Design of adsorption column for reclamation of methyldiethanolamine using homogeneous surface diffusion model
Department of Chemical Engineering, Khalifa University, P.O. Box: 127788, Abu Dhabi, United Arab Emirates
Accepted: 14 September 2020
A predictive simulation model was applied to design a fixed-bed adsorber for studying the removal of Total Organic Acid (TOA) anions from lean Methyldiethanolamine (MDEA) solution using Calcium Alginate Bentonite (CAB) clay hybrid composite adsorbent. Unlike other conventional techniques typically used for packed bed design, the predictive Homogeneous Surface Diffusion Model (HSDM) does not require any test column breakthrough curves a priori. Mass transfer coefficients and isotherm model parameters are provided as input data to HSDM for simulating column breakthrough curves. Various isotherm models were fitted to batch equilibrium data for TOA adsorption on CAB composite adsorbent. Based on Akaike Information Criterion (AIC), Freundlich isotherm was selected and the model parameters were obtained by non-linear regression. Film transfer coefficients and surface diffusivities were determined using appropriate empirical correlations available in the literature. HSDM predictions were first validated using lab-scale column adsorption data generated at lower residence times. The effects of dimensionless numbers (Biot and Stanton) on breakthrough times were investigated using the dimensionless HSDM system and a suitable scale-up regime (Bi& ~& 1 and St& >& 10) was established wherein the sensitivity of mass transfer parameters would be minimal. Using similitude rules on key design parameters, a pilot-scale adsorption column was designed and breakthrough curves were generated using the validated HSDM. The appropriateness of the design technique was verified by comparing the estimated breakthrough data and column design parameters with conventional scale-up and kinetic approaches.
© P. Kannan et al., published by IFP Energies nouvelles, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.