Ono鈥揔ondo Model for High-Pressure Mixed-Gas Adsorption on Activated Carbons and Coals
详细信息 查看全文
- 作者：Mahmud Sudibandriyo ; Sayeed A. Mohammad ; Robert L. Robinson ; Jr. ; Khaled A. M. Gasem
- 刊名：Energy & Fuels
- 出版年：2011
- 出版时间：July 21, 2011
- 年：2011
- 卷：25
- 期：7
- 页码：3355-3367
- 全文大小：966K
- 年卷期：v.25,no.7(July 21, 2011)
- ISSN：1520-5029
文摘
Theory-based models for adsorption behavior are needed to develop optimal strategies for enhanced coalbed methane (CBM) recovery operations and CO2 sequestration. Although a number of frameworks are available for describing the adsorption phenomenon, the Ono鈥揔ondo (OK) lattice model offers several practical advantages in modeling supercritical, high-pressure adsorption systems. In a recent work, a generalized Ono鈥揔ondo model was developed for predicting pure-gas adsorption on activated carbons and coals. The goal of the present work is to utilize the pure-component, generalized OK model to predict, a priori, the mixture adsorption of coalbed gases. Specifically, the OK model parameters obtained from pure-gas adsorption were used to predict mixed-gas adsorption for selected multicomponent adsorption systems. In addition, the ultimate correlative capabilities of the OK model for mixed-gas adsorption were also investigated by using binary interaction parameters.Traditional modeling of mixed-gas adsorption typically involves the equilibrium gas-phase mole fractions as required model input. However, the experimental gas-phase molar fractions are generally not available for coalbed reservoir simulation studies. Therefore, in this work, an iteration function method is developed for mixed-gas adsorption that does not rely on measurements of gas-phase molar fractions and, therefore, is ideally suited for use in coalbed reservoir simulators. The results indicate that the OK model can be used to (a) predict binary gas adsorption within 2 times the experimental uncertainties, on average, based on pure-component model parameters alone and (b) represent total and individual adsorptions to within their expected experimental uncertainties with the use of one binary interaction parameter.