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.

Direct contact membrane distillation applied to saline wastewater: parameter optimization.

Freshwater availability is increasingly under pressure from growing demand, resource depletion and environmental pollution. Desalination of saline wastewater is an option for supplying households, industry and agriculture with water, but technologies such as reverse osmosis, evaporation or electrodialysis are energy intensive. By contrast, membrane distillation (MD) is a competitive technology for water desalination. In our study, response surface methodology was applied to optimize the direct contact membrane distillation (DCMD) treatment of synthetic saline wastewater. The aim was to enhance the process performance and the permeate flux Jp (L/m2·h) by optimizing the operating parameters: temperature difference ΔT, feed velocity Vf, salt concentration [NaCl], and glucose concentration [Gluc]. The results are a high permeate quality, with 99.9% electrical conductivity reduction and more than 99.9% chemical oxygen demand (COD) removal rate. The predicted optimum permeate flux Jp was 34.1 L/m2·h at ΔT = 55.2 °C and Vf = 0.086 m/s, the two most significant parameters. The model created showed a high degree of correlation between the experimental and the predicted responses, with high statistical significance.

Optimization of permeate flux produced by solar energy driven membrane distillation process using central composite design approach.

Membrane distillation (MD) is considered as a relatively high-energy requirement. To overcome this drawback, it is recommended to couple the MD process with solar energy as the renewable energy source in order to provide heat energy required to optimize its performance to produce permeate flux. In the present work, an original solar energy driven direct contact membrane distillation (DCMD) pilot plant was built and tested under actual weather conditions at Jeddah, KSA, in order to model and optimize permeate flux. The dependency of permeate flux on various operating parameters such as feed temperature (46.6-63.4°C), permeate temperature (6.6-23.4°C), feed flow rate (199-451L/h) and permeate flow rate (199-451L/h) was studied by response surface methodology based on central composite design approach. The analysis of variance (ANOVA) confirmed that all independent variables had significant influence on the model (where P-value <0.05). The high coefficient of determination (R(2) = 0.9644 and R(adj)(2) = 0.9261) obtained by ANOVA demonstrated good correlation between experimental and predicted values of the response. The optimized conditions, determined using desirability function, were T(f) = 63.4°C, Tp = 6.6°C, Q(f) = 451L/h and Q(p) = 451L/h. Under these conditions, the maximum permeate flux of 6.122 kg/m(2).h was achieved, which was close to the predicted value of 6.398 kg/m(2).h.

A comprehensive factorial design study of variables affecting CaCO3 scaling under magnetic water treatment.

Magnetic water treatment (MWT) could be an interesting alternative to chemical treatment to prevent scaling and has been used as an antiscaling treatment for domestic and industrial equipment. A two level-three factor (2(3)) full factorial design was used to evaluate the effects of pH (6-7.5), flow rate (0.54-0.94) and application of a magnetic field on the induction time (IT), total precipitation (TP) rate and homogeneous precipitation (HP) rate of calcium carbonate (CaCO3) scale from hard water. The experimental results and statistical analysis show that the pH, flow rate and interaction between pH and magnetic field have negative effects on IT response. In the case of TP rate response, the magnitude of the main influence is attributed to the magnetic field, followed by pH value and their interaction. Flow rate and pH value have a negative effect on HP rate response, but their interaction has a positive effect. Within these factor ranges, these studied responses predicted by the models were in good agreement with the experimental values. The coefficient of determination (R(2)) for reduction time, TP ratio and HP ratio were 97.8%, 99.12% and 99.92%, respectively. These results were analyzed statistically using Minitab 15.

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.

Fluoride removal from aqueous solution by direct contact membrane distillation: theoretical and experimental studies.

Direct contact membrane distillation (DCMD) process using polyvinylidene fluoride (PVDF) membrane was used for fluoride removal from aqueous solution. This study has been carried out on heat and mass transfer analyses in DCMD. The dusty-gas model was used to analyze the mass transfer mechanism and to calculate the permeate flux. The heat transfer is analyzed based on energy balance, and the different layers are considered as a series of thermal resistances. Mass transfer analysis showed that the transition Knudsen-molecular diffusion is the dominant mechanism to describe the transport of water vapor through the pores of the PVDF membrane. The most significant operating parameter is the feed temperature. The permeate increases sensitively with feed temperature and velocity, and it shows insignificant change with feed salts concentration. Heat transfer analysis showed the conduction through the matrix of the membrane presents the major part of available energy. The increasing feed temperature leads to increase thermal efficiency (TE) and decrease temperature polarization coefficient (TPC). The experimental results are in good agreement with theoretical values. Therefore, it is suggested to work at high feed temperature, which will benefit both the thermal efficiency and permeate flux. The experimental results proved that DCMD process is able to produce almost fluoride-free water suitable for many beneficial uses.