Hydrogen separation and purification using crosslinkable PDMS/zeolite A nanoparticles mixed matrix membranes

The transport properties of gases in polydimethylsiloxane (PDMS)/zeolite A mixed matrix membranes (MMMs) were determined based on pure gas permeation experiments. MMMs were prepared by incorporating zeolite 4A nanoparticles into a PDMS matrix using a new procedure. The permeation rates of C₃H₈, CH₄, CO₂, and H₂ were evaluated through a dense homogeneous pure PDMS membrane and PDMS/4A MMMs to assess the viability of these membranes for natural gas sweetening and hydrogen purification. SEM investigations showed good adhesion of the polymer to the zeolite in MMMs. Permeation performance of the membranes was also investigated using a laboratory-scale gas separation apparatus and effects of feed pressure, zeolite loading and pore size of zeolite on the gas separation performance of the MMMs were evaluated. The MMMs exhibited both higher selectivity of H₂/CH₄ and H₂ permeability as compared with the neat PDMS membrane, suggesting that these membranes are very promising for gas separations such as H₂/CH₄ separation.     

Fabrication and characterization of poly(phenylene oxide)/SBA-15/carbon molecule sieve multilayer mixed matrix membrane for gas separation

A novel multilayer mixed matrix membrane (MMM), consisting of poly(phenylene oxide) (PPO), large-pore mesoporous silica molecular sieve zeolite SBA-15, and a carbon molecular sieve (CMS)/Al₂O₃ substrate, was successfully fabricated using the procedure outlined in this paper. The membranes were cast by spin coating and exposed to different gases for the purpose of determining and comparing the permeability and selectivity of PPO/SBA-15 membranes to H₂, CO₂, N₂, and CH₄. PPO/SBA-15/CMS/Al₂O₃ MMMs with different loading weights of zeolite SBA-15 were also studied. This new class of PPO/SBA-15/CMS/Al₂O₃ multilayer MMMs showed higher levels of gas permeability compared to PPO/SBA-15 membranes. The permselectivity of H₂/N₂ and H₂/CH₄ combinations increased remarkably, with values at 38.9 and 50.9, respectively, at 10 wt% zeolite loading. Field emission scanning electron microscopy results showed that the interface between the polymer and the zeolite in MMMs was better at a 10 wt% loading than other loading levels. The increments of the glass transition temperature of MMMs with zeolite confirm that zeolite causes polymer chains to become rigid.     

GNs/MOF-based mixed matrix membranes for gas separations

NU-1000 and graphene nanosheet (GNs) with different loadings have been used as fillers to prepare mixed matrix membranes (MMMs) with polyethersulfone (PES). The high performance of the MMMs has been successfully fabricated for the evaluation of gas separation at 1 bar and various temperatures (20, 40, 60 °C). The successful fabrication of the MMMs were confirmed by using SEM, FTIR, AFM, and XRD. The crystalline nature of GNs and NU-1000 in the MMMs are evidenced by XRD, which confirms the successful fabrication of the MMMs. In addition, the thermal stability of the MMMs was enhanced with the increase of the GNs. Separation performance of H₂ was superior to CO₂, N₂ and CH₄ separation on the MMMs which is a critical for producing energy. The best gas separation results in terms of both permeability and selectivity were obtained with 0.03% GNs and 10% NU-1000. PG3N membrane presented maximum H₂/CO₂, H₂/N₂ and H₂/CH₄ selectivity of 5, 4.2, 3.3 at 20 C, respectively. With an increase in temperature, the permeability increased, while the selectivity of all the MMMs decreased. The MMMs exhibited excellent gas separation capability, which offers unique opportunities for potential large-scale practical applications.     

Gas sorption in H2-selective mixed matrix membranes: Experimental and neural network modeling

Robust artificial neural network (ANN) was developed to forecast sorption of gases in membranes comprised of porous nanoparticles dispersed homogenously within polymer matrix. The main purpose of this study was to predict sorption of light gases (H₂, CH₄, CO₂) within mixed matrix membranes (MMMs) as function of critical temperature, nanoparticles loading and upstream pressure. Collected data were distributed into three portions of training (70%), validation (19%), and testing (11%). The optimum network structure was determined by trial-error method (4:6:2:1) and was applied for modeling the gas sorption. The prediction results were remarkably agreed with the experimental data with MSE of 0.00005 and correlation coefficient of 0.9994.     

