A stochastic grey-box model of a solvent-based carbon capture pilot plant

Solvent-based post-combustion carbon capture (PCC) is a mature and highly exploited carbon capture technology that benefits from dynamic models for design improvement, optimal operations, and model-based control. The current models rely on detailed properties of solvents, have high complexity that limits their use to steady-state dynamics, and typically do not integrate time-dependent changes in data from specific capture plants. Here we present a lumped, low-complexity, data-driven, dynamic, and stochastic grey-box model of a PCC system, validated on experimental data from a Danish carbon capture pilot plant. The model accurately predicts the measured CO 2 capture rates as well as the transient responses of CO 2 concentrations and temperatures in both the absorber and desorber, where we investigate responses to step changes in various input variables (flue gas flow rate, liquid flow rate, and temperatures). Specifically, the hard-to-quantify interfacial CO 2 capture rate is modeled as a linear function of the concentrations and temperatures. The model uses stochastic differential equations for the physical states and maximum likelihood estimation for the parameters, and outperforms a fully physical (white-box) model used as a comparative benchmark. Several grey-box models are explored and the best chosen. We demonstrate that the best model is able to predict dynamic behavior of the pilot plant with high accuracy in most operating scenarios, providing foundation for model predictive control