Leveraging Data-Driven Techniques for Dynamic Modeling of a Solar Photovoltaic Thermal-Powered Carbon Capture System

Global energy demand is rising rapidly due to population growth and accelerating industrialization. Integrating a post-combustion carbon capture (PCC) system into fossil fuel power plants has proven effective in reducing excessive CO2 emissions. However, the addition of a PCC system introduces a significant energy penalty, negatively affecting overall power plant performance. To address this challenge, one promising solution is to couple the PCC system with renewable energy technologies, such as a solar photovoltaic thermal (PVT) system. This approach aims to offset the energy penalty by supplying the thermal energy required for the PCC reboiler and simultaneously generating electricity for the power grid. Nevertheless, the integration of a PVT–PCC system, as an emerging technology, exhibits dynamic and multifaceted behaviours, with many unknown parameters and complex process models. Present work aims to investigate the dynamic behaviour of the PVT–PCC plant by leveraging the nonlinear auto-regressive model with exogenous inputs (NLARX) technique to enhance simulation accuracy and system understanding. Based on the simulation analysis, integration of PVT with PCC via steam supply to the reboiler establishes a responsive thermal coupling that strongly governs the CO2 capture and energy performance. Effective integration maximizes CO2 capture efficiency and minimizes energy consumption when solar conditions and load profiles are properly matched, underscoring the potential of renewable-assisted carbon capture systems for sustainable emissions mitigation.