Subspace system identification for CO2 recovery processes

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.