Energy, exergy, economic and environmental (4E) analysis of a cryogenic carbon purification unit with membrane for oxyfuel cement plant flue gas.

The carbon capture, utilisation and storage (CCUS) process chain is subject to increasing interest, and its overwhelming implementation on the industrial scale appears to be one of the main ways to reduce CO 2 emissions. In this context, the optimisation of a CO 2 purification process for oxy-combustion cement plant flue gases is proposed. This optimisation is based on a multidimensional study on the energy, exergy, economy, and environmental impacts of the process. The results of the optimisations carried out show that it is more favourable to increase the CO 2 recovery above 90%, from an energy, exergy and economic point of view. The analysis of the evolution of the capture cost as a function of the CO 2 recovery shows that for a given carbon tax, there is a minimum for the total cost which includes the sum of the carbon tax contributions for the uncaptured CO 2 and the capture cost. As the unit uses only electrical energy, the cost and the electricity generation will directly impact the capture cost as well as the overall balance in terms of CO 2 avoided. As the electricity price increases from 50 to 250 €/MWh, the CO 2 capture cost increases by almost 250%. An analysis of the parameter uncertainties allows to observe their impacts on the results, and to define a standard deviation from the optimised points and show the robustness of these. Considering the technical parametric uncertainties, the standard deviation on the electrical consumption (3.65 kWh/t CO2), CO 2 recovery (0.09%) and exergy efficiency (0.92%) is limited. • Study of hybrid CO 2 capture system combining cryogenic and membrane technologies. • 4E analyses considering recovery, energy consumption, exergy yield and capture cost. • Maximum exergy efficiency for the cases where the CO 2 recovery is above 90%. • High sensitivity of the electricity price on the CO 2 capture cost. • Uncertainty quantification of modelling assumptions and impact on 4E analyses. [ABSTRACT FROM AUTHOR]