Design of Carbon Capture Processes Under Part-load Operating Conditions

Solvent-based carbon capture can reduce CO2 emissions resulting from a continued reliance on fossil power plants for firm power.

These capture processes remove CO2 from flue gases via a solvent.

Careful design via process systems optimization can limit the overall cost of carbon capture, which is both capital- and energy intensive.

As dispatchable power plants operate to meet varying load demand, the design process needs to account for varying operating points.

However, optimizing the design over multiple operating points yields high computational complexity, which is why designs are often based on a single operating point in practice.

Here, we identify optimal carbon capture process designs via stochastic optimization, reducing computational complexity through a data-driven approach-to-equilibrium model of the absorption and desorption processes.

We represent variable flue gas conditions based on part-load operation data of a representative coal power plant.

Accounting for this variability in the design substantially reduces equipment size and total plant cost by 6-9 % at the expense higher operating costs, yielding a reduction in total cost of carbon capture by 0.7-1.7 %.

Given the capital intensity of carbon capture, variability of flue gas conditions therefore should be considered at the design stage, particularly if capture is deployed on plants subject to load following.