Sorption properties of hydrogen-selective PDMS/zeolite 4A mixed matrix membrane

The present study explores the fundamental science of estimating sorption of gases in membranes comprised of inorganic porous fillers within a polymer matrix with a novel semi-empirical correlation. The sorption properties of H₂, C₃H₈, CO₂ and CH₄ were determined in polydimethylsiloxane (PDMS)/zeolite 4A mixed matrix membranes (MMMs) to assess the viability of these membranes for hydrogen purification and natural gas sweetening. Zeolite filling in MMMs results an increase in solubility over neat PDMS membrane. In addition, incorporation of zeolite 4A to PDMS membrane improved H₂ permeation and H₂/CH₄ selectivity. The results confirmed that zeolite 4A can significantly improve the separation properties of poorly H₂-selective PDMS membrane from 0.7 up to 11 and this overcomes the Robeson upper-bound limitation. This improvement was explained referring the Flory–Huggins interaction parameter within MMMs.     

Hydrogen separation and purification with poly (4-methyl-1-pentyne)/MIL 53 mixed matrix membrane based on reverse selectivity

The effect of MIL 53 (Al) metal organic framework on gas transport properties of poly (4-methyl-1-pentyne) (PMP) was determined based on reverse selectivity. Mixed matrix membranes (MMMs) were fabricated considering various weight percent of MIL 53 particles. The reverse MMMs permselectivities were evaluated through measurement of pure CO₂ and H₂ permeation together with calculation of CO₂/H₂ selectivity. The PMP/MIL 53 (Al) MMMs exhibited privileged CO₂/H₂ permselectivity in comparison with the neat PMP. In addition, CO₂ solubility coefficient was significantly increased with increasing the MIL 53 loading, while the H₂ solubility coefficient was almost remained unchanged. Moreover with increasing the feed pressure the permeability of CO₂ and CO₂/H₂ selectivity were dramatically enhanced, especially at higher filler loadings. Therefore, it was observed that the reverse selectivity of MMMs was enhanced so that the Robeson upper bound was overcome. The best yielding membranes (PMP/30 wt.% MIL 53) represented the CO₂ permeability and CO₂/H₂ selectivity of 377.24 barrer and 24.91 for pure gas experiments respectively.     

H2-selective mixed matrix membranes modeling using ANFIS,PSO-ANFIS,GA-ANFIS

The novel contribution of the current study is to employ adaptive neuro-fuzzy inference system (ANFIS) for evaluation of H₂-selective mixed matrix membranes (MMMs) performance in various operational conditions. Initially, MMMs were prepared by incorporating zeolite 4A nanoparticles into polydimethylsiloxane (PDMS) and applied in gas permeation measurement. The gas permeability of CH₄, CO₂, C₃H₈ and H₂ was used for ANFIS modeling. In this manner, the H₂/gas selectivity as the output of the model was modeled to the variations of feed pressure, nanofiller contents and the kind of gas, which were defined as input (design) variables. The proposed method is based on the improvement of ANFIS with genetic algorithm (GA) and particle swarm optimization (PSO). The PSO and GA were applied to improve the ANFIS performance. To determine the efficiency of PSO-ANFIS, GA-ANFIS and ANFIS models, a statistical analysis was performed. The results revealed that the PSO-ANFIS model yields better prediction in comparison to two other methods so that root mean square error (RMSE) and coefficient of determination (R²) were obtained as 0.0135 and 0.9938, respectively. The RMSE and R² values for GA-ANFIS were 0.0320 and 0.9653, respectively, and for ANFIS model were 0.0256 and 0.9787, respectively.     

