Modeling and Simulation
ehsan salehi; Golara Nikravesh; Masoud Mandooie
Abstract
Metal-organic frameworks have emerged as extended-network, tunable, crystalline hydrogen storage adsorbents. The uptake of H2 on Zn4O-based MOFs with different linkers was studied in the current work. The binding energies, consecutive binding energy and step energy of H2-adsorption on MOF-177, MOF-200 ...
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Metal-organic frameworks have emerged as extended-network, tunable, crystalline hydrogen storage adsorbents. The uptake of H2 on Zn4O-based MOFs with different linkers was studied in the current work. The binding energies, consecutive binding energy and step energy of H2-adsorption on MOF-177, MOF-200 and a newly defined MOF (NEW-MOF) have been calculated on different possible sorption sites, using DFT/Dmol3/PBE. The linkers have the same benzene ring in center, but different numbers of phenyl rings, including 3, 6 and 9 phenyl rings in MOF-177, MOF-200 and NEW-MOF around the center ring, respectively. Our study results showed that the binding energy of the H2 molecules with the linker NEW-MOF was -4.165 kcal/mol, more negative than those obtained for MOF-177 (-3.276 kcal/mol) and MOF-200 (-3.438 kcal/mol). The obtained thermo-favorability may be attributed to the less steric hindrance for adsorption of H2 on the MOF with the larger linker. Step energy results showed that the linkers of MOF-177, MOF-200 and NEW-MOF could adsorb 7, 9 and 12 number of H2 molecules, respectively. Results also disclosed adsorbed moles of H2 per 1×1×1 unit cell of the MOFs decreases with increasing the linker length according to the order of 0.263 (for MOF-177), 0.16 (for MOF-200) and 0.137 (for NEW-MOF), mainly due to reduced packing density of the active sites in the MOFs with larger linkers. The most negative binding energy was also tabulated for the perpendicular approaching of H2 molecules to the node of the central phenyl ring with the bonding distance of 3.19 Å from the linker.
Modeling and Simulation
T. Fattahi; E. Salehi; Z. Hosseini
Abstract
The Ethanol-water separation involves a well-known azeotrope that confines the achievement of the ethanol purity to the values higher than 95 wt% using straightforward distillation. Many attempts have been made to identify how it can be possible to produce ultra-pure ethanol (99.95 wt%) for various valuable ...
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The Ethanol-water separation involves a well-known azeotrope that confines the achievement of the ethanol purity to the values higher than 95 wt% using straightforward distillation. Many attempts have been made to identify how it can be possible to produce ultra-pure ethanol (99.95 wt%) for various valuable applications. In practice, minimizing the total cost of the process is of high importance beside having the finished product with utmost purity. As a consequence, finding the best process conditions imposed to apply the simulation and statistical optimization methods in combination. Numerical optimization provides the best trade-offs to achieve the goals. In this research, the separation of the ethanol/water mixture (87 wt%) was simulated using azeotropic distillation in Aspen plus© environment. Indeed, cyclohexane was chosen as an effective azeotrope-former. The UNIQUAC equation was used to describe the phase behavior. The two-column arrangement, in which the first column was used to dehydrate ethanol and the second to recover the entrainer, was applied in this simulation. The effect of important process variables, including the number of the trays in columns and the feed-tray position in each tower on the total capital cost were investigated. Finally, the process variables were optimized via the Response Surface Methodology to minimize the total cost of the process. The results uncovered that the total capital cost would be minimized if the number of the trays in the azeotropic (C1) and recovery (C2) columns were set to 34 and 40, whereas, the feed-tray numbers were adjusted to 19 and 9 respectively.
Modeling and Simulation
P. Sharafi; E. Salehi; H.R Sanaeepur; A. Ebadi Amooghin
Abstract
In this work, the separation of carbon monoxide (CO) from a synthesis gas (syngas) mixture was modeled. It was considered a copper-based adsorbent consisting of cuprous chloride (CuCl) on an activated carbon (AC) support (CuCl/AC) in a pressure swing adsorption (PSA) process. First, the adsorption of ...
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In this work, the separation of carbon monoxide (CO) from a synthesis gas (syngas) mixture was modeled. It was considered a copper-based adsorbent consisting of cuprous chloride (CuCl) on an activated carbon (AC) support (CuCl/AC) in a pressure swing adsorption (PSA) process. First, the adsorption of syngas components on the CuCl/AC adsorbent at 303.15 K was simulated to determine the required data. Next, the PSA process to separate CO from syngas using CuCl/AC absorbent at ambient temperature and pressure of 1000 kPa was evaluated by computational fluid dynamics simulation. The simulation results showed that with an adsorption bed of 2 m in height and 1 m in diameter, CO with appropriate purity (~ 99.5%) is separated from syngas by CuCl/AC. In addition, reducing the inlet feed pressure, or in other words, its velocity or flow can increase the efficiency of the operation (e.g, with a shorter bed height of 0.5 m, a CO purity of more than 99.8% can be achieved at 700 kPa, but with a significant increase in operating cost).
Modeling and Simulation
E. Salehi; S. Tahmasbi; V. Tahmasbi; M. Rahimi
Abstract
An adaptive neuro-fuzzy inference system (ANFIS) was applied to simulate the batch adsorption of triglyceride (TG) from the human blood serum using the cinnamon powder, which has appeared as a potential biosorbent for serum purification, in our previous work. The obtained experimental results were used ...
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An adaptive neuro-fuzzy inference system (ANFIS) was applied to simulate the batch adsorption of triglyceride (TG) from the human blood serum using the cinnamon powder, which has appeared as a potential biosorbent for serum purification, in our previous work. The obtained experimental results were used to train and evaluate the ANFIS model. Temperature (°C), the adsorption time (h), the stirring rate (rpm), the dose of adsorbent (g) and the adsorbent milling time (min) (or the particle sizes of the powder) were considered as the model inputs and TG removal (%) was chosen as the model response. The ANFIS model was trained with 75 % of the available data while 25 % of the remaining data was used to verify the validity of the obtained model. Sobol sensitivity analysis results indicated that the cinnamon dose with 71 % and the adsorbent milling time (or the particle size of the powder) with 15 % impact share were the most influential variables on the TG removal. Furthermore, the specific surface area and the number of reactive adsorption sites were found to be the most important characteristics of the adsorbent. Generally, the results of this study confirmed the advantages of applying the ANFIS and Sobol approaches for the data-based modeling of the bioprocesses.