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.