In arid and water-stressed regions, groundwater desalination plants are critical for ensuring reliable potable water supplies, making improvements in their operational efficiency and cost effectiveness a priority for utilities. In many such facilities, lime and soda ash softening remain common pretreatment practices, which increase chemical consumption and sludge generation, prompting the need for alternative low-chemical strategies. This study evaluates the technical, operational, and economic implications of transitioning a full-scale brackish groundwater desalination plant, from lime–soda ash softening (old plan) to a low-chemical pretreatment strategy based on antiscalant dosing (new plan) upstream of reverse osmosis (RO). Key parameters, including pH, total hardness, calcium and magnesium hardness, silica, iron, alkalinity, and total dissolved solids (TDS), were measured and compared at multiple locations within the treatment plant under both the old and new plans. Removing lime and soda ash caused higher levels of hardness, alkalinity, and silica in the water before RO treatment, increasing the risk of scaling. Operationally, the feed pressure increased from 11.43 ± 0.16 bar (old plan) to a peak of 25.50 ± 0.10 bar in the new plan, accompanied by a decline in water production. Chemical cleaning effectively restored performance, reducing feed pressure to 13.13 ± 0.05 bar, confirming that fouling and scaling were the primary, reversible causes. Despite these challenges, the plant consistently produced water that complied with Saudi Standards for Unbottled Drinking Water (e.g., pH = 7.18 ± 0.09, TDS = 978.27 ± 9.26 mg/L). Economically, the new strategy reduced operating expenditure by approximately 54% (0.295 → 0.135 $/m³), largely due to substantial reductions in chemical and sludge handling costs, although these savings were partially offset by higher energy consumption and more frequent membrane maintenance. Overall, the findings emphasize the importance of systematic performance evaluation during operational transitions, providing guidance for utilities seeking to optimize pretreatment design while maintaining compliance, long-term membrane protection, and environmental sustainability.

Capacitive deionization (CDI) is an emerging low-energy desalination technology that removes salt ions from brackish water using porous carbon electrodes under low-voltage operation. However, the occurrence of cathodic Faradaic reactions, particularly oxygen reduction reactions (ORRs), reduces charge efficiency and limits sodium (Na⁺) removal. This study presents a practical strategy to enhance Na⁺ removal efficiency in CDI by employing an asymmetric electrode mass configuration. Reducing the cathode-to-anode mass ratio to 0.5 significantly improved Na⁺ adsorption capacity and overall cell charge efficiency by 83% and 40%, respectively, relative to a symmetric cell. In the asymmetric configuration, the working potential windows of the electrodes were strategically shifted. Specifically, the cathode half-cell potential increased by up to 30%, resulting in a stronger electric driving force for Na⁺ electrosorption. Additionally, analysis of the anode potential profile and corresponding current-time behavior revealed a reduction of up to 67% in the charge fraction consumed for co-ion (Na⁺) desorption, particularly during the period when the anode operated below its potential of zero charge ( E _pzc ). Moreover, effluent measurements of dissolved oxygen (DO) and hydrogen peroxide (H₂O₂) confirmed that ORRs were significantly suppressed under asymmetric conditions, likely due to DO depletion at the cathode, thereby minimizing competition with capacitive ion removal. This work demonstrates a simple and effective operational approach to improve CDI desalination performance by tuning electrode mass ratios, optimizing electrode potential distributions, and mitigating parasitic Faradaic losses. The findings offer valuable insights for the development of more efficient and sustainable CDI-based water treatment systems.

Continuous monitoring of seawater alkalinity is essential for maintaining chemical stability in coastal environments and supporting efficient operation of desalination and water-treatment systems; however, direct alkalinity measurements are often limited in temporal resolution. This study develops and evaluates a machine learning framework for estimating seawater alkalinity using quality-controlled daily and sub-daily water-quality observations collected from a coastal monitoring station along the Arabian Gulf coast of eastern Saudi Arabia during 2017–2023. Five machine learning models, Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Support Vector Regression (SVR), and K-Nearest Neighbors (KNN), are assessed under two configurations: a baseline setup relying on the original predictor variables and an enhanced setup incorporating wavelet-decomposed features to represent multiscale temporal variability. Model performance is evaluated using five-fold cross-validation and quantified using R², root mean square error (RMSE), and mean absolute error (MAE). Under the baseline configuration, ensemble-based models outperform single-estimator and distance-based approaches, with RF achieving the best performance (R² = 0.77, RMSE = 2.57 ppm, MAE = 1.71 ppm). The incorporation of wavelet-based feature enrichment leads to consistent performance improvements across all models, reflected by higher R² values and reduced RMSE and MAE. The wavelet-enhanced RF model exhibits the strongest overall performance, attaining a mean R² of approximately 0.91 together with an RMSE of about 1.6 ppm and an MAE of around 1.0 ppm, while also showing reduced variability across cross-validation folds. The XGB model shows notable improvement with wavelet enrichment, whereas SVR and KNN benefit mainly through moderate error reduction. Overall, the findings show that wavelet-based feature enrichment improves the accuracy and stability of ML models for seawater alkalinity estimation, with RF providing the most reliable performance for coastal monitoring applications.