Removal of Trace Uranium from Groundwaters Using Membrane Capacitive Deionization Desalination for Potable Supply in Remote Communities: Bench, Pilot, and Field Scale Investigations.

The performance of membrane capacitive deionization (MCDI) desalination was investigated at bench, pilot, and field scales for the removal of uranium from groundwater. It was found that up to 98.9% of the uranium can be removed using MCDI from a groundwater source containing 50 µg/L uranium, with the majority (94.5%) being retained on the anode. Uranium was found to physiochemically adsorb to the electrode without the application of a potential by displacing chloride ions, with 16.6% uranium removal at the bench scale via this non-electrochemical process. This displacement of chloride did not occur during the MCDI adsorption phase with the adsorption of all ions remaining constant during a time series analysis on the pilot unit. For the scenarios tested on the pilot unit, the flowrate of the product water ranged from 0.15 to 0.23 m3/h, electrode energy consumption from 0.28 to 0.51 kW h/m3, and water recovery from 69 to 86%. A portion (13-53% on the pilot unit) of the uranium was found to remain on the electrodes after the brine discharge phase with conventional cleaning techniques unable to release this retained uranium. MCDI was found to be a suitable means to remove uranium from groundwater systems though with the need to manage the accumulation of uranium on the electrodes over time.

Machine learning modelling of a membrane capacitive deionization (MCDI) system for prediction of long-term system performance and optimization of process control parameters in remote brackish water desalination.

Membrane Capacitive Deionization (MCDI) is a promising electrochemical technique for water desalination. Previous studies have confirrmed the effectiveness of MCDI in removing contaminants from brackish groundwaters, especially in remote areas where electricity is scarce. However, as with other water treatment technologies, performance deterioration of the MCDI system still occurs, hindering the stability of long-term operation. Herein, a machine learning (ML) modelling framework and various ML models were developed to (i) investigate the performance deterioration due particularly to insufficient charging/discharging of the electrode caused by accumulation of ions and electrode scaling and (ii) optimise MCDI operating parameters such that the impacts of these deleterious effects on unit performance were minimized. The ML models developed in this work exhibited a prediction accuracy of cycle time with average mean absolute percentage error (MAPE) values of 16.82% and 16.09% after 30-fold cross validation for Random Forest (RF) and Multilayer Perceptron (MLP) models respectively. The pre-trained ML model predicted different declining trends of water production for two different operating conditions and provided corresponding recommendations on frequencies of chemical cleaning. A case study on the adjustment of operating parameters using the results suggested by the optimization ML model was conducted. The model validation results showed that the overall water production and water recovery of the system using the cycle-based optimized process control parameters (SCN 1) exceeds the MCDI system performance under three fixed parameter settings that were used at each stage of SCN 1 by 1.78% to 4.48% and 2.95% to 9.46%, respectively. Permutation-based and Shapley additive explanation (SHAP) coefficients were also employed for variable importance (VIMP) analysis to uncover the "black-box" nature of the ML models and to better understand the various features' contributions to overall MCDI system performance.

Optimization of constant-current operation in membrane capacitive deionization (MCDI) using variable discharging operations.

Membrane capacitive deionization (MCDI) is an emerging electric field-driven technology for brackish water desalination involving the removal of charged ions from saline source waters. While the desalination performance of MCDI under different operational modes has been widely investigated, most studies have concentrated on different charging conditions without considering discharging conditions. In this study, we investigate the effects of different discharging conditions on the desalination performance of MCDI electrode. Our study demonstrates that low-current discharge (1.0 mA/cm2) can increase salt removal by 20% and decrease volumetric energy consumption by 40% by improving electrode regeneration and increasing energy recovery, respectively, while high-current discharge (3.0 mA/cm2) can improve productivity by 70% at the expense of electrode regeneration and energy recovery. Whether discharging electrodes at the low current or high current is optimal depends on a trade-off between productivity and energy consumption. We also reveal that stopped flow discharge (85%) can achieve higher water recovery than continuous flow discharge (35-59%). However, stopped flow discharge caused a 20-30% decrease in concentration reduction and a 25-50% increase in molar energy consumption, possibly due to the higher ion concentration in the macropores at the end of discharging step. These results reveal that an optimal discharging operation should be obtained from achieving a balance among productivity, water recovery and energy consumption by varying discharging current and flow rate.