(647f) Evaluation of Adsorbent Performance By Optimal Temperature Swing Adsorption Process | AIChE

(647f) Evaluation of Adsorbent Performance By Optimal Temperature Swing Adsorption Process


Kawajiri, Y., Nagoya University
Yajima, T., Nagoya University
Adsorptive separation is one of the most promising methods for industrial gas purification. Since process performance, quantified by metrics such as purity, recovery, energy consumption, and productivity, is highly dependent on the combination of adsorbent used and operation method, analysis using a mathematical model is necessary to design a process that achieves the target performance. For this challenge, past studies have conducted predictions of process performance using process models and searched for efficient operating conditions by optimization1,2. In addition, various new porous materials such as zeolites and Metal-Organic Frameworks (MOFs) have been developed in recent years, and their evaluation and comparison remains another challenge, which is tackled by many researchers. Also for this problem, process modeling and model-based optimization to evaluate novel adsorbents can be a promising approach3,4.

However, comprehensive comparison considering a variety of operating strategies optimized for each adsorbent has been rarely performed. Previous studies have focused on optimization of operating conditions, such as cycle time and operating temperature, where a certain operating cycle is assumed and fixed. In such approaches, the optimal cycle, which can be found from a wide variety of TSA operating methods, may not be found for each adsorbent.

The purpose of this study is to identify differences in optimal operating methods for each adsorbent. The operating cycle and operating conditions were optimized for multiple adsorbents, and the differences in performance and operating methods were analyzed. In this study, a rigorous mathematical model based on mass and heat balances was used to the analysis. For optimization, Pyomo, a python-based modeling package, was used to discretize the model both in time and in space, and the interior point method was employed to perform deterministic optimization considering multiple performance criteria such as CO2 purity, recovery, productivity, and energy efficiency.

[1] Ko, D., Siriwardane, R. & Bieggler, L. T. (2005). Industrial and Engineering Chemistry Research, 44(21), 8084–8094.

[2] Agarwal, A. Biegler, L. T. & Zitney, S. E. (2010). AIChE J., 56, 1813-1828.

[3] Shafeeyan, M. S., Daud, W. M. A. W. & Shamiri, A. (2014). Chemical Engineering Research and Design, 92, 961-988.

[4] Burns, T. D., Pai, K. N., Subraveti, S. G., Collins, S. P., Krykunov, M., Pajendran, A. & Woo, T. K. (2020). Environmental Science and Technology, 54, 4536-4544.