Membrane Process Optimization Research Articles

Modeling, Simulation, and Optimization of Membrane Processes

From water desalination and gas purification to metal recovery and beyond, membrane separation has become integral to numerous industrial applications [...]

Modeling and Optimization of Membrane Process for Salinity Gradient Energy Production

When hydraulic pressure was added on the feed side of the membrane in the otherwise conventional pressure retarded osmosis (PRO) process, the production rate of the salinity gradient energy could be significantly increased by manipulating the hydraulic pressures on both sides of the membrane. With hydraulic pressure added on the feed side of the membrane, much higher water flux could be obtained than that under the osmotic pressure of the same value. The osmotic pressure of the draw solution, instead of drawing water through the membrane, was mainly reserved to increase the hydraulic pressure of the permeate. In this way, orders of magnitude higher power density than that in the conventional PRO can be obtained with the same salinity gradient. At the optimal conditions, it was demonstrated that the energy production rates that were much higher than the economical breakeven point could be obtained from the pair of seawater and freshwater with the currently available semipermeable membranes.

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.

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.

Influence of ZnO Nanomaterial Shape on UF Membrane Properties: a Comparative Study Between Nanoparticles and Nanowires

Membrane research has managed to reach ever greater heights. The optimization of membrane processes is of common interest to industry, research and the domestic environment. Nanomaterials have been progressively researched in the membrane sector within the last decade, contributing in particular to their beneficial properties for the prevention of membrane fouling. This research investigates the effect of two shapes of ZnO nanomaterials, respectively nanoparticles and nanowires, on the properties of ultrafiltration membranes composed of 25 wt.% polysulfone. The method of membrane manufacturing is phase inversion, the immersion precipitation technique, and the procedure of nanomaterial incorporation into the polymeric matrix is known as blending. The results demonstrated the positive influence of nanomaterials on the performance of membranes, regardless of their shape, compared to the control membrane. In terms of permeability, the membrane with addition of ZnO nanoparticles showed an increase of 207.19 %, while the membrane with addition of zinc oxide nanowires recorded an increase of 89.16%.

Multi-scale membrane process optimization with high-fidelity ion transport models through machine learning

Innovative membrane technologies optimally integrated into large separation process plants are essential for economical water treatment and disposal. However, the mass transport through membranes is commonly described by nonlinear differential-algebraic mechanistic models at the nano-scale, while the process and its economics range up to large-scale. Thus, the optimal design of membranes in process plants requires decision making across multiple scales, which is not possible using standard tools. In this work, we embed artificial neural networks (ANNs) as surrogate models in the deterministic global optimization to bridge the gap of scales. This methodology allows for deterministic global optimization of membrane processes with accurate transport models – avoiding the utilization of inaccurate approximations through heuristics or short-cut models. The ANNs are trained based on data generated by a one-dimensional extended Nernst-Planck ion transport model and extended to a more accurate two-dimensional distribution of the membrane module, that captures the filtration-related decreasing retention of salt. We simultaneously design the membrane and plant layout yielding optimal membrane module synthesis properties along with the optimal plant design for multiple objectives, feed concentrations, filtration stages, and salt mixtures. The developed process models and the optimization solver are available open-source, enabling computational resource-efficient multi-scale optimization in membrane science.

Study on mechanism and method of membrane preparation and membrane process optimization based on molecular structure analysis of noneffective common macromolecules

The molecular weight of the effective components of traditional Chinese medicine( TCM) is usually less than 1 000.However, " noneffective common macromolecules"( starch,pectin and other macromolecules commonly present in the water extract of TCM) generally have no physiological activity,which restricts the overall advantages of membrane technology to obtain small molecular pharmacodynamic substances,and such macromolecules are the main influence factor of membrane fouling. Therefore,in order to obtain the total pharmacological efficacy of TCM,based on the molecular structure analysis of noneffective common macromolecules,aimed at the key scientific problems in correlation between the molecular structure of noneffective common macromolecules and the pore structure of membrane material,and by referring to the material science theory and molecular simulation method,the correlations between noneffective common macromolecules' molecular structure-solution environment-membrane antagonism were investigated. Multidisciplinary approaches could be integrated to: ① optimize the spatial form of membrane surface and improve the membrane's antifouling ability; ② accurately control the pore structure and the size distribution of membranes,aimed at the innovative preparation technology of special membrane used for TCM; ③ adjust solution environment based on the analysis of molecular structure,and establish the pretreatment method based on the optimization of solution environment. Furthermore,the technical bottleneck on how to obtain the pharmacodynamic micromolecules effectively might be solved,and the theory and technology about TCM pharmaceutical engineering could be developed based on the concept of multivariate and integration.

