(681b) Improving Flexibility and Energy Efficiency of a Post-Combustion CO2 Capture Process Using Economic Model Predictive Control

Rising
concerns for climate change has resulted in strict environmental regulations on
emission of CO₂ into the atmosphere by emission sources such as
fossil-fueled power plants. To reduce CO₂ emissions from power
plants, electricity companies have diversified their generation sources.
However, fossil fuels will remain an integral energy generation source as they
are more reliable compared to the renewable energy sources. Therefore, to
reduce emissions, carbon capture and storage (CCS) must be implemented.

Among the
various options for CO₂ capture, Post-combustion CO₂
Capture (PCC) using reactive solvents is considered the most matured and viable
option as it can easily be retrofitted to existing power plants. However, a
major setback to the realization of this technology is its high regeneration
heat requirement. Studies have shown that PCC attached to a power plant reduces
the power plant's efficiency from about 40 % to 30 %. An overview of a PCC
plant attached to a power generation plant is shown in the figure below.

As can be
seen, steam from the power is sent to the PCC plant for CO₂
regeneration. This diversification as well as changing electricity demand could
hinder effective economic operation of a PCC plant attached to the power plant
to reduce CO₂ emissions. Again, unavailability of steam to the PCC
plant during the peak periods could hinder process operation. To tackle this
problem, efficient control algorithms are necessary. Many different control
strategies have been applied to the post-combustion capture process in
literature.  The
proportional-integral (PI) algorithm as well as tracking Model Predictive
Control (MPC) has been applied to the process. However, economic MPC has not
been applied to the PCC process, hence, the need for this to be investigated.

In this
work, both tracking and economic model predictive controllers (MPC) were
applied to a PCC plant and their economic performances compared under different
scenarios namely: no emission limit, emission limit and cap-and-trade system.
These scenarios were formulated in the optimization problem as soft constraints
on the CO₂ emission. First, the tracking MPC was well designed and
open-loop tests conducted to ensure the economic performance is as close as
possible to that of economic MPC. Second, an appropriate setpoint update
strategy from real-time optimization (RTO) was determined. Finally, the
economic performances of the two controllers were compared for time-varying
process operation. The results show that economic MPC was able to determine the
optimal control policy depending on the scenario without further tuning while
tracking MPC needed retuning in some instances. Again, the operation of the PCC
plant under EMPC resulted in reduced heat duty. Therefore, economic MPC can
potentially improve the operation of the post-combustion CO₂ capture
process.