Using multi-objective optimisation in the design of CO2 capture systems for retrofit to coal power stations

An Aspen Plus (R) simulation of an existing power station with a potassium carbonate based carbon capture (CCS) plant including CO2 compression is combined with an Excel based genetic algorithm to optimise the net power output of the power station and amount of CO2 captured for a range of solvent flowrates, lean loading and stripper pressures. The net power output was compared for a CCS plant that is added to the power station without any heat integration to a system where heat integration is maximised by the use of pinch analysis and linear optimisation to calculate the amount of steam required to be extracted from the turbine to meet the additional heating requirements of the CCS plant. The multi-objective optimisation of the process identified that lean solvent loading and stripper pressure will have a large impact on the net power output and amount of CO2 captured. The curves developed in the multi-objective optimisation can provide not only the ability to determine the CO2 capture rate to maximise the profit at a given time due to fluctuating electricity prices, but will also provide the optimum solvent flowrate and lean loading to achieve that maximum capture rate for a given net power. The paper shows that the design of the optimum carbon capture plant will depend not only on the specific capture process but also on the conditions of the power station and the importance in optimising the whole process at the same time. The minimum energy penalty for the potassium carbonate system combined with the reference power station modeled in this paper is 1.02 MJ(e)/kgCO(2) with a reboiler regeneration energy of 5.3 MJ(th)/kgCO(2). In this example optimisation and heat integration was able to reduce the energy penalty by 0.4 MJ(e)/kgCO(2). Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.