Computational simulation and theoretical modeling of CO2 separation using EDA, PZEA and PS absorbents inside the hollow fiber membrane contactor

Computational evaluation of micropores wetting effect on the removal process of CO2 through the membrane contactor

In the current years, gas-liquid membrane contactors (GLMCs) have been introduced as a promising, versatile and easy-to-operate technology for mitigating the emission of major greenhouse contaminants (i.e., CO2 and H2S) to the ecosystem. This paper tries to computationally study the role of membrane pores wettability on the removal performance of CO2 inside the HFMC. To fulfill this purpose, a mathematical model based on finite element procedure (FEP) has been employed to solve the momentum and mass transport equations in the partial-wetting (50% wetting of micropores) and non-wetting (0% wetting of micropores) modes of membrane during operation. Additionally, a comprehensive simulation was ensembled to predict the results. In this research, 2-amino-2-methyl-l-propanol (AMP) has been employed as a relatively novel alkanolamine absorbent to separate CO2 form CO2/N2 mixture. Analysis of the results implied that the wetting of membrane micropores significantly deteriorated the removal efficiency due to the enhancing mass transfer resistance towards transferring CO2 (75% in the non-wetting mode > 8% considering 50% wetting of micropores).

Comprehensive review on the efficiency of ionic liquid materials for membrane separation and environmental applications

In the current twenty years, industrial applications of ionic liquids (ILs) have been of paramount attention due to their indisputable positive characteristics like negligible volatility and chemical/thermal stability. These brilliant advantages open new horizons towards environmentally friendly application of ILs in several industrial activities like membrane-based CO₂ separation, electrolyte, bioprocessing, targeted drug delivery and solar panels. The principal intention of this article is to prepare a comprehensive review on the potential efficiency of IL-based absorbents to separate CO₂ acidic contaminant from industrial gaseous streams compared to alkanolamine absorbents as the benchmark. For this purpose, a techno-economic evaluation is presented to compare the cost-effectiveness of ILs compared to alkanolamine absorbents. Finally, major environmental impacts of the ILs applications in industries are discussed and future perspectives towards solving the operational challenges are presented in detail.

Computational fluid dynamics comparison of prevalent liquid absorbents for the separation of SO2 acidic pollutant inside a membrane contactor

In recent years, the emission of detrimental acidic pollutants to the atmosphere has raised the concerns of scientists. Sulphur dioxide (SO₂) is a harmful greenhouse gas, which its abnormal release to the atmosphere may cause far-ranging environmental and health effects like acid rain and respiratory problems. Therefore, finding promising techniques to alleviate the emission of this greenhouse gas may be of great urgency towards environmental protection. This paper aims to evaluate the potential of three novel absorbents (seawater (H₂O), dimethyl aniline (DMA) and sodium hydroxide (NaOH) to separate SO₂ acidic pollutant from SO₂/air gaseous stream inside the hollow fiber membrane contactor (HFMC). To reach this goal, a CFD-based simulation was developed to predict the results. Also, a mathematical model was applied to theoretically evaluate the transport equations in different compartments of contactor. Comparison of the results has implied seawater is the most efficient liquid absorbent for separating SO₂. After seawater, NaOH and DMA are placed at the second and third rank (99.36% separation using seawater > 62% separation using NaOH > 55% separation using DMA). Additionally, the influence of operational parameters (i.e., gas and liquid flow rates) and also membrane/module parameters (i.e., length of membrane module, hollow fibers' number and porosity) on the SO₂ separation percentage is investigated as another highlight of this paper.

Kinetic and Thermodynamic Analysis of High-Pressure CO2 Capture Using Ethylenediamine: Experimental Study and Modeling

