Prediction of thermal and oxidative degradation of amines to improve sustainability of CO₂ absorption process

Amine-based CO₂ absorption is a leading technology for post-combustion carbon capture, but solvent degradation remains a critical barrier to its long-term sustainability. Degradation reduces capture efficiency, increases solvent make-up costs, and generates environmentally harmful by-products, undermining the viability of carbon capture as a sustainable climate mitigation strategy. This study applies advanced machine learning techniques—Artificial Neural Networks (ANN), Random Forest (RF), XGBoost, and Adaptive Neuro-Fuzzy Inference Systems (ANFIS)—to predict thermal and oxidative degradation of amine solvents under varying operating conditions. Experimental datasets for piperazine-based mixtures and tertiary amines were used to train and validate predictive models with high statistical accuracy. The results demonstrate that machine learning can reliably forecast degradation behaviour, reducing dependence on resource-intensive experimental campaigns and enabling more sustainable CO₂ capture systems. By improving solvent stability assessment and process monitoring, this work contributes to the development of more resilient, cost-effective, and environmentally responsible carbon capture technologies, directly supporting global sustainability and climate change mitigation goals.

Citation

Borhani, T.N.; Short, M. Prediction of Thermal and Oxidative Degradation of Amines to Improve Sustainability of CO2 Absorption Process. Sustainability 2025, 17, 10311. https://doi.org/10.3390/ su172210311

Type

Journal article

Language

en

Description

© 2025 The authors. Published by MDPI. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.3390/su172210311

ISSN

2071-1050

EISSN

2071-1050

Collections

Faculty of Science and Engineering