Molecular-level mixed matrix membranes comprising Pebax and POSS for hydrogen purification via preferential CO2 removal

The molecular-level mixed matrix membranes (MMMs) comprising Pebax^® and POSS have been developed by tuning the membrane preparation process in this work. They exhibit a simultaneous enhancement in CO₂ permeability and CO₂/H₂ selectivity by optimizing the POSS content at extremely low loadings. This is mainly attributed to the large cavity of POSS itself and its effect on the segmental-level polymeric chain packing. More interestingly, the Pebax^®/POSS MMMs reveal a much higher separation performance in the mixed gas test than that in the pure gas test. The highest CO₂/H₂ selectivity reaches 52.3 accompanied by CO₂ permeability of 136 Barrer at 8 atm and 35 °C. This is due to the CO₂-induced plasticization that improves the free volume and polymer chain mobility, hence benefiting the interaction between the polymer matrix and penetrant CO₂. These features may ensure the superiority of Pebax^®/POSS molecular-level MMMs as CO₂-selective membranes in the industrial application of hydrogen purification.     

Fabrication and characterization of highly efficient three component CuBTC/graphene oxide/PSF membrane for gas separation application

Metal organic frameworks (MOFs) with marvelous properties have aroused enormous attention for different application especially gas adsorption and separation. In this regard, fabrication of MOF hybrids with carbon based materials is new strategy to upgrade MOF performance. In this study CuBTC (Copper benzene-1,3,5-tricarboxylic acid)/graphene oxide (GO) composite was synthesized and characterized by BET, SEM, TGA, XRD and FT-IR techniques. Then CuBTC and CuBTC/GO composite were incorporated into polysulfone (PSF) polymer to construct mixed matrix membranes (MMMs). The obtained membranes were characterized by SEM, TGA, XRD and tensile tests and their gas permeability was measured. The results were compared to those of CuBTC/PSF MMMs. It was revealed that CuBTC/GO composite as filler showed superior performance relative to CuBTC. For instance, 15 wt% loading of CuBTC/GO in PSF represented outstanding gas separation behavior while the same loading of CuBTC in PSF deteriorated performance of MMM. Well particle dispersion and favorable polymer-filler interaction were responsible for such observed difference. A high H₂/CH₄ and H₂/N₂ selectivity of 80.03 and 70.46 were recorded for CuBTC/GO in PSF (15 wt%) compared to 44.56 and 40.92 for CuBTC in PSF (15 wt%).     

Artificial neural network application in comparison with modeling allometric equations for predicting above-ground biomass in the Hyrcanian mixed-beech forests of Iran

There is a lack of accurate protocol for predicting aboveground biomass (AGB) and carbon pools in Iran's Hyrcanian forests. This study aimed to figure out the most accurate model for the site-specific prediction of AGB. Site-specific allometric equations on the basis of power-law function and artificial neural network (ANN) type of multi-layer perception based on back-propagation training algorithm were developed to model engineering application for predicting the AGB. The AGB was measured by destructively sampling and weighing 174 fallen trees in the field and breast height diameter (D), total height (H) and basic wood density (ρ) were recorded as explanatory variables for developing allometric equations and ANN models. The findings showed that the simple allometry model including the geometrical variable (D²Hρ) was a highly accurate predictor with the highest certainty among the allometric equations (Adj. R² = 0.91; CF = 1.04; AIC = −430). Furthermore, the product ofD²Hρ and the primary variables were the effective input nodes for training algorithm in the ANN. Statistical issues such as collinearity among the parameters, reliability of parameters and application of dubious empirical equations (…) were the main problems for developing allometric equations; however, there was no limiting factor for designing the models in ANN. According to training and testing data set, the best architecture designed in the ANN model was composed of two hidden layers and 20 neurons in each layer including function of tangent sigmoid. The results showed that the best designed model in ANN predicted the AGB with the higher accuracy (RMSE% = 7.3) than allometric equations. Thus, ANN is offered rather than traditional protocols for predicting the AGB in natural forest ecosystems.     

