An in-depth analysis of membrane distillation research (1990-2023): Exploring trends and future directions through bibliometric approach.

This bibliometric analysis offers a comprehensive investigation into membrane distillation (MD) research from 1990 to 2023. Covering 4389 publications, the analysis sheds light on the evolution, trends, and future directions of the field. It delves into authorship patterns, publication trends, prominent journals, and global contributions to reveal collaborative networks, research hotspots, and emerging themes within MD research. The findings demonstrate extensive global participation, with esteemed journals such as Desalination and the Journal of Membrane Science serving as key platforms for disseminating cutting-edge research. The analysis further identifies crucial themes and concepts driving MD research, ranging from membrane properties to strategies for mitigating membrane fouling. Co-occurrence analysis further highlights the interconnectedness of research themes, showcasing advancements in materials, sustainable heating strategies, contaminant treatment, and resource management. Overlay co-occurrence analysis provides temporal perspective on emerging research trends, delineating six key topics that will likely shape the future of MD. These include innovations in materials and surface engineering, sustainable heating strategies, emerging contaminants treatment, sustainable water management, data-driven approaches, and sustainability assessments. Finally, the study serves as a roadmap for researchers and engineers navigating the dynamic landscape of MD research, offering insights into current trends and future trajectories, ultimately aiming to propel MD technology towards enhanced performance, sustainability, and global relevance.

Optimization and prediction of lead removal from aqueous solution using FO-MD hybrid process: Statistical and artificial intelligence analysis.

Heavy metals (HMs) has become one of the most serious pollutants that are harmful to the environment and ecology. This paper focused on the removal of lead contaminant from wastewater by forward osmosis-membrane distillation (FO-MD) hybrid process using seawater as draw solution. Modeling, optimization, and prediction of FO performance are developed using complementary approach based on response surface methodology (RSM) and an artificial neural network (ANN). FO process optimization using RSM revealed that under initial lead concentration of 60 mg/L, feed velocity of 11.57 cm/s and draw velocity of 7.66 cm/s, FO process achieved highest water flux of 6.75 LMH, lowest reverse salt flux of 2.78 gMH and highest lead removal efficiency of 87.07%. Fitness of all models was evaluated based on determination coefficient (R2) and mean square error (MSE). Results showed highest R2 value up to 0.9906 and lowest RMSE value up to 0.0102. ANN modeling generates the highest prediction accuracy for water flux and reverse salt flux, while RSM produces the highest prediction accuracy for lead removal efficiency. Subsequently, FO optimal conditions are applied on FO-MD hybrid process using seawater as draw solution and evaluate their performance to simultaneously remove lead contaminant and desalination of seawater. Results displays that FO-MD process shows a highly efficient solution to produce fresh water with almost free heavy metals and very low conductivity.

Unlocking groundwater desalination potential for agriculture with fertilizer drawn forward osmosis: prediction and performance optimization via RSM and ANN.

The agricultural sector uses 70% of the world's freshwater. As clean water is extracted, groundwater quality decreases, making it difficult to grow crops. Brackish water desalination is a promising solution for agricultural areas, but the cost is a barrier to adoption. This study investigated the performance of the fertilizer drawn forward osmosis (FDFO) process for brackish water desalination using response surface methodology (RSM) and artificial neural network (ANN) approaches. The RSM model was used to identify the optimal operating conditions, and the ANN model was used to predict the water flux (Jw) and reverse solute flux (Js). Both models achieved high accuracy, with RSM excelling in predicting Js (R2 = 0.9614) and ANN performing better for Jw (R2 = 0.9801). Draw solution (DS) concentration emerged as the most critical factor for both models, having a relative importance of 100% for two outputs. The optimal operating conditions identified by RSM were a DS concentration of 22 mol L-1, and identical feed solution (FS) and DS velocities of 8.1 cm s-1. This configuration yielded a high Jw of 4.386 LMH and a low Js of 0.392 gMH. Furthermore, the study evaluated the applicability of FDFO for real brackish groundwater. The results confirm FDFO's potential as a viable technology for water recovery in agriculture. The standalone FO system proves to be less energy-intensive than other desalination technologies. However, FO exhibits a low recovery rate, which may necessitate further dilution for fertigation purposes.