Synergistic assessment of DIPA-AMP Blends for CO2 capture Using thermodynamic and ANN approaches
열역학 모델 및 인공신경망을 이용한 DIPA-AMP 혼합 흡수제의 CO2 포집 시너지 분석
- 주제(키워드) 도움말 CO2 absorption , Regeneration energy , Artificial neural network , e-NRTL , Shortcut method
- 발행기관 국립강릉원주대학교 일반대학원
- 지도교수 도움말 김경민
- 발행년도 2025
- 학위수여년월 2026. 2
- 학위명 석사
- 학과 및 전공 도움말 일반대학원 생명화학공학과
- 실제URI http://www.dcollection.net/handler/kangnung/000000012358
- UCI I804:42001-000000012358
- 본문언어 영어
초록/요약 도움말
The application of amine-based aqueous solutions for chemical absorption is a promising strategy for mitigating CO2 emissions. This study investigates the effects of blended-amine aqueous solutions composed of diisopropanolamine (DIPA) and 2-amino-2-methyl-1-propanol (AMP) on CO2 solubility, absorption behavior, and regeneration energy. CO2 solubility was experimentally measured for solutions with various blending ratios (30:0:70, 21:9:70, 15:15:70, 9:21:70, and 0:30:70, w/w) at temperatures of 313, 333, 373, and 383 K and CO2 partial pressures of up to 450 kPa. The experimental results were correlated using both a thermodynamic model based on the electrolyte non-random two-liquid (e-NRTL) framework and an artificial neural network (ANN) model. The thermodynamic model provided detailed insights into absorption behavior, including species distributions, pH variation, and heat of absorption, while the ANN model offered a simplified yet accurate approach for predicting CO2 solubility. The blended-amine systems exhibited preferential protonation of AMP, leading to AMP-dominated absorption behavior at low CO2 loading ratios. Process-level analysis revealed that regeneration energy—comprising sensible, reaction, and latent heat components—was minimized for the single AMP aqueous solution. Although blending 5 wt% DIPA improved solvent stability, it resulted in an approximately 8% increase in regeneration energy. These results demonstrate the trade-off between process efficiency and solvent stability and highlight the potential of a thermodynamic-ANN hybrid framework for efficient solvent screening and process evaluation in CO2 capture applications.
more목차 도움말
1. Introduction 7
2. Experiments 13
2.1. Materials 13
2.2. CO2 solubility measurement 14
3. Mathematical modeling 15
3.1. Thermodynamic model 15
3.1.1. Chemical and phase equilibrium equation 15
3.1.2. Activity coefficient model 20
3.1.3. Heat of absorption 21
3.2. ANN model 22
3.3.Error functions and evaluation methods 25
3.4. Shortcut method 27
4. Result and discussion 30
4.1. CO2 solubility 30
4.2. Absorption behavior 35
4.2.1 Effect of amine blending on absorption behavior 35
4.2.2. Effect of amine blending on pH and heat of absorption 38
4.3. The performance of absorbents 41
4.3.1. Cyclic capacity based on process conditions 41
4.3.2. Regeneration energy in the amin absorption process 44
4.4. Development of the prediction models 49
5. Conclusion 53
6. Abstract 61
7. Supplementary materials 62