One of the alternatives to reduce CO2 emissions from industrial sources (mainly the oil and gas industry) is CO2 capture. Absorption with chemical solvents (alkanolamines in aqueous solutions) is the most widely used conventional technology for CO2 capture. Despite the competitive advantages of chemical solvents, the technological challenge in improving the absorption process is to apply alternative solvents, reducing energy demand and increasing the CO2 captured per unit of solvent mass. This work presents an experimental study related to the kinetic and thermodynamic analysis of high-pressure CO2 capture using ethylenediamine (EDA) as a chemical solvent. EDA has two amine groups that can increase the CO2 capture capacity per unit of solvent. A non-stirred experimental setup was installed and commissioned for CO2 capture testing. Tests of the solubility of CO2 in water were carried out to validate the experimental setup. CO2 capture testing was accomplished using EDA in aqueous solutions (0, 5, 10, and 20 wt.% in amine). Finally, a kinetic model involving two steps was proposed, including a rapid absorption step and a slow diffusion step. EDA accelerated the CO2 capture performance. Sudden temperature increases were observed during the initial minutes. The CO2 capture was triggered after the absorption of a minimal amount of CO2 (~10 mmol) into the liquid solutions, and could correspond to the “lean amine acid gas loading” in a typical sweetening process using alkanolamines. At equilibrium, there was a linear relationship between the CO2 loading and the EDA concentration. The CO2 capture behavior obtained adapts accurately (AAD < 1%) to the kinetic mechanism.

Extraction of ingredients from tea leaves using oxidative enzymatic reaction and optimization of extraction conditions

Peroxidase (POD) and polyphenol oxidase (PPO) are used as biocatalyst in many processes such as oxidization reactions, wastewater treatment, phenol synthesis and so on. The purpose of current study is enzymes extraction from biomass (tea leaves) as well as evaluation of their activation. Different parameters including temperature, buffer concentration, buffer type, buffer/tea leaves ratio, addition of high molecular weight polymers and emulsifiers, and pH were optimized in order to obtain the highest enzymes activity. Response Surface Methodology (RSM) procedure is employed for statistical analysis of enzymes extraction. It is understood from the result that PPO and POD possess the highest activity at temperatures of 25 °C and 50 °C, pH 7 and 5, buffer molarity of 0.1, and 0.05, buffer/tea leaves ratio = 5 for both, contact time = 20 min and 10 min, and presence of 6% and 3% PVP, 5% and 0% Tween 80 for PPO and POD, respectively. Amounts of highest activity for PPO and POD biocatalysts were calculated 0.42 U/mL and 0.025493 U/mL, respectively. Moreover, the entire inactivation of PPO took place after 30 min at 40 °C and 60 °C and 20 min at 80 °C. However, POD lost 35% of its activity after 30 min at 40 °C and 60 °C. The amount of 6% POD activity was kept after 45 min at 80 °C. Generally, it was indicated that POD was more resistant to thermal treatment than PPO.

Computational modeling of drug separation from aqueous solutions using octanol organic solution in membranes

Continuous membrane separation of pharmaceuticals from an aqueous feed was studied theoretically by development of high-performance mechanistic model. The model was developed based on mass and momentum transfer to predict separation and removal of ibuprofen (IP) and its metabolite compound, i.e. 4-isobutylacetophenone (4-IBAP) from aqueous solution. The modeling study was carried out for a membrane contactor considering mass transport of solute from feed to organic solvent (octanol solution). The solute experiences different mass transfer resistances during the removal in membrane system which were all taken into account in the modeling. The model’s equations were solved using computational fluid dynamic technique, and the simulations were carried out to understand the effect of process parameters, flow pattern, and membrane properties on the removal of both solutes. The simulation results indicated that IP and 4-IBAP can be effectively removed from aqueous feed by adjusting the process parameters and flow pattern. More removal was obtained when the feed flows in the shell side of membrane system due to improving mass transfer. Also, feed flow rate was indicated to be the most affecting process parameter, and the highest solute removal was obtained at the lowest feed flow rate.

Simulation of liquid flow with a combination artificial intelligence flow field and Adams-Bashforth method

Direct numerical simulation (DNS) of particle hydrodynamics in the multiphase industrial process enables us to fully learn the process and optimize it on the industrial scale. However, using high-resolution computational calculations for particle movement and the interaction between the solid phase and other phases in fine timestep is limited to excellent computational resources. Solving the Eulerian flow field as a source of solid particle movement can be very time-consuming. However, by the revolution of the fast and accurate learning process, the Eulerian domain can be computed by smart modeling in a very short computational time. In this work, using the machine learning method, the flow field in the square shape cavity is trained, and then the Eulerian framework is replaced with a machine learning method to generate the artificial intelligence (AI) flow field. Then the Lagrangian framework is coupled with this AI flow field, and we simulate particle motion through the fully AI framework. The Adams–Bashforth finite element method is used as a conventional CFD method (Eulerian framework) to simulate the flow field in the cavity. After simulating fluid flow, the ANFIS method is used as an AI model to train the Eulerian data-set and represents AI fluid flow (framework). The Lagrangian framework is coupled with the AI method, and the particle freely migrates through this artificial framework. The results reveal that there is a great agreement between Euler-Lagrangian and AI- Lagrangian in the cavity. We also found that there is an excellent agreement between AI overview with the Adams–Bashforth approach, and the new combination of machine learning and CFD method can accelerate the calculation of the flow field in the square-shaped cavity. AI model can mimic the vortex structure in the cavity, where there is a zero-velocity structure in the center of the domain and maximum velocity near the moving walls.

