Characterization and Performance Relationships for a Commercial Thin Film Composite Membrane in Forward Osmosis Desalination and Pressure Retarded Osmosis

On the evaluating membrane flux of forward osmosis systems: Data assessment and advanced intelligent modeling

As an emerging desalination technology, forward osmosis (FO) can potentially become a reliable method to help remedy the current water crisis. Introducing uncomplicated and precise models could help FO systems' optimization. This paper presents the prediction and evaluation of FO systems' membrane flux using various artificial intelligence-based models. Detailed data gathering and cleaning were emphasized because appropriate modeling requires precise inputs. Accumulating data from the original sources, followed by duplicate removal, outlier detection, and feature selection, paved the way to begin modeling. Six models were executed for the prediction task, among which two are tree-based models, two are deep learning models, and two are miscellaneous models. The calculated coefficient of determination (R2 ) of our best model (XGBoost) was 0.992. In conclusion, tree-based models (XGBoost and CatBoost) show more accurate performance than neural networks. Furthermore, in the sensitivity analysis, feed solution (FS) and draw solution (DS) concentrations showed a strong correlation with membrane flux. PRACTITIONER POINTS: The FO membrane flux was predicted using a variety of machine-learning models. Thorough data preprocessing was executed. The XGBoost model showed the best performance, with an R2 of 0.992. Tree-based models outperformed neural networks and other models.

Design Strategies for Forward Osmosis Membrane Substrates with Low Structural Parameters-A Review

This article reviews the many innovative strategies that have been developed to specifically design the support layers of forward osmosis (FO) membranes. Forward osmosis (FO) is one of the most viable separation technologies to treat hypersaline wastewater, but its successful deployment requires the development of new membrane materials beyond existing desalination membranes. Specifically, designing the FO membrane support layers requires new engineering techniques to minimize the internal concentration polarization (ICP) effects encountered in cases of FO. In this paper, we have reviewed several such techniques developed by different research groups and summarized the membrane transport properties corresponding to each approach. An important transport parameter that helps to compare the various approaches is the so-called structural parameter (S-value); a low S-value typically corresponds to low ICP. Strategies such as electrospinning, solvent casting, and hollow fiber spinning, have been developed by prior researchers-all of them aimed at lowering this S-value. We also reviewed the quantitative methods described in the literature, to evaluate the separation properties of FO membranes. Lastly, we have highlighted some key research gaps, and provided suggestions for potential strategies that researchers could adopt to enable easy comparison of FO membranes.

Insights into the Influence of Membrane Permeability and Structure on Osmotically-Driven Membrane Processes

The success of osmotically-driven membrane (OM) technology relies critically on high-performance membranes. Yet trade-off of membrane properties, often further complicated by the strongly non-linear dependence of OM performance on them, imposes important constraint on membrane performance. This work systematically characterized four typical commercial osmotic membranes in terms of intrinsic separation parameters, structure and surface properties. The osmotic separation performance and membrane scaling behavior of these membranes were evaluated to elucidate the interrelationship of these properties. Experimental results revealed that membranes with smaller structural parameter (S) and higher water/solute selectivity underwent lower internal concentration polarization (ICP) and exhibited higher forward osmosis (FO) efficiency (i.e., higher ratio of experimental water flux over theoretical water flux). Under the condition with low ICP, membrane water permeability (A) had dominant effect on water flux. In this case, the investigated thin film composite membrane (TFC, A = 2.56 L/(m² h bar), S = 1.14 mm) achieved a water flux up to 82% higher than that of the asymmetric cellulose triacetate membrane (CTA-W(P), A = 1.06 L/(m² h bar), S = 0.73 mm). In contrast, water flux became less dependent on the A value but was affected more by membrane structure under the condition with severe ICP, and the membrane exhibited lower FO efficiency. The ratio of water flux (J_{v TFC}/J_{v CTA-W(P)}) decreased to 0.55 when 0.5 M NaCl feed solution and 2 M NaCl draw solution were used. A framework was proposed to evaluate the governing factors under different conditions and to provide insights into the membrane optimization for targeted OM applications.

Osmotic pressure estimation using the Pitzer equation for forward osmosis modelling

Forward osmosis (FO) has received widespread recognition in the past decade due to its potential low energy production of water. This study presents a new model analysis for predicting the water flux in FO systems when inorganic-based draw solutions are used under variable experimental conditions for using a laboratory scale cross-flow single cell unit. The new model accounts for the adverse impact of concentration polarization (both ICP and ECP) incorporating the water activity by Pitzer to calculate the bulk osmotic pressures. Using the water activity provides a better correlation of experimental data than the classical van't Hoff equation. The nonlinear model also gave a better estimate for the structural parameter factor (S) of the membrane in its solution. Furthermore, the temperature and concentration of both the draw and feed solutions played a significant role in increasing the water flux, which could be interpreted in terms of the mass transfer coefficient representing ECP; a factor sensitive to the hydraulics of the system. The model provides greatly improved correlations for the experimental water fluxes.