Amine-based chemisorption of CO2 is a promising near term option for the decarbonisation of large, fixed-point CO2 emission sources. However, the operational expenditure (OPEX) associated with this technology imposes a significant energy penalty on the power plant. As it is the solvent which determines the thermodynamic and kinetic efficiency of the process, the design of advanced solvents provides a real opportunity to reduce the OPEX. Typically, when one refers to the dynamic operation of CCS-type processes, it is the transient behaviour of the start-up and shut-down of this process which is considered. However, the so-called steady-state operation of power-plants is itself dynamic, i.e., the flowrate, temperature and composition of the inlet flue-gas can vary in real time. Thus, a given solvent and mode of process operation which may be optimal for a particular operating regime, may be sub-optimal for another operating regime. Consequently, the implementation of advanced control strategies present an important opportunity for the intensification of these processes resulting in a significant reduction in the lifetime operational expenditure associated with CCS systems.
This work focuses on the design of an explicit model predictive controller (MPC) for the real-time control of solvent composition and process operation as successfully implemented in other applications such as .To achieve this, we integrate molecular-based fluid theories with high-fidelity process models. Based on this model, an approximate model is developed and a model predictive controller is formulated for the reduced model where our multi-parametric algorithms are applied to derive a suitable and robust explicit MPC controller. By incorporating the explicit controller expressions (control laws) in the original model, a validation step is then carried out.
In this way, we present a unified-systems based methodology for the OPEX reduction of solvent-based post-combustion CO2 capture processes.
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