Neural computations in modelling of CO2 capture from Gas stream emissions by Sodium Glycinate solution

The present contribution was performed in order to predict CO₂ loading capacity in aqueous sodium glycinate as a novel class of green solution under wide operating range using radial basis function artificial neural network (RBFANN). The predicted CO₂ loading capacity values were in brilliant agreement with those corresponding experimental values. The estimated values of MSE and R-squared were 0.00045 and 0.997, respectively. Accordingly, statistical and graphical analyses confirm satisfactory prediction of our proposed model.     

Recovery enhancement of liquid hydrocarbons in dew point control unit of natural gas processing plant

ABSTRACT

The low temperature absorption method is currently used in a gas-processing unit to control the natural gas dew point. The major problem of this unit is the simultaneous absorption of high amount of methane within heavier hydrocarbons, which leads to low purity of ethane and propane streams. Considering the operational conditions, the mentioned method used in second and third phases, will control the dew point at ?23° c at the best condition. This temperature is not proper for dew point and shows very high amount of hydrocarbons in product gas. In this study, a new process has been introduced in order to drop the gas liquid dew point (natural gas liquid) to ?85° c by self-refrigeration technology. Due to the advanced nature of absorption process, the problem of methane existence in the product of gas liquids is noticeably overcome. In this process, natural gas enters the turbo expander after passing through a refrigeration cycle at ?37° c and it is then expanded in an isotropic process to 2896 kPa pressure. Expander outlet with reflux and condensate produced from a cold separator are fed to an absorption tower with a reboiler and the separation will occur. The advantage of this method is controlling the concentration of methane in the product streams. Simulation results show that the process can daily produce 22,280 barrels of gas liquids with a concentration of 0.5 mole% of methane. In addition, the recovery efficiencies of propane and butane in the newly proposed method are 97.3% and 99.99%, respectively, which show a remarkable advantage over the current trend.     

Direct Sunlight Catalytic Decomposition of Organic Pollutants via Sm- and Ce-Doped BiFeO3 Nanopowder Synthesized by a Rapid Combustion Technique

In the development of photocatalytic processes towards waste water treatment, we have been long faced three foremost obstacles, including catalyst mass production, photon-energy cost and finally catalyst separation process after the treatment. In this study, such problems were addressed through the development of samarium and cerium-doped BiFeO₃ (BFO) nanoparticles (NPs) (Bi_xRE_xFeO₃; RE?=?Sm, Ce, x?=?0.00, 0.01, 0.03, 0.05;) employing a rapid solution combustion synthesis (SCS). This technique is greatly capable of large scale nanopowder production at low temperature. In the SCS procedure, different amount of oxidant-to-fuel (glycine-to-nitrate ion, Gly/NO₃^?) were investigated (Gly/NO₃^??=?0.2, 0.3, 0.37, 0.56, and 0.8). Moreover, a catalytic sunlight irradiation was employed to study the effect of Sm and Ce dopant contents on the photodegradation of benzene and methyl orange (MO) in the aqueous solution. The as-synthesized catalysts were characterized by X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), UV–Visible diffuse reflectance spectroscopy (UV–Vis DRS) and Brunauer–Emmett–Teller (BET)/Barrett-Joyner Halenda (BJH) techniques. The band gap energy of BFO decreaed from 2.14 to 2.06 eV with the increase of Sm³⁺ contents while it increased up to 2.22 eV in the case of Ce-doped BFO. The solar decomposition of the organic pollutants demonstrated the superior performance of Bi_1-xSm_xFeO₃ photocatalyst rather than using cerium in the BFO crystalline structure which is attributed to the increased surface area and visible light harvesting.

Graphic Abstract