Environmental Engineering,
Zohreh khoshraftar; Ahad Ghaemi; Hossein Mashhadimoslem
Abstract
In this research, silica gel as a low-cost adsorbent for the uptake of carbon dioxide was investigated experimentally. The samples were characterized by XRD, BET and FT-IR. It shows that as pressure was increased from 2 to 8 bar, the CO2 adsorption capability improved over time. At a pressure of 6 bar ...
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In this research, silica gel as a low-cost adsorbent for the uptake of carbon dioxide was investigated experimentally. The samples were characterized by XRD, BET and FT-IR. It shows that as pressure was increased from 2 to 8 bar, the CO2 adsorption capability improved over time. At a pressure of 6 bar and a dose of 1 g of silica gel, the impact of temperature (25, 45, 65, and 85 °C) on the CO2 adsorption capacity (mg/g) was determined. The process behavior was investigated using isotherm, kinetics and thermodynamic models. As the temperature rises at a constant pressure, the adsorption capacity decreases. The experimental data of the carbon dioxide adsorption using silica gel have a high correlation coefficient with both Langmuir (0.998) and Freundlich (0.999) models. The results of the carbon dioxide adsorption kinetics with the silica gel adsorbent show that the correlation coefficient (R2) of the second-order model and Ritchie's second model are equal to 0.995 and have the highest value. The total pore volume was 0.005119 (cm3 g-1) and the specific surface area was 2.1723 (m2g−1). The maximum CO2 adsorption capacity at 25 °C near 8 bar was 195.8 mg/g.
Modeling and Simulation
M. Khajeh Amiri; A. Ghaemi; H. Arjomandi
Volume 16, Issue 1 , March 2019, , Pages 54-64
Abstract
In this work, zeolite 13X with porosity structure has been used as an adsorbent for adsorption of CO2 flue gas. The effect of operating conditions including pressure and time on adsorption capacity were investigated. The experiments conditions are constant temperature, the range of pressure 1 - 9 bar ...
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In this work, zeolite 13X with porosity structure has been used as an adsorbent for adsorption of CO2 flue gas. The effect of operating conditions including pressure and time on adsorption capacity were investigated. The experiments conditions are constant temperature, the range of pressure 1 - 9 bar and the registration of adsorption capacity with passing of time. Experimental data were adjusted with adsorption isotherm models including two and three parameters isotherm. Also the process was studied in terms of kinetic models and after the implementation of the experimental data with kinetic models, the speed of this process equations were obtained. The best kinetic model for this process was selected first order equation. The results showed that adsorption capacity of 13X is 71.5 mg/g at pressure of 8 bars. Also the result indicate that 13x has high capacity at low pressures. With regard to achieved results for adsorption isotherm modeling, the adsorption isotherm followed of the three-parameter and among three-parameter models, Toth isotherm can be interpreted the process. Also the results of the fixed bed indicate a very high adsorbent selectivity to carbon dioxide adsorption and there was little oxygen and nitrogen adsorption.
Modeling and Simulation
M. Mahmoudian; A. Ghaemi; H. Hashemabadi
Volume 13, Issue 2 , April 2016, , Pages 46-56
Abstract
In the Bayer process, the reaction of silica in bauxite with caustic soda causes the loss of great amount of NaOH. In this research, the bound-soda losses in Bayer process solid residue (red mud) are predicted using intelligent techniques. This method, based on the application of regression and artificial ...
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In the Bayer process, the reaction of silica in bauxite with caustic soda causes the loss of great amount of NaOH. In this research, the bound-soda losses in Bayer process solid residue (red mud) are predicted using intelligent techniques. This method, based on the application of regression and artificial neural networks (AAN), has been used to predict red mud bound-soda losses in Iran Alumina Company. Multilayer perceptron (MLP), radial basis function (RBF) networks and multiple linear regressions (MLR) were applied. The results of three methodologies were compared for their predictive capabilities in terms of the correlation coefficient (R), mean square error (MSE) and the absolute average deviation (AAD) based on the experimental data set. The optimum MLP network was obtained with structure of two hidden layer including 13 and 15 neurons in each layer respectively. The results showed that the RBF model with 0.117, 5.909 and 0.82 in MSE, AAD and R, respectively, is extremely accurate in prediction as compared with MLP and MLR.