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RISS 인기검색어
- 검색키워드
- 저자 : Amir Hossein Saeedi Dehaghani (검색결과 4 건)
- 무료
- 기관 내 무료
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Amir Hossein Saeedi Dehaghani,Mohammad Hadi Nematollahi,Reza Abedini 한국화학공학회 2016 Korean Journal of Chemical Engineering Vol.33 No.2
The effect of alumina (Al2O3) nano-particles on gas separation properties of poly(4-methyl-1-pentyne) known as TPX was evaluated. Mixed matrix membranes (MMMs) were prepared with various weight percent (5, 10, 15, 20 and 30) of alumina nano-particles through solution casting along with solvent evaporation method. TPX and consequent MMMs were characterized using FT-IR, SEM and TGA methods. The MMMs permselectivities were determined through pure CO2 and CH4 permeation measurement and CO2/CH4 selectivity calculation. SEM images demonstrated the proper dispersion of alumina nano-particles in TPX matrix. Results from gas permeation showed that the permeability of both CO2 and CH4 as well as CO2/CH4 selectivities were increased with increasing alumina content. Significant increase of CO2 permeability (from 157.43 Barrer at 8 bar and no loading of Al2O3 to 527.78 Barrer at 8 bar and 30 wt% loading of Al2O3) and conspicuous enhancement of selectivity, from 7.73 to 12.51, were obtained in TPX MMMs.
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Reza Soleimani,Amir Hossein Saeedi Dehaghani,Navid Alavi Shoushtari,Pedram Yaghoubi,Alireza Bahadori 한국화학공학회 2018 Korean Journal of Chemical Engineering Vol.35 No.7
Knowledge of the surface tension of ionic liquids (ILs) and their related mixtures is of central importance and enables engineers to efficiently design new processes dealing with these fluids on an industrial scale. It’s obvious that experimental determination of surface tension of every conceivable IL and its mixture with other compounds would be a herculean task. Besides, experimental measurements are intrinsically laborious and expensive; therefore, accurate prediction of the property using a reliable technique would be overwhelmingly favorable. To do so, a modeling method based on artificial neural network (ANN) trained by Bayesian regulation back propagation training algorithm (trainbr) has been proposed to predict surface tension of the binary ILs mixtures. A total set of 748 data points of binary surface tension of IL systems within temperature range of 283.1-348.15 K was used to train and test the applied network. The obtained results indicated that the predictive values and experimental data are quite matching, representing reliability of the used ANN model for such purpose. Also, compared with other methods, such as SVM, GA-SVM, GA-LSSVM, CSA-LSSVM, GMDH-PNN and ANN trained with trainlm algorithm the proposed model was better in terms of accuracy.
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Study effect of different parameters on the sulphate sorption onto nano alumina
Reza Katal,Mehdi Jafari,Amir Hossein Saeedi Dehaghani,Seyedmehdi Sharifian,Mohammed Ali Ghayyem,Mohsen Vafaie Sefti 한국공업화학회 2012 Journal of Industrial and Engineering Chemistry Vol.18 No.1
The aim of this research work is to investigate sorption characteristic of modified nano alumina (n-Al) for the removal of SO42 from aqueous solutions and wastewater. The sorption of SO42 by batch method is carried out. The optimum conditions of sorption were found to be: a sorbent dose of 0.3 g in 100 ml of SO42, contact time of 35 min, pH = 5. In optimum condition, removal efficiency was 85.6% for the SO42. Three equations, i.e. Morris–Weber, Lagergren and pseudo second order have been tested to track the kinetics of removal process. The Langmuir, Freundlich and D–R are subjected to sorption data to estimate sorption capacity. It can be concluded that n-Al has potential to remove SO42 ions from aqueous solutions at different concentrations. It was found that the temperature has positive effect on the process and negative DG values indicated thermodynamically feasible and spontaneous nature of the sorption. Positive value of DS reveals the increased randomness at the solid–solution interface during the fixation of the ion on the active sites of the sorbent. The effect of other anions was studied and it was found the existence of them in the solution has considerable effect on the sulphate removal.
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Mohammad Hadi Nematollahi,Amir Hossein Saeedi Dehaghani,Vahid Pirouzfar,Ebrahim Akhondi 한국고분자학회 2016 Macromolecular Research Vol.24 No.9
In regards to the evolution of desirable properties and chemical structure for high performance of gas separation by membranes, mixed matrix membranes (MMMs), need to be carefully designed and controlled to realize high efficiency. This research investigates the role of the main parameters in the fabrication and performance analysis of MMMs prepared through blending of alumina nano particles (Al2O3) and poly(4-methyl-1-pentyne) known as PMP with various weight percentages of alumina nano-particles in the PMP precursor. Precursor and resultant MMMs were characterized using thermogravimetric analysis (TGA) and scanning electron microscopy (SEM) techniques. SEM images demonstrated the proper dispersion of Al2O3 particles in the precursor matrix. Results indicated that the microstructure of the precursor, blend composition, and the content of nano particles play an important role in gas transport properties of the resulting MMMs. The influence of the percentage of alumina nano particles used in the precursor matrix on the CO2 and N2 permeability and CO2/N2 illustrated a selective trend for the MMMs. Using higher alumina content resulted in membranes with higher permeability and ideal selectivity. The highest rate of CO2 and N2 permeability could be obtained from PMP-alumina with a loading of 30 wt% alumina (PMP30) at 10 bar. Furthermore, these results suggest that PMP30 MMMs (at operating pressures of 8 bar) are exceptional candidates for the CO2/N2 separation, offering enhanced gas pair selectivity in the range of 4.5-5 depending on the operating pressure. The results of this research revealed that high-performance gas separation by MMMs can be realized through adopting a judicious combination of blending and dispersing techniques.
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