Ion exchange and temperature: development of mathematical model for the prediction of ion exchange equilibria with respect to temperature for a multi-component system

• Main Author: RUSTAM, MASOOMA
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: 2013
• Subjects: ion exchange; equilibria; temperature; prediction; Davidson; Antarc*; Antarctica
• Online Access: http://hdl.handle.net/11343/38272

© 2013 Dr. Masooma Rustam Ion exchange is a versatile separation, purification and water treatment technology. The most important applications of ion exchange are found in the food and beverage, pharmaceutical, hydrometallurgical, metals finishing, nuclear, petrochemical, power and environmental waste management industries. Ion exchange has also found use for ion exchange chromatography and the remediation of contaminated soil in Antarctica. Prediction and modeling of ion exchange equilibria are essential for the design and development of efficient ion exchange processes. A broad survey of the published literature has shown that a number of semi-theoretical models have been developed which can successfully predict equilibrium behaviour of four components systems from binary system data. No comprehensive work has been conducted to examine the effects of temperature on cation exchange equilibrium behaviour for multi-component system. This study aims to extend the semi-empirical thermodynamics exchange model originally developed by Mehablia et al. (1994) to incorporate temperature dependence for multi-component ion exchange equilibria with minor modification. In this model the Pitzer electrolyte solution model, incorporating the effects of ion association with respect to temperature, is used to describe the non-idealities of solution phase. The equilibrium constant is calculated at each temperature via the approach of Agersinger and Davidson and correlated with temperature. The Wilson model is used to describe the non-idealities of the exchanger phase. The variations of the Wilson binary interaction parameters with temperature are correlated. The proposed model is used to design the more efficient ion exchange processes by allowing designers to optimize the operating temperature of the process. This model may be used to predict performance at temperatures other than those for which experimental data has been collected.