Effects of metal-modified catalysts on the desorption performance of mixed amine solutions and machine learning prediction

Authors

• Xunxuan Heng, China University of Mining Technology - Xuzhou
• Zhenzhen Zhang, China University of Mining Technology - Xuzhou
• Longhua Zhu, China University of Mining Technology - Xuzhou
• Li Yang, China University of Mining Technology - XuzhouFollow
• Shugang Xie, China University of Mining Technology - Xuzhou
• Zeyu Wang, China University of Mining Technology - Xuzhou
• Dongtai Han, China University of Mining Technology - XuzhouFollow
• Fang Liu, China University of Mining Technology - Xuzhou
• Kunlei Liu, University of KentuckyFollow

Abstract

The high energy penalty associated with solvent regeneration is still a major bottleneck in amine-based CO₂ capture. In this work, the effects of five solid acid catalysts on the desorption performance of a mixed-amine solvent were compared, and the HY catalyst with superior desorption behavior was selected and further modified with four transition metals (Co, Mn, Gr and Ce) to enhance its catalytic activity. The findings indicate that the CO₂ desorption capacity and maximum desorption rate of the Co-modified HY catalyst reach 48.96 mmol and 0.02211 mmol/s, corresponding to increases of 36.80% and 35.39% relative to the blank system, while the relative regenerative load decreases to 73.12% of the blank. In the later stage of desorption, the value of the desorption factor (DF) reaches 1.33 × 10^–6 mol³·kJ^-1·min^-1, which is 259.46% higher than that of the blank, and the catalyst also exhibits good cyclic stability with negligible impact on the absorption performance. Field Emission Scanning Electron Microscope (FESEM), Brunauer-Emmett-Teller (BET), X-ray Diffraction (XRD) and Fourier Transform infrared spectroscopy (FTIR) characterizations indicate that the catalyst does not alter the chemical composition of the absorbent. Furthermore, six machine-learning models were developed to predict the desorption behavior, among which the Spline-KRR model achieves an RMSE of only 0.00133, with R² and Pearson correlation coefficients of 0.992 and 0.998, respectively, demonstrating high fitting accuracy without obvious outliers and confirming the reliability of the model. Future efforts will continue optimizing this catalyst system, providing robust technological support for greenhouse gas reduction and climate change mitigation.

Document Type

Article

Publication Date

2026

Notes/Citation Information

2772-6568/© 2026 The Authors. Published by Elsevier Ltd on behalf of Institution of Chemical Engineers (IChemE). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Digital Object Identifier (DOI)

https://doi.org/10.1016/j.ccst.2026.100575

Funding Information

This work was supported by National Key Research and Development Program (2022YFE0130000), and the Xuzhou City Science and Technology Project (KC23077), and the Fundamental Research Funds for the Central Universities (2023KYJD1005), and the Natural Science Foundation of Jiangsu Province (BK20240208).

Repository Citation

Heng, Xunxuan; Zhang, Zhenzhen; Zhu, Longhua; Yang, Li; Xie, Shugang; Wang, Zeyu; Han, Dongtai; Liu, Fang; and Liu, Kunlei, "Effects of metal-modified catalysts on the desorption performance of mixed amine solutions and machine learning prediction" (2026). Mechanical Engineering Faculty Publications. 100.
https://uknowledge.uky.edu/me_facpub/100