Preparation of PPO-silica mixed matrix membranes by in-situ sol–gel method for H2/CO2 separation

Poly(2,6-dimethyl-1,4-phenylene oxide)(PPO)-silica mixed matrix membranes (MMMs) were synthesized through the in-situ sol–gel method. The effects of the acid–base catalysis conditions and silica loading weight on the gas separation performance of the membranes were investigated. The functional groups, crystalline structure, thermal stability, and morphology of the MMMs were examined using Fourier transform-infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), atomic force microscopy (AFM), and thermogravimetric analysis (TGA), respectively. The results indicate that using the in-situ sol–gel method to synthesize PPO-silica MMMs is beneficial for improving the adhesion between the silica and polymer and for the dispersion of the silica. The additives significantly enhanced the thermal stability of the membranes. Compared with pure PPO membranes, the PPO-silica MMMs prepared with 10 wt.% acid-silica loading exhibited the best H₂/CO₂ separation properties: H₂ permeability was enhanced from 82.1 to 548.7 Barrer, and an H₂/CO₂ separation ratio of approximately 3.56 was observed.     

Artificial neural network models for biomass gasification in fluidized bed gasifiers

Artificial neural networks (ANNs) have been applied for modeling biomass gasification process in fluidized bed reactors. Two architectures of ANNs models are presented; one for circulating fluidized bed gasifiers (CFB) and the other for bubbling fluidized bed gasifiers (BFB). Both models determine the producer gas composition (CO, CO₂, H₂, CH₄) and gas yield. Published experimental data from other authors has been used to train the ANNs. The obtained results show that the percentage composition of the main four gas species in producer gas (CO, CO₂, H₂, CH₄) and producer gas yield for a biomass fluidized bed gasifier can be successfully predicted by applying neural networks. ANNs models use in the input layer the biomass composition and few operating parameters, two neurons in the hidden layer and the backpropagation algorithm. The results obtained by these ANNs show high agreement with published experimental data used R² > 0.98. Furthermore a sensitivity analysis has been applied in each ANN model showing that all studied input variables are important.     

Improvement of hydrogen-separating performance by on-stream catalytic cracking of silane over hollow fiber MFI zeolite membrane

MFI zeolite membranes were synthesized on porous α-alumina hollow fibers by in-situ hydrothermal synthesis. The membranes were further modified for H₂ separation by on-stream catalytic cracking deposition of methyldiethoxysilane (MDES) in the zeolitic pores. The separation performance of the modified membranes was characterized by separation of H₂/CO₂ gas mixture at 500 °C. Activation of MFI zeolite membranes by air at 500 °C was found to promote catalytic cracking deposition of silane in the zeolitic pores effectively, which resulted in significant improvement of H₂-separating performance. The H₂/CO₂ separation factor of 45.6 with H₂ permeance of 1.0 × 10⁻⁸ mol m⁻² s⁻¹ Pa⁻¹ was obtained at 500 °C for a modified hollow fiber MFI zeolite membrane. The as-made membranes showed good thermochemical stability for the separation of H₂/CO₂ gas mixture containing H₂O and H₂S, respectively.     

Methane dry reforming using oil palm shell activated carbon supported cobalt catalyst: Multi-response optimization

Dry reforming of methane with carbon dioxide was investigated using oil palm shell activated carbon (OPS-AC) supported cobalt catalyst. The cobalt loaded OPS-AC catalysts were prepared by wet-impregnation method and characterized using SEM, FESEM, BET, TPR and TPD. Surface morphology of OPS-AC supported cobalt catalysts exhibited higher porosity, surface area and micropore volume with different densities of cobalt particles and support. Furthermore, greater amount of H₂ chemisorbed and acidity were observed with increasing cobalt contents. Response surface methodology (RSM) was employed to design the experiments based on factorial central composite design. Catalytic testing was performed using a micro reactor system by varying four variables: temperature, gauge pressure, CH₄/ CO₂ ratio and gas hourly specific velocity (GHSV). H₂ and CO yields were analyzed and quantified by gas chromatography with thermal conductivity detector (TCD). Both responses (H₂ and CO) yields were optimized simultaneously using desirability function analysis. Reaction temperature was the most influential variable with high desirability prevalent for both responses. The optimum response values of H₂ and CO yields corresponded to 903 °C, 0.88 bar(g), CH₄/ CO₂ = 1.31 and GHSV = 4,488 mL/h.g-catalyst.     

