Partial Desalination of Saline Groundwater: Comparison of Nanofiltration, Reverse Osmosis and Membrane Capacitive Deionisation

The sources, leaching, remediation, and environmental concerns associated with groundwater salinity

Water resources management and sustainable development depend on the quality of groundwater as a major source of fresh water. As a result of rising water demand in emerging nations and overexploitation, groundwater quality has declined globally in many aquifers. One of the most significant elements that lower the quality of the groundwater is salinization. This review is to provide an overview of various materials that are used in the design and development of innovative chitosan-based nanocomposite polymeric membranes for desalination. Biodegradable, non-toxic, affordable, and easily available, with film-forming ability and poly-functionality, chitosan is an ideal material for a sustainable future. Membrane preparation for desalination using chitosan helps to provide antibacterial and antioxidant activities, great chelating capabilities, and strong adsorption capacity. In this research, we discuss a variety of concepts concerning the different sources of elevated salinity and available desalination methods. A comprehensive framework was also developed to understand the leaching and percolation of salt in groundwater, an essential component of managing risks and ensuring safety. Additionally, we explain the various remediation strategies for reducing groundwater's salt concentration and explore the best method for desalination specifically focused on chitosan.

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.

Influence of the Zeolite ZSM-22 Precursor on a UF-PES Selective Substrate Layer for Salts Rejection

Fabrication of the ZSM-22/Polyethersulfone (ZSM-22/PES) membranes as selective salt filters represent a growing membrane technological area in separation with the potential of high economic reward based on its low energy requirements. The incorporation of ZSM-22 zeolite material as additives into the PES polymer matrix has the prospective advantage of combining both the zeolite and polymer features while overcoming the limitations associated with both materials. This work investigated the influence of the nature of the silica precursor on ZSM-22 zeolite hydrothermally synthesised using colloidal (C60) and fumed (C60) silica to Si/Al of 60. The successful synthesis of the highly crystalline zeolitic materials was confirmed through XRD, FTIR, and SEM with EDX. The ZSM-22 additives were directly dispersed into a PES polymeric matrix to form a casting solution for the preparation of the ZSM-22/PES selective substrate layers via a phase inversion method for salts rejection. The polymeric PES was selected as an organic network in which the content of the ZSM-22 zeolite (ranging between 0 and 1.0 wt.%), was obtained and characterised by XRD, FTIR, and SEM analysis, as well as water contact angle (WCA) measurement and dead-end filtration cell. The phase inversion preparation method has induced the resulting ZSM-22/PES NF substrates anisotropy, as attributed to a high water flux to the above 700 L·m−2·h−1; high selectivity and rejection of salts to above 80% is revealed by the obtained results. The materials also exhibited improved antifouling behavior to above 70% flux recovery ratios. As such, the nature of the silica precursor influences ZSM-22 zeolite synthesis as a potential additive in the PES polymer matrix and led to the enhanced performance of the pure PES ultrafiltration membrane.