ANFIS grid partition framework with difference between two sigmoidal membership functions structure for validation of nanofluid flow

In this study, a square cavity is modeled using Computational Fluid Dynamics (CFD) as well as artificial intelligence (AI) approach. In the square cavity, copper (Cu) nanoparticle is the nanofluid and the flow velocity characteristics in the x-direction and y-direction, and the fluid temperature inside the cavity at different times are considered as CFD outputs. CFD outputs have been assessed using one of the artificial intelligence algorithms, such as a combination of neural network and fuzzy logic (ANFIS). As in the ANFIS method, we have a non-dimension procedure in the learning step, and there is no issue in combining other characteristics of the flow and thermal distribution beside the x and y coordinates, we combine two coordinate parameters and one flow parameter. This ability of method can be considered as a meshless learning step that there is no instability of the numerical method or limitation of boundary conditions. The data were classified using the grid partition method and the MF (membership function) type was dsigmf (difference between two sigmoidal membership functions). By achieving the appropriate intelligence in the ANFIS method, output prediction was performed at the points of cavity which were not included in the learning process and were compared to the existing data (the results of the CFD method) and were validated by them. This new combination of CFD and the ANFIS method enables us to learn flow and temperature distribution throughout the domain thoroughly, and eventually predict the flow characteristics in short computational time. The results from AI in the ANFIS method were compared to the ant colony and fuzzy logic methods. The data from CFD results were inserted into the ant colony system for the training process, and we predicted the data in the fuzzy logic system. Then, we compare the data with the ANFIS method. The results indicate that the ANFIS method has a high potentiality compared to the ant colony method because the amount of R in the ANIFS system is higher than R in the ant colony method. In the ANFIS method, R is equal to 0.99, and in the ant colony method, R is equal to 0.91. This shows that the ant colony needs more time for both the prediction and training of the system. Also, comparing the pattern recognition in the two systems, we can obviously see that by using the ANFIS method, the predictions completely match the target points. But the other method cannot match the flow pattern and velocity distribution with the CFD method.

Molecular separation of ibuprofen and 4-isobutylacetophenone using octanol organic solution by porous polymeric membranes

Molecular separation of pharmaceutical contaminants from water has been recently of great interest to alleviate their detrimental impacts on environment and human well-being. As the novelty, this investigation aims to develop a mechanistic modeling approach and consequently its related CFD-based simulations to evaluate the molecular separation efficiency of ibuprofen (IP) and its metabolite 4-isobutylacetophenone (4-IBAP) from water inside a porous membrane contactor (PMC). For this purpose, octanol has been applied as an organic phase to extract IP and 4-IBAP from the aqueous solution due to high solubility of solutes in octanol. Finite element (FE) technique is used as a promising tool to simultaneously solve continuity and Navier-Stokes equations and their associated boundary conditions in tube, shell and porous membrane compartments of the PMC. The results demonstrated that the application of PMC and liquid-liquid extraction process can be significantly effective due to separating 51 and 54% of inlet IP and 4-IBAP molecules from aqueous solution, respectively. Moreover, the impact of various operational / functional parameters such as packing density, the number of fibrous membrane, the module length, the membrane porosity / tortuosity, and ultimately the aqueous solution flow rate on the molecular separation efficiency of IP and 4-IBAP is studied in more details.

