Postcombustion CO2 Capture: A Comparative Techno-Economic Assessment of Three Technologies Using a Solvent, an Adsorbent, and a Membrane

By S. Zanco, José-Francisco Pérez-Calvo, Antonio Gasós, Beatrice Cordiano, Viola Becattini, M. Mazzotti. ACS Engineering Au, 2021

193 citations recorded by the source corpus.

: This work compares three postcombustion CO 2 capture processes based on mature technologies for CO 2 separation, namely, (i) absorption using an aqueous piperazine solution, (ii) adsorption using Zeolite 13X in conventional fi xed beds (either vacuum swing adsorption or temperature swing adsorption), and (iii) multistage membrane separation using a polymeric material (with CO 2 /N 2 selectivity of 50 and permeability for CO 2 of 1700 GPU). All three capture plants are assumed to be retro fi tted to a generic industrial CO 2 - emitting source with 12% CO 2 v/v (with 95% relative humidity at the inlet temperature and pressure of 30 ° C and 1.3 bar, respectively) to deliver CO 2 at 96% purity. In the cases of adsorption and membranes, the fl ue gas is dried before feeding it to the CO 2 capture unit. In a fi rst step, the capture processes (i.e., components and design parameters) are optimized based on their technical performance, de fi ned through process exergy requirement and plant productivity; exergy − productivity Pareto fronts are computed for varying CO 2 recovery rates. Second, the economic performance of the processes is assessed through a cost analysis. Estimates of CO 2 capture costs are provided for each process as a function of the plant size and CO 2 recovery rate. The comparative assessment shows that, although the adsorption-and membrane-based processes analyzed may become cost competitive at the small scale (i.e., below sizes of 100 tons of fl ue gas processed per day) and low recovery rates (i.e., below ca. 40%), the absorption-based process considered is the most cost-e ff ective option at most plant sizes and

Subfields: Biomedical Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/ceb91e173a0de80e0df573b3e2d410e2971430c2