Dynamic modeling of post-combustion amine scrubbing for process control strategy development
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Intensified process designs with advanced solvents have been proposed to decrease both capital and operating costs of post-combustion carbon capture with amine scrubbing. These advanced flowsheets create process control challenges because process variables are designed to operate near constraints and the degrees of freedom are increased due to heat recovery. Additionally, amine scrubbing is tightly integrated with the upstream power plant and downstream enhanced oil recovery (EOR) facility. This work simulated an amine scrubbing plant that uses an intercooled absorber and advanced flash stripper configuration with aqueous piperazine to capture CO2 from a 550 MWe coal-fired power plant. The objective of this research was to develop a process control strategy that resulted in favorable closed-loop dynamics and near-optimal conditions in response to disturbances and off-design operation. Two models were created for dynamic simulation of the amine scrubbing system: a medium-order model of an intercooled absorber column and a low-order model of the entire plant. The purpose of the medium-order model was to accurately predict the absorber temperature profile in order to identify a column temperature that can be controlled by manipulating the solvent circulation rate to maintain a constant liquid to gas ratio. The low-order model, which was shown to sufficiently represent dynamic process behavior through validation with pilot plant data, was used to develop a plantwide control strategy. A regulatory control layer was implemented and tested with bounding cases that represent either electricity generation requirements, CO2 emission regulations, or EOR constraints dominating the control strategy. Satisfying the operational and economic objectives of one system component was found to result in unfavorable dynamic performance for the remainder of the system. Self-optimizing control variables were identified for the energy recovery flowrates of the advanced flash stripper that maintained good energy performance in off-design conditions. Regulatory control alone could not satisfactorily achieve the set point for CO2 removal rate from the flue gas. A supervisory model predictive controller was developed that manipulates the set point for the stripper pressure controller in order to control removal. The straightforward single-input, single-output constrained linear model predictive controller exhibited a significant improvement compared to PI control alone.