Integration of sustainability assessment into early-stage carbon capture process design with an explainable AI framework

This study introduces a novel framework for reducing environmental impacts by optimising operating conditions using a surrogate modelling approach integrated with Explainable AI (XAI). Two surrogate models were developed: a sequential surrogate model (SSM) with a two-step structure, and a direct surrogate model (DSM) with a single-step architecture. Both were trained on data from a validated physics-based simulation of a monoethanolamine (MEA)-based carbon capture process to predict environmental impacts across human health, ecosystem quality, and resource depletion. SHapley Additive exPlanations (SHAP) were used to enhance transparency by identifying key input variables influencing outcomes. Multi-objective optimisation was conducted using Particle Swarm Optimisation (PSO) and NSGA-II to determine optimal operating conditions. DSM achieved high prediction accuracy (R² up to 0.995) and lower errors, while SSM offered better interpretability and broader exploration of Pareto-optimal solutions. This study also shows that our framework identified optimum parameters that reduced environmental impacts by 76–88 % compared with the experiment optimum. This framework supports sustainable process design by combining interpretability, predictive performance, and computational efficiency.