Multi-Period Optimisation Design of Post-Combustion CO2 Capture Processes

There have been several design and optimisation studies in the context of amine-based post-combustion CO₂ capture (PC-CC) from coal-fired power stations, and it is well accepted that the principal contributor to the capital-cost (CAPEX) of amine-based PC-CC is the absorber, with the majority of operating cost (OPEX) being accounted for by solvent regeneration[1]. However, the majority of these studies consider only the maximal stable generation (MSG) point of the power station. In reality, many power-plants do not operate consistently at the MSG. Thus, the cost optimal design of a PC-CC process must account for the dynamic operation of many fossil-fuel power-plants. The economic operation of a decarbonised fossil-fired power-plant is a complex trade-off between the time varying cost-of-electricity and cost of fuel. These variables will in turn affect the cost-optimal degree of CO₂ capture, and thus the design and operation of the CO₂ capture plant.

In this work, we present a new approach for the multi-period design of an integrated coal-fired power-plant and an alkanolamine-based CO₂ capture process. A validated[2] rate-based model of an absorber/stripper system for the chemisorption of acid gas in aqueous solvent solutions is used to represent the CO₂ capture process. To account for the non-idealities that are typical of amines and water, the statistical associating fluid theory for potentials of variable range (SAFT-VR)[3]^,[4] is used. This is a molecular approach, specifically suited to associating fluids. The SAFT formalism is used to represent some of the equilibrium reactions characterising the system, thereby simplifying the description of the chemical reactions[5]. The dynamic operation of the power-plant is described using data obtained from a real power station describing the variation in fuel burn rates in response to changing power demand. As we lack a thermodynamically consistent method in describing the degradation effects of SO_x on the solvents, we consider a model flue gas comprising N₂, CO₂ and H₂O. All models are implemented in the gPROMS[6] software package.

Cost-optimal process design and operation are identified via a constrained, multi-parametric optimisation approach. The objective function is the minimisation of total process cost (both CAPEX and OPEX) per unit of CO₂ captured. The design variables are the flowrate and state of the lean solvent stream as well as column geometry.  Key performance indicators such as CO₂ emissions, solvent losses to the environment as well as the minimisation of energy required for solvent regeneration and also minimisation of CO₂ emissions to the atmosphere are included in the optimality criteria.

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[1] Mac Dowell, N. et al, Energy. Environ. Sci., 3, 2010, 1645-1669

[2] Mac Dowell, N., Galindo, A., Jackson, G and Adjiman, C. S., Comp. Aided Chem.  Eng., 28, 2010, 1231-1236

[3] Chapman, W.G., Gubbins, K.E., Jackson, G. & Radosz, M., Ind. Eng. Chem. Res. 29, 1709-1721., 1990

[4] Gil-Villegas, A., Galindo, A., Whitehead, P. J., Mills, S. J. & Jackson, G., J. Chem. Phys. 106 (10), 1997

[5] Mac Dowell, N., Llovell, F., Adjiman, C. S.,  Jackson, G and Galindo, A., Ind. Eng. Chem. Res., 49(4), 1883-1899, 2010

[6] Process Systems Enterprise (PSE) Ltd. http://www.psenterprise.com/index.html

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