Effect of hydrogen enrichment on swirl/bluff-body lean premixed flame stabilization

This paper reports the mechanism of hydrogen enrichment in stabilizing swirl/bluff-body CH₄/air lean premixed flame. Large Eddy Simulation (LES) coupled with Thickened Flame (TF) model was performed to resolve the turbulent reacting flow. A detailed chemistry was used to describe the oxidization of CH₄/H₂/air mixtures. Particle Image Velocimetry (PIV) and Planar Laser-Induced Fluorescence of OH (OH-PLIF) simultaneous measurements were conducted to obtain the velocity fields and flame structures respectively. The numerical methods were validated by experimental data and showing good agreements. Both the experimental and numerical results show that, the flame brush attachment tends to leave the inner shear layer with increasing hydrogen addition, which will reduce the risk of flame lift-off. The chemical analyses prove that the attachment of CH₄/air flame is inherently weak. On the one hand, the CH₄/air flame is stabilized by the hot products inside the recirculation. On the other hand, the burnt gas suppresses the oxidation of H₂ and CO through H₂ + OH = H + H₂O and CO + OH = CO₂ + H, respectively. Although the proportion of CH₄ decomposition through CH₄ + OH = CH₃ + H₂O will be reduced by hydrogen addition, the path of CH₄ + H = CH₃ + H₂ will be enhanced significantly. Hydrogen addition will not only increase the overall reaction rate, but also change the combustion intensity at the nozzle exit from relatively weak to strong, which is also important for flame stabilization. The robust flame attachment obtained by hydrogen addition can attributed to the enhanced reactions of H₂ + OH = H + H₂O and CH₄ + H = CH₃ + H₂.     

Development of a comprehensive laminar burning velocity and flame instability profile of refined producer gas (H2:CO:CH4) – Air mixtures at elevated pressures

Unstretched laminar burning velocity (LBV) and intrinsic instabilities of Refined producer gas (H₂:CO:CH₄)-Air mixtures were systematically investigated at 300 K, 1–4 bar and ? = 0.8–1.2 using freely expanding spherical flame method. In H₂/CO/CH₄ rich mixtures, LBV increased with increase in CO (and reduction in CH₄)/H₂ (and reduction in CH₄)/H₂ (and reduction in CO) at any given equivalence ratio, while peak LBV occurred at ? = (1.2 and remained at 1.2)/(1.2 and shifted to 1.1)/(1.1 and remained at 1.1). Computed unstretched LBV using GRI Mech 3.0 and FFCM mechanisms deviated from measurements with initial pressure. From the comprehensive susceptibility analysis (to instabilities), the composition H₂:CO:CH₄ = 0:1:1 had the highest resilience towards thermo-diffusive and hydrodynamic instabilities. Refined producer gas with higher mole fractions of H₂ were vulnerable to intrinsic instabilities, while increment in CH₄ suppressed the susceptibility to hydrodynamic instability and increment in CO suppressed the thermo-diffusive instability.     

H2 permeation and its influence on gases through a SAPO-34 zeolite membrane

This work analysed the permeation of binary and ternary H₂-containing mixtures through a SAPO-34 membrane, aiming at investigating how hydrogen influences and its permeation is influenced by the presence of the other gaseous species, such as CO₂ and CH₄. We considered the behaviour of various gas mixtures in terms of permeability and selectivity at various temperatures (25–300 °C), feed pressures (400–1000 kPa) and compositions by means of an already validated mass transport model, which is based on surface and gas translation diffusion. We found that the presence of CO₂ and CH₄ in the H₂-containing mixtures influences in a similar way the H₂ permeation, reducing its permeability of about 80% compared to the single-gas value because of their stronger adsorption. On the other hand, H₂ promotes the permeation of CO₂ and CH₄, causing an increment of their permeability with respect to those as single gases. These combined effects reflected in interesting selectivity values in binary mixture (e.g., CO₂/H₂ about 11 at 25 °C, H₂/CH₄ about 9 at 180 °C), which showed the potential of SAPO-34 membranes in treating of H₂-containing mixtures.     

