distributor on the fluid distribution are evaluated through computational fluid dynamics (CFD) software. As a result, Liquid distributor‐3 is demonstrated to be optimal, which can facilitate the fluid uniform distribution and relieve the impact of the inlet flow rate on the fluid distribution. Correspondingly, the adsorption capacity reaches up to 23.8 mg·g−1 for this novel MCDI cell under the 2000 mg·L−1 NaCl solution. To find a trade‐off between desalination performance and energy efficiency, a modified MCDI system based on two novel MCDI cells and three valves is established by analyzing three adsorption/desorption processes, including Process 1 (both adsorption and desorption in parallel), Process 2 (adsorption in series and desorption in parallel), and Process 3 (both adsorption and desorption in series). The best performance is obtained by Process 1, where the current efficiency increases by about 25% in the adsorption process and by about 37% during the desorption process compared with Process 3. This study lays a foundation for the commercial application of multicomponent MCDI technology.
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