Modeling MEA with the CPA equation of state: A parameter estimation study adding local search to PSO algorithm

Letícia Cotia dos Santos, Frederico Wanderley Tavares, Victor Rolando Ruiz Ahón, Georgios Kontogeorgis

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Due to the intensification of environmental constrains combined with the tendency to process crude oils with high C/H, S/H ratios and natural gas with increasing CO2/CH4 and H2S/CH4 ratios, acid gas removal from gas streams is probably the most required process in the petroleum and gas industries nowadays. Absorption with aqueous alkanolamines such as MEA, is one commonly used process for this purpose. On modeling MEA with CPA, it has been shown that only the co-volume b parameter does not present local minima near the final solution and, also, VLE data are not sufficient to estimate reliable parameters for MEA. This work proposes adding LLE information systematically in the CPA parameter estimation procedure. At first, the parameter search space is defined by the results from the PSO sensitivity analysis for VLE considering the experimental error for vapor pressures and liquid densities (objective function cut off). Then, two possible methodologies are discussed: the first one uses all the possible parameter sets and check them against the LLE and VLE experimental data. The second method explicitly incorporates LLE information into the objective function and uses both PSO and PSO-simplex hybrid algorithm to improve the convergence and refine the final solution. With this methodology it was possible to model simultaneously LLE and VLE. The CPA was then applied for a mixture containing cross-association (MEA-water) and the results show good agreement with experimental data indicating the effectiveness of the proposed strategies.
Original languageEnglish
JournalFluid Phase Equilibria
Volume400
Pages (from-to)76-86
ISSN0378-3812
DOIs
Publication statusPublished - 2015

Keywords

  • Cubic plus association (CPA)
  • LLE
  • MEA
  • Parameter estimation
  • PSO algorithm
  • VLE

Cite this