Carbon capture plant model identification through simultaneous state and parameter estimation with estimable variable selection

Abstract

This paper addresses the challenge of estimating both states and parameters for post-combustion carbon capture plants (CCPs), with the goal of predicting ${\mathrm{CO}}_2$ capture using temperature measurements. We develop a first-principle model of the CCP, modified to align with the actual industrial process, and employ simultaneous state and parameter estimation within a moving horizon estimation (MHE) framework. Sensitivity analysis and orthogonalization are used in variable selection step to select estimable states and parameters, enhancing estimation accuracy and computational efficiency. Real industrial data is used to validate the model, and comparisons with alternative estimation methods highlight the effectiveness of our approach. This work contributes practical insights into state and parameter selection, estimation method modifications for differential algebraic equation (DAE) systems, and data pre-processing in real-world settings.