Assessment of advanced solvent-based post-combustion CO2 capture processes using a bi-objective optimization technique

Charles A. Kang, Adam R. Brandt, Louis J. Durlofsky and Indira Jayaweera

Applied Energy, 2016, vol. 179, issue C, 1209-1219

Abstract: The optimized performance of two advanced CO2 capture processes is compared to that of a monoethanolamine (MEA) baseline for a gas-powered CO2 capture retrofit of an existing coal-fired facility. The advanced temperature-swing processes utilize piperazine and mixed-salt solvents. The mixed-salt treatment involves the use of ammonia for CO2 absorption and potassium carbonate primarily to control ammonia slip. The processes are represented in terms of energy duty requirements within a modular heat integration code developed for CO2 capture modeling and optimization. The model includes a baseload coal plant, a gas-fired subsystem containing gas turbines and a heat recovery steam generator (HRSG), and a CO2 capture facility. A formal bi-objective optimization procedure is applied to determine the design (e.g., detailed HRSG components and pressure levels, gas turbine capacity, CO2 capture capacity) and time-varying operations of the facility to simultaneously maximize net present value (NPV) and minimize total capital requirement (TCR), while meeting a maximum CO2 emission intensity constraint. For a realistic scenario constructed using historical data, optimization results indicate that both advanced processes outperform MEA in both objectives, and the mixed-salt process in turn outperforms the piperazine process. Specifically, for the scenario considered, the base case mixed-salt process achieves 16% greater NPV and 14% lower TCR than the MEA process, and 10% greater NPV and 5% lower TCR than the piperazine process. A five-case sensitivity study of the mixed-salt process indicates that it is competitive with the piperazine process and consistently outperforms the MEA process.

Keywords: CO2 capture; Technology assessment; MEA; Piperazine; Mixed-salt; Process optimization; MINLP; Bi-objective optimization (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (8)

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DOI: 10.1016/j.apenergy.2016.07.062

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