Membrane process optimization for carbon capture

The utilization of membrane technology in post-combustion applications is hindered by low CO2 feed partial pressure in the flue gas stream. In order to meet the separation targets of 90% CO2 recovery, 95% or higher CO2 purity, and a Levelized Cost of Electricity (LCOE) increase of less than 35%, Membrane Technology and Research, Inc. (MTR) (Merkel et al., 2010) has proposed the use of the boiler air feed as a sweep stream to increase the CO2 concentration in the flue gas and partial pressure driving force for permeation without additional compression or vacuum. Such a design significantly reduces capture cost but leads to a detrimental reduction in the O2 concentration of the feed air to the boiler. The transport properties and operating pressures of the membrane stages are optimized in this study. Membrane CO2/N2 selectivity is varied over a broad range encompassing the values considered by MTR. Membrane CO2 permeability is varied with selectivity according to the variation anticipated by the upper bound of the Robeson plot for CO2 and N2. Membrane CO2 permeance is calculated assuming membranes can be fabricated with an effective thickness of 0.1μm. Additionally, the two stages may utilize different membrane materials. The feed and permeate pressures also are varied over ranges encompassing the values proposed by MTR. The optimization space of membrane properties and operating conditions is scanned globally to determine the process design that minimizes LCOE. The O2 concentration to the boiler is evaluated during the optimization process and can be used to constrain viable alternatives. The results indicate a fairly broad range of membrane properties can yield comparable LCOE near the minimum. The optimal operating pressure range is somewhat narrower. The minimum allowable O2 concentration constrains viable designs significantly and is critical to process economics.

Modeling of suspension fouling in nanofiltration

Prediction of membrane process performance using experimental and mathematical models facilitates optimization of membrane processes and their operating conditions. On the other hand investigation of nanofiltration process performance over time and estimation of the time for recovery are very essential in the membrane industry. In this work a mathematical model considering membrane resistance changes and also dissolution of deposited particles in nanofiltration feed, for a cross flow nanofiltration system was developed. The validation of this model with experimental data demonstrated a good agreement. At a constant concentration of 0.2g/L error of the present models varied from 3% (at the pressure of 1bar) up to about 3.8% (at the pressure of 2.9bar) while at a constant pressure of 2bar error varied from 2.45% (at the concentration of 0.1g/L) up to about 4.2% (at the concentration of 0.4g/L). A comparison between this model and three other previous models proved a better performance for the present model with an average error of 3.25%.

Optimization of Membrane Process for Methane Recovery from Biogas

Including biogas, methane (CH4) could be used as one of the important energy sources. In this research, the membrane separation process took place to recover purified CH4, such as hydrogen sulfide (H2S). The Polyethersulfone (PES) Hollow Fiber Membranes (HFM) was prepared by the dry-wet phase inversion technique. The simulation program using MATLAB was evaluated by utilizing the information from experimental results of a well-designed process. From the simulation program, the process that meets the specified conditions was designed. The three-stage process that has a membrane area of 454.3 m2 recovered 91.3% of CH4 in the feed of 250 l/min at 5 kgf/cm2 and 30°C. The membrane area and the recovery of CH4 decreases as the operating process increases.