Optimization of hydrogen production via toluene steam reforming over Ni–Co supported modified-activated carbon using ANN coupled GA and RSM

Hydrogen (H₂) is a clean fuel that can be produced from various resources including biomass. Optimization of H₂ production from catalytic steam reforming of toluene using response surface methodology (RSM) and artificial neural network coupled genetic algorithm (ANN-GA) models has been investigated. In RSM model, the central composite design (CCD) is employed in the experimental design. The CCD conditions are temperature (500–900 °C), feed flow rate (0.006–0.034 ml/min), catalyst weight (0.1–0.5 g) and steam-to-carbon molar ratio (1–9). ANN model employs a three-layered feed-forward backpropagation neural network in conjugation with the tangent sigmoid (tansig) and linear (purelin) as the transfer functions and Levenberg-Marquardt training algorithm. Best network structure of 4-14-1 is developed and utilized in the GA optimization for determining the optimum conditions. An optimum H₂ yield of 92.6% and 81.4% with 1.19% and 6.02% prediction error are obtained from ANN-GA and RSM models, respectively. The predictive capabilities of the two models are evaluated by statistical parameters, including the coefficient of determination (R²) and root mean square error (RMSE). Higher R² and lower RSME values are reported for ANN-GA model (R² = 0.95, RMSE = 4.09) demonstrating the superiority of ANN-GA in determining the nonlinear behavior compared to RSM model (R² = 0.87, RMSE = 6.92). These results infer that ANN-GA is a more reliable and robust predictive steam reforming modelling tool for H₂ production optimization compared to RSM model.     

Hydrogen production enhancement using hot gas cleaning system combined with prepared Ni-based catalyst in biomass gasification

This paper investigates the hot gas temperature effect on enhancing hydrogen generation and minimizing tar yield using zeolite and prepared Ni-based catalysts in rice straw gasification. Results obtained from this work have shown that increasing hot gas temperature and applying catalysts can enhance energy yield efficiency. When zeolite catalyst and hot gas temperature were adjusted from 250 °C to 400 °C, H₂ and CO increased slightly from 7.31% to 14.57%–8.03% and 17.34%, respectively. The tar removal efficiency varies in the 70%–90% range. When the zeolite was replaced with prepared Ni-based catalysts and hot gas cleaning (HGC) operated at 250 °C, H₂ contents were significantly increased from 6.63% to 12.24% resulting in decreasing the hydrocarbon (tar), and methane content. This implied that NiO could promote the water-gas shift reaction and CH₄ reforming reaction. Under other conditions in which the hot gas temperature was 400 °C, deactivated effects on prepared Ni-based catalyst were observed for inhibiting syngas and tar reduction in the HGC system. The prepared Ni-based catalyst worked at 250 °C demonstrate higher stability, catalyst activity, and less coke decomposition in dry reforming. In summary, the optimum catalytic performance in syngas production and tar elimination was achieved when the catalytic temperature was 250 °C in the presence of prepared Ni-based catalysts, producing 5.92 MJ/kg of lower heating value (LHV) and 73.9% tar removal efficiency.     

Thermodynamic evaluation during the reduction of MWO4 (M = Fe,Mn, Ni) with methane for the production of hydrogen-syngas

In order to overcome one of the most important disadvantages of the partial oxidation of methane (POX) reaction, which deals with the use of pure oxygen as a gas feed, a mixed metal oxide (MWO₄, M = Fe, Mn, Ni) is proposed as an oxygen carrier. The aim of this study is to evaluate the feasibility of these tungstates through the use of thermodynamic analyses and process simulations in an arrangement of two reactors. First reactor: CH₄ + MWO₄ = H₂ + CO + M + W; CH₄ + MWO₄ = H₂ + CO₂ + M + W and possibly CH₄ = C + 2H₂. While in the second reactor: M + W + H₂O = MWO₄ + H₂; C + H₂O = H₂ + CO; C + 2H₂O = 2H₂ + CO₂. Then, the MWO₄ is recycled back to the first reactor to make a continuous process. Simulation results of this process with the different MWO₄ oxides are presented using Aspen Plus^©.