Optimization of the Forward Osmosis Process Using Aquaporin Membranes in Chromium Removal

Naghdali, Zohreh; Sahebi, Soleyman; Mousazadeh, Milad; Jamali, Hamzeh Ali

doi:10.1002/ceat.201900381

Cited by 28 publications

(14 citation statements)

References 37 publications

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“…Later on, Tang research group used conventional interfacial polymerization method for making the AQPs‐FO membrane which opened a scalable approach to making the AQPs‐FO membrane on a roll‐to‐roll basis on an industrial scale 39,40 . The capability of the commercial AQPs‐FO membrane had been evaluated for the removal of heavy metals in industrial wastewater 41 …”

Section: Introductionmentioning

confidence: 99%

“…39,40 The capability of the commercial AQPs-FO membrane had been evaluated for the removal of heavy metals in industrial wastewater. 41 This study aims to investigate the performance of newly commercialized AQP membranes in the FO process using commonly used fertilizers. This is the first study on evaluating five commonly used fertilizers as DS for desalination of feed water with different total dissolved solids (TDS) for AQP FO membrane.…”

Section: Introductionmentioning

confidence: 99%

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Performance evaluation of aquaporin forward osmosis membrane using chemical fertilizers as a draw solution

Mirshekar

Kamarehie

Jafari

et al. 2020

Env Prog and Sustain Energy

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The performance of a newly commercialized aquaporin (AQP)‐based biomimetic forward osmosis (FO) membrane was evaluated with several commonly used fertilizers ((CO (NH2)2, KCl, CaCl2, (NH4)2SO4 and (NH4)2HPO4) as a draw solutions (DSs) to verifying the possible implementation of this AQP‐FO membrane in the fertigation industry. Also, DS and feed solution (FS) concentrations were evaluated under FO and pressure retarded osmosis (PRO) operation modes. The results showed that CaCl2 seems to be a more suitable DS in terms of the tradeoff between water flux and salt permeability, compared to the other DSs used in this study. However, KCl gained the highest performance in terms of water flux which was 13.9 and 24.1 Lm−2 h−1 in the FO and PRO mode, respectively. The performance of the AQP membrane in terms of water flux and reverse solute flux can be improved by modifying the structural and the membrane inhibitor layer characteristics for utilizing in commercial scales to compete with other available membranes of the FO process.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance evaluation of aquaporin forward osmosis membrane using chemical fertilizers as a draw solution

Mirshekar

Kamarehie

Jafari

et al. 2020

Env Prog and Sustain Energy

Self Cite

View full text Add to dashboard Cite

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“…The flow rate and concentration of both feed solution (FS) and draw solution (DS) were used to maximize the membrane flux and removal efficiency. Naghdali et al investigated a FO process with an aquaporins-based membrane and maximized chromium rejection and membrane flux using RSM [ 29 ]. FS concentration, DS concentration, and time were the critical parameters optimized in their study.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology Techniques

2021

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The forward osmosis (FO) process is an emerging technology that has been considered as an alternative to desalination due to its low energy consumption and less severe reversible fouling. Artificial neural networks (ANNs) and response surface methodology (RSM) have become popular for the modeling and optimization of membrane processes. RSM requires the data on a specific experimental design whereas ANN does not. In this work, a combined ANN-RSM approach is presented to predict and optimize the membrane flux for the FO process. The ANN model, developed based on an experimental study, is used to predict the membrane flux for the experimental design in order to create the RSM model for optimization. A Box–Behnken design (BBD) is used to develop a response surface design where the ANN model evaluates the responses. The input variables were osmotic pressure difference, feed solution (FS) velocity, draw solution (DS) velocity, FS temperature, and DS temperature. The R2 obtained for the developed ANN and RSM model are 0.98036 and 0.9408, respectively. The weights of the ANN model and the response surface plots were used to optimize and study the influence of the operating conditions on the membrane flux.

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“…These modifications have involved incorporation of nanoparticles or chemical additives into the layers 5, 6 and new methods of membrane fabrication, such as layer‐by‐layer approaches 7 and different integral asymmetric 8 and thin‐film composite (TFC) membranes. The last‐named are the benchmark membranes for FO systems 9–13.…”

Section: Introductionmentioning

confidence: 99%

Thin‐Film Composite Forward‐Osmosis Membranes Reinforced on Woven Mesh and Nonwoven Backing Fabric Supports

et al. 2021

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Thin‐film composite (TFC) forward‐osmosis (FO) membranes were developed on different woven and nonwoven backing fabrics as support. Embedded backing fabrics have a significant effect on the membrane film sublayer by imparting porosity, membrane mechanical strength, and thickness. Woven and nonwoven backing fabric supports have been used for FO membranes cast on the commercial scale. There are also a few studies on the laboratory scale, but repeating them seems to be a challenge. The results herein show that TFC membranes with nonwoven incorporated substrates gain a higher water flux while keeping the specific reverse salt flux low.

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Optimization of the Forward Osmosis Process Using Aquaporin Membranes in Chromium Removal

Cited by 28 publications

References 37 publications

Performance evaluation of aquaporin forward osmosis membrane using chemical fertilizers as a draw solution

Performance evaluation of aquaporin forward osmosis membrane using chemical fertilizers as a draw solution

Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology Techniques

Thin‐Film Composite Forward‐Osmosis Membranes Reinforced on Woven Mesh and Nonwoven Backing Fabric Supports

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