Recovery optimization of membrane processes for treatment of produced water with high silica content

Disc tube (DT) and spiral wound (SW) configurations of nanofiltration (NF) and reverse osmosis (RO) processes were tested at pilot-scale using a two-pass configuration to treat produced water obtained from natural gas wells. First pass NF membranes were used to remove divalent cations from produced water. Permeate from the first pass NF membranes were used as feed to second pass RO membranes after increasing the pH to 10.0 to enhance silica solubility. To reduce the fouling potential on NF and RO membranes dissolved air floatation (DAF), ceramic ultrafiltration (UF), MYCELX cartridges, and organoclay filters were tested as pretreatment alternatives. Pretreatment processes were effective for turbidity and oil and grease removal but were not efficient in retaining organic matter, primarily protein-like and polysaccharide-like material, which eventually fouled the first pass NF membranes. The second pass RO membranes were scaled predominantly by silica. The overall feed water recovery of the two-pass NF-RO s...

Immersed hollow fibres microfiltration (MF) for removing undesirable micro-algae and protecting semi-closed aquaculture basins

In this study, an original use of immersed microfiltration membranes (mean pore size equal to 0.2μm) is investigated for the total removal of toxic dinoflagellates from seawater. Using a membrane autopsy and fouling model approach and the use of fouling indexes (called Pore Blocking Index, Pore Constriction Index and Cake Filtration Index), three dinoflagellate suspensions (Heterocapsa triquetra, Alexandrium minutum and Prorocentrum lima) have been microfiltered in order to study the influence of micro-algal species and its concentrations (1,000 and 30,000cells/mL) on filtration yield and membrane fouling mechanisms. Results showed that all micro-algae have been retained after 180min of microfiltration. At 30,000 cells/mL, permeate fluxes declined rapidly and an internal fouling occurred at the beginning of microfiltrations followed by a cake deposition. At 1,000cells/mL, flux declined slowly and was mainly due to an internal fouling. For a given micro-algae concentration, the filtration behaviour and fouling behaviour can be very different based on the micro-algae species. The dissolved organic substances and particulate size distribution are important factors affecting internal but also external fouling. SEM analyses and fouling indexes are useful tools, simple to implement, and allow the study of membrane fouling and membrane process optimization.

Optimization of a nanofiltration membrane process for tomato industry wastewater effluent treatment

The wastewater stream produced during tomato manufacturing is characterized by a dark color and bad smell, and is heavily polluted by organics, suspended solids and ground particles. The concentration of pollutants in the effluent can vary considerably with time and space due to the changes in the harvested fruit composition and season. A discharge in the municipal sewage system of these streams is not directly possible because of the high organic contents above the legally tolerated limits. This work deals with the treatment and purification of the wastewater stream from the tomato industry by a biological pretreatment step and a batch nanofiltration process step. For the latter, critical fluxes were measured at different recovery levels. This permits applying membrane process optimization methods based on the critical flux. The experiments show that the purification of the wastewater up to a water compatible with the municipal sewer system requirements is possible, with a recovery rate of 90%. Short-term fouling issues may be avoided at permeate fluxes about or below 8.2 l h −1 m 2.

Optimization of membrane processes in white biotechnology

White biotechnology for the production of fine chemicals will substitute some chemistry based on petrol and gas in the near future. A standard process for this technology is fermentation. The downstream treatment with membranes is an essential and cost-intensive part of the processing. For downstream processing plate and frame systems are very common due to their robustness and their open channel design. Another essential part of the process is the optimization of membrane fouling. A process for a protease production was used as a reference. The antifoam agent that was used had a strong fouling tendency. It could be shown that a high priced polyamide-imide/polyethersufone membrane could be substituted with a standard, lower priced polyethersufone membrane by optimizing the temperature and antifoam agents.

Optimization diagram for membrane separations

A novel methodology has been developed which enables optimization of membrane separations. In multi-component separation processes, sieving coefficients for the individual solutes, defined as the ratio of the filtrate and feed concentrations, tend to reach optimum values under different process conditions. It is not possible to determine a priori the pair of sieving coefficients which will give the best combination of product yield and purification for a given application. A purification factor-yield diagram for such an optimization has been developed which utilizes a family of curves representing two dimensionless numbers plotted on yield versus purification-factor coordinates. Analysis can be performed with knowledge of only three experimental variables: the filtrate flux and the two solute sieving coefficients. Complete optimization of membrane processes can be achieved by combining these variables with membrane area, process time, and retentate-volume constraints. The methodology should be applicable to ultrafiltration, microfiltration, and high-performance tangential flow (selective) filtration processes.