Energy breakdown in capacitive deionization
Graphical abstract
Introduction
Energy has traditionally been the dominant cost component for many desalination systems such as those applying distillation, which is highly energy intensive (Anderson et al., 2010). Reverse osmosis (RO) has dramatically reduced the energy requirements for desalination, with modern systems achieving roughly 50% energy efficiency for treating seawater based on the thermodynamic ideal free energy of mixing (Elimelech and Phillip, 2011). However, RO fares significantly worse for water with lower concentrations of dissolved solids, such as brackish water, where it only reaches 10% or less efficiency (Shrivastava et al., 2014). RO forces all treated water through the active membrane, with energy losses (and plant size) roughly corresponding to the total throughput of the plant.
Capacitive deionization (CDI) is a method of desalination that directly acts on the ions in solution and sequesters them into electric double layers leaving purified water, which is flushed from the cell. CDI has been investigated in various forms for over 50 years (Blair and Murphy, 1960, Johnson and Newman, 1971), but has recently seen a rapid increase in activity. Because the ions themselves are directly targeted, the energy consumption of this technique largely scales with the amount of salt removed (i.e. throughput times input concentration). This scaling promises higher energy efficiency for CDI compared to competing technologies when treating waters with lower dissolved solid concentrations than seawater (e.g. brackish water) (Zhao et al., 2013). There are a variety of operational parameters that can be tuned for CDI, including time dependence of charging voltage or current, level of cell charging (i.e. final cell voltage), and flow rate. The choice of these can dramatically influence the energy efficiency achieved in operation, and a consistent framework for determining optimal conditions for operation of CDI cells is still lacking.
We note that electric double layer capacitors, or supercapacitors, rely on very similar physics to CDI and have been optimized to maximize charge/discharge cycle efficiency and energy storage density. A number of studies have looked at the loss mechanisms present in supercapacitors (Conway, 2013), including series resistance (Conway and Pell, 2002, Yang and Zhang, 2013), charge redistribution loss, and parasitic reaction loss (Ike et al., 2016). However, the design and operational regimes of supercapacitors are very different than CDI. Importantly, there is generally no electrolyte flow, and organic solvent based, high concentration electrolytes are commonly used to achieve high operating voltage windows and minimize resistance. The goal of supercapacitor operation is solely the storage and recovery of energy. Further, supercapacitors are often applied in high current applications, and this requires a focus on series resistive losses. This focus has led to substantial supercapacitor optimization and sub-milliohm equivalent series resistances are commonly achieved (Yu et al., 2013).
The promise of CDI for energy efficient processing of lower concentration inlet feeds has led to a number of studies concerning energy loss (Alvarez-Gonzalez et al., 2016, Choi, 2015, Demirer et al., 2013; Dykstra et al., 2016, García-quismondo et al., 2015, García-Quismondo et al., 2013, Kang et al., 2014, Zhao et al., 2012a, Zhao et al., 2012b). These have generally focused on the total energy loss of the process, which is useful for comparison with different technologies or among different CDI designs, but provides little insight for optimizing CDI operation or refining current CDI designs. One element that has been studied in some detail is the choice of operation of CDI cells with constant current charging versus constant voltage charging (Choi, 2015, Kang et al., 2014, Zhao et al., 2012a). Constant current operation generally leads to superior energy performance with energy usage reduced by up to 30% (Kang et al., 2014), and some studies have dealt with the specific mechanisms of loss operative in CDI. Alvarez-Gonzalez et al. (2016) developed a simple model accounting for resistive and parasitic losses consisting of series and parallel resistances and parameterized this model using experimental data. They then optimized cell geometry and charging current in terms of cell energy loss using this model and showed good agreement with experiments. Detailed studies have also been conducted on the series resistance of CDI cells, e.g. (Qu et al., 2015). Improved understanding of the constituent energy loss mechanisms in CDI offers the opportunity for more efficient operation of existing cells and improved future designs, and hence, motivates this work.
Here, we experimentally quantify the specific energy loss mechanisms operative during CDI with constant current charging. These mechanisms separate roughly into those dominant at high or low charging currents. The mechanisms dominant at high currents motivate slow charging of the cell. We attribute these losses mostly to resistive dissipation during charge and discharge and, to a lesser degree, redistribution of accumulated charge within electrodes. We perform in situ, real-time measurements of cell series resistance as a function of charging current and time within the charging phase. The dominant losses at low charging currents, corresponding to parasitic currents in the cell, prompt acceleration of the charge phase and a reduction of charge time. We perform an independent set of constant voltage experiments to measure parasitic currents vs. cell voltage. We characterize both loss categories over a broad operational parameter space and show that balancing these losses leads to optimal energy efficiency. Total salt removed per cycle is another key parameter for CDI operation. We define two figures of merit (FOMs) relevant for practical CDI operation and plant design, salt removed per unit time and salt removed per unit energy. These provide quantitative metrics for evaluating tradeoffs between operational requirements (e.g. throughput vs. energy efficiency). We also provide relations for the investigated CDI cell identifying regimes of charging current and maximum cell voltage which allow a balance between cell throughput and energy efficiency as quantified by the product of salt removal rate and salt removed per unit energy.
Section snippets
CDI cell design
Fig. 1a shows a schematic of our radial flow-between CDI (fbCDI) cell. We fabricated the cell using five pairs of activated carbon electrodes (two of which are shown here) with 6 cm diameter and 270 μm thickness and total dry mass of 4.3 g. The electrode material (Materials & Methods, PACMM™ 203, Irvine, CA) has been used and characterized for CDI applications extensively and is well described(Biesheuvel et al., 2016, Dykstra et al., 2016, Zhao et al., 2012b). We stacked the electrodes between
Voltage profile and energy breakdown
Fig. 2a shows voltage profiles of our cell vs. time with 200 mA charge/discharge current and limit voltage of Vmax= 1.2 V and 2 mL min−1 flow rate (under DSS condition). Solid curve shows voltage measured by the sourcemeter and denoted as Vext. Dashed curve corresponds to underlying “equivalent capacitance” voltage (Vcap), the total voltage difference across the electrodes excluding voltage drop across the series resistance. We term this Vcap as an analogy to the equivalent RC circuit shown as
Conclusions
We have quantified individual loss mechanisms operative during CDI charging and discharging, and characterized their dependence on the parameters of charging current and maximum cell voltage. We identified losses dependent on cell voltage attributable to parasitic currents and losses depending on charging rate, which are dominated by cell resistances. We measured series resistance for the cell throughout charge/discharge phases for a range of input solute concentrations and a variety of
Acknowledgements
This work was supported jointly by LLNL LDRD project 15-ERD-068 and TomKat Center for Sustainable Energy at Stanford University. Work at LLNL was performed under the auspices of the US DOE by LLNL under Contract DE-AC52-07NA27344. A.H. gratefully acknowledges the support from the Stanford Graduate Fellowship program of Stanford University. J.G.S. and A.H. also gratefully acknowledge support from TomKat Center for Sustainable Energy as part of the Distributed Production and Energy Generation
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