Molecular investigation into the effect of carbon nanotubes interaction with CO2 in molecular separation using microporous polymeric membranes

The use of nanofluids has been recently of great interest to separate acidic contaminants such as CO₂. The main objective of this research is to assess the influence of carbon nanotubes (CNTs) addition to distilled water on enhancing the CO₂ molecular separation through a porous membrane contactor (PMC). For this aim, a comprehensive model is developed based on non-wetted and counter-current operational modes to evaluate the principal mass and momentum transport equations in tube, membrane and shell compartments of PMC. Consequently, a CFD-based axisymmetrical simulation is implemented according to finite element technique (FET) to prognosticate the results. It is found from the results that the addition of 0.1 wt% carbon nanotubes (CNTs) particles to water significantly enhances the mass transfer and consequently the CO₂ molecular separation efficiency from 38 to 63.3%. This considerable enhancement can be justified due to the existence of two momentous phenomena including Brownian motion and Grazing effect, which enhance the mass transport of CO₂ molecules in the PMC. Moreover, the effect of CNTs concentration, some membrane's parameters such as number of hollow fibers and porosity and also some module's design parameters including module radius and length on the CO₂ separation performance are investigated in this paper as another highlight of the current work.

Theoretical investigations on the effect of absorbent type on carbon dioxide capture in hollow-fiber membrane contactors

Chemical absorption of carbon dioxide from flue or natural gas in hollow-fiber membrane contactors (HFMCs) has been one of the most beneficial techniques to alleviate its emission into the environment. A theoretical research study was done to investigate the change in membrane specifications and operating conditions on CO2 absorption using different alkanolamine solvents. The mathematical model was developed for a parallel counter-current fluid flow through a HFMC. The developed model's equations were solved based on finite element method. The simulations revealed that the increase in membrane porosity, length and the number of fibers has a positive impact on CO2 removal, while the gas flow rate and tortuosity enhancement resulted in the reduction of CO2 absorption. Furthermore, it was found that 4-diethylamino-2-butanol (DEAB) with approximately 100% CO2 absorption is suggested as the best solvent in this system, but ethyl-ethanolamine (EEA) with only 46% CO2 absorption had the lowest capacity for CO2 absorption (DEAB>MEA>EDA>MDEA>TEA>EEA). It is worth pointing out that the CO2 absorption can be improved using EEA solvent via change in membrane specifications such as increase in membrane porosity, length and the number of fibres.

Modification of polyethersulfone membrane using MWCNT-NH2 nanoparticles and its application in the separation of azeotropic solutions by means of pervaporation

In this study, functionalized multi-walled carbon nanotubes (MWCNT-NH₂) were synthesized as an additive for the preparation of mixed matrix membranes (MMMs) and then were investigated by FTIR and FE-SEM techniques. Polyether sulfone (PES) polymeric membrane modified with functionalized MWCNT-NH₂ carbon nanotubes was prepared by phase inversion method. The effect of MWCNT-NH₂ on the morphology and property of the PES membrane was evaluated using scanning electron microscopy. The flux, enrichment factor and swelling properties of modified membranes were also used to investigate the membranes performance. The results showed that the flux and enrichment factor in modified PES membrane containing 5 wt.% of functionalized MWCNT-NH₂ carbon nanotubes were obtained 1.2 L.m^-2h^-1 and 3.3, respectively. The influence of methanol concentration on the flux and enrichment factor was investigated. The results corroborated that the flux didn’t change significantly, while the enrichment factor was decreased.

Computational Fluid Dynamic Modeling and Simulation of Hydrocracking of Vacuum Gas Oil in a Fixed-Bed Reactor

A four-lump computational fluid dynamic (CFD) model was proposed for the investigation of vacuum gas oil hydrocracking in a trickle-bed reactor. The experiment was conducted at 360-390 °C and 146 bar in the reactor at three different flow rates. It was found that the modeling predictions of vacuum gas oil cracking agreed well with the experimental measurements. Furthermore, the developed model analyzed the effects of the feed flow rate in the reactors on the concentration distribution and product yield. The maximum yields of the products including distillate (31%), naphtha (14%), and gas (3%) were obtained at the lowest feed flow rate. However, the feed flow rate enhancement from 0.1568 to 0.2059 kg·h^-1 led to the increasing feed concentration and reducing the product concentration at the outlet of the reactor. The latter phenomenon was happened due to the decreasing feed residence time with the increasing mass flow rate.

