Subspace system identification for CO<inf>2</inf> recovery processes

Abstract

The development of amine scrubbing for coal and natural gas-fired power plants represents a key technology to reduce CO2 emissions. Among the strategies required to maximize CO2 capture during plant operations is the design of tailor-made dynamic models for optimal control. This paper presents a novel application of subspace system identification to a CO2 recovery plant, where major decision variables are considered to develop a simple state space model that can estimate more than sixty process outputs. This model demonstrates to have a great predictive potential, which opens opportunities for the implementation of robust predictive controllers that can quickly adjust to power plant load changes. I. INTRODUCTIONThe CO 2 capture process with amine solvent based absorption and stripping is the most significant industrial method for the removal of carbon dioxide from coal-and natural gas-fired power plants [1]. Several dynamic models have been developed for the CO 2 capture process [2] [3]. However, most of these models require reaction kinetic and thermodynamic properties of the species considered in the process. Therefore, to obtain the simulation results, rigorous property calculations are programmed in specialized software that runs in off-line hardware. Such a modeling implementation approach is useful for model based control design and optimization strategies. Nevertheless, the application of such models during process operations is limited because simulation convergence, time and computational resources are limited in industrial setups.A practical approach to obtain a dynamic model from a plant in operation is to use data-driven empirical models, where the model is made to match the process measurements. Among empirical models, the subspace system identification algorithms have been well accepted in industry not only because of their simplicity and robustness, but also because they provide state space form models that are very convenient for prediction, process monitoring and model based control [4] [5] [6]. This work applies subspace system identification to a CO 2 recovery pilot plant. The empirical model is developed by collecting data from more than seventy sensors and making typical operational changes to perturb the process. Such changes are expected to excite the different modes of operation to determine the number of states required to describe the dynamic response of the process.Although the models developed in this paper are calculated off-line, the equations can be easily incorporated into the

Non-linear system identification of solvent-based post-combustion CO2 capture process

“…In the past, most studies have represented the capture process as a linear model [15][16][17][18][19][20]. It should be noted that the capture process exhibits highly nonlinear behaviour [21].…”