Sharif Digital Repository

In order to define a new temperature correction factor in this study, accurate experimental values were presented for permeability and diffusivity of carbon dioxide and methane in imidazolium-based room temperature ionic liquid: [bmim][PF6] (1-butyl-3-methylimidazolium hexafluorophosphate) immobilized on an inorganic membrane support. Results were presented as a function of temperature and pressure for temperatures within 300-320K and pressures below 50kPa. According to the literature, experimental values of permeability and diffusivity for CH4 in [bmim][PF6] vs. temperature are reported for the first time in this study. Results obtained for CO2 permeability revealed good agreement with data... 

Interesting properties of ionic liquids lead to their application as sub-component of membrane structures. Supported ionic liquid membranes (SILMs) are porous membranes whose pores are saturated with ionic liquids. Two ionic liquids, pr[mim]2[Tf2N]2 [1,3-di(3-methyl-imidazolium) propane bis(trifluoromethylsulfonyl) imide] and, h[mim]2 [Tf2N]2 [(1,6-di(3-methylimidazolium)hexane bis(trifluoromethylsulfonyl)imide)], were synthesized in our laboratory and stabilized on an alumina porous support. Permeability and permselectivity of carbon dioxide and methane using membranes containing these ionic liquids were then measured. The experiments were performed in the pressure range of 10-50kPa and... 

In this study, a numerical and empirical scheme for increasing cooling tower performance is developed by combining the particle swarm optimization (PSO) algorithm with a neural network and considering the packing’s compaction as an effective factor for higher accuracies. An experimental setup is used to analyze the effects of packing compaction on the performance. The neural network is optimized by the PSO algorithm in order to predict the precise temperature difference, efficiency, and outlet temperature, which are functions of air flow rate, water flow rate, inlet water temperature, inlet air temperature, inlet air relative humidity, and packing compaction. The effects of water flow rate,... 

In this study, a numerical and empirical scheme for increasing cooling tower performance is developed by combining the particle swarm optimization (PSO) algorithm with a neural network and considering the packing’s compaction as an effective factor for higher accuracies. An experimental setup is used to analyze the effects of packing compaction on the performance. The neural network is optimized by the PSO algorithm in order to predict the precise temperature difference, efficiency, and outlet temperature, which are functions of air flow rate, water flow rate, inlet water temperature, inlet air temperature, inlet air relative humidity, and packing compaction. The effects of water flow rate,...