Changes in the Number of Membership Functions for Predicting the Gas Volume Fraction in Two-Phase Flow Using Grid Partition Clustering of the ANFIS Method

A 2D-bubble column reactor (BCR) including gas and liquid phases is simulated, and fluid characteristics such as gas-phase volume fraction and gas-phase turbulence are extracted from the CFD simulations. A type of heuristic algorithm called adaptive network-based fuzzy inference system (ANFIS) is applied here to simulate the gas-phase volume fraction in a physical system. Indeed, the x direction, the y direction, and gas-phase turbulence are considered as the ANFIS inputs. Changes in the number of inputs as well as membership functions are evaluated and studied to obtain a high level of ANFIS intelligence. By implementing the highest ANFIS intelligence, a surface is predicted, which suggests that the gas-phase volume fraction is based on x and y directions. It provides capability to achieve the amount of gas-phase volume fraction in different points of a 2D-BCR.

Prediction of thermal distribution and fluid flow in the domain with multi-solid structures using Cubic-Interpolated Pseudo-Particle model

A nanofluid is a suspension of very small solid particles in a continuous fluid with significant improvement of heat transfer characteristics in the main liquid. In general, in industrial equipment, the heat transfer rate can be improved with optimization of equipment including the domain structure and using the different types of nanofluids. Still, there is a big challenge to analyze the heat transfer and fluid circulation in the domain. Having nanofluids with experimental observation as using sensors and probes are destructive for the liquid stream and they are costly to observe the details of particles and the original fluid. Over the 20 years, different numerical methods have been implemented in the modeling of the heat and fluid distribution in industrial equipment containing nanofluids. Among all mathematical and numerical methods, Cubic-Interpolated Pseudo-Particle (CIP) model provides a strong potential in the prediction of the fluid structure and heat analysis, when there is a complex structure of thermal walls and high concentration of nanoparticles. However, this method is not frequently used by researchers in nanofluids analysis. In this study, the Cubic-Interpolated Pseudo-Particle model is applied to predict the flow in the square domain. different thermal walls (multi-solid structure) and hot cylindrical wall are specifically used to observe the fluid flow and heat distribution in the domain. Additionally, for a better understanding of the flow in the domain, different numbers of cylinders are used and also different amounts of nanofluid in the continuous fluid are added. The results show that adding more walls in the domain causes the change in the vortex structure. Furthermore, using nanofluid results in better heat transfer rate in the system. The CIP method is also a capable tool to predict the heat and fluid flow in the multi-solid structure domain.

Prediction of Nanofluid Temperature Inside the Cavity by Integration of Grid Partition Clustering Categorization of a Learning Structure with the Fuzzy System

In this study, a quadratic cavity is simulated using computational fluid dynamics (CFD). The simulated cavity includes nanofluids containing copper (Cu) nanoparticles. The L-shaped thermal element exists in this cavity to produce heat distribution along with the domain. Results such as fluid velocity distribution in two dimensions and the fluid temperature field were generated as CFD simulation results. These outputs were evaluated using an adaptive neuro-fuzzy inference system (ANFIS) for learning and then the prediction process. In the training process related to the ANFIS method, x coordinates, y coordinates, and fluid temperature are three inputs, and the fluid velocity in line with Y is the output. During the learning process, the data have been classified using a clustering method called grid clustering. In line with the attempt to rise ANFIS intelligence, the alterations in the number of input parameters and of membership structure have been analyzed. After reaching the highest level of intelligence, the fluid velocity nodes were predicted to be in line with y, especially cavity nodes, which were absent in CFD simulations. The simulation findings indicated that there is a good agreement between CFD and clustering approach, while the total simulation time for learning and prediction is shorter than the time needed for calculation using the CFD method.

Effect of graphene oxide on modifying polyethersulfone membrane performance and its application in wastewater treatment

In the present paper, Graphene Oxide (GO) particles were prepared via Hummer method, and used in synthesis of composite membranes. Polyethersulfone (PES) nanocomposite membranes were synthesized via wet phase inversion technique, and using water as non-solvent. The membrane morphology was investigated using scanning electron microscopy (SEM). Change in the membrane surface hydrophilicity after modification was studied using contact angle measurements. The performance of fabricated PES nanocomposite membranes was measured by evaluating pure water flux, salt rejection, dye retention and heavy metals removal. The results indicated that by increasing the filler percentage up to 5 wt.%, the contact angle between the water droplet and the membrane surface was decreased and the droplet was more dispersed on the membrane surface which implies higher hydrophilicity of the prepared nanocomposite membranes. Moreover, the experimental results corroborated that addition of GO to the membrane significantly improved the pure water flux, salt rejection and heavy metals removal, and can be used as a novel methodology for preparation of high performance membranes in water treatment.