Captura de CO2 con CaO en reactor de lecho fluidizado circulante

  1. Rodríguez Gómez, Nuria
Dirigida por:
  1. Mónica Alonso Carreño Director/a
  2. Juan Carlos Abanades García Director/a

Universidad de defensa: Universidad de Oviedo

Fecha de defensa: 14 de julio de 2010

Tribunal:
  1. Julio Luis Bueno de las Heras Presidente/a
  2. Consuelo Pizarro García Secretaria
  3. Juan Carlos Ballesteros Aparicio Vocal
  4. Ramón Murillo Villuendas Vocal
  5. Ángeles García Borrego Vocal

Tipo: Tesis

Resumen

[EN] The CO emitted from fossil fuel use for energy production is the main cause of climate change. CO2 capture and storage from large stationary sources can be one of the main options for climate change mitigation in a short and medium term. This is the general framework for this PhD, which is mainly focused on the CO2 post-combustion capture process by carbonation and calcination. During the investigation of this emerging capture process a basic analysis of the design characteristics and the cost structure of two CO2 capture processes has been carried out. One of these processes is based on capturing CO2 from flue gases by means of carbonation-calcination cycles, and the other one is aimed at the separation of a pure stream of CO2 resulting from calcination of CaCO3 in cement plants. An experimental pilot plant of two interconnected circulating fluidized beds has been used to test the carbonator reactor in these systems. Many tests of carbonation and calcination have been carried out in continuous mode to demonstrate the viability of CO2 capture with a bed of CaO. A carbonation reactor model based on simple fluid dynamic assumptions, which includes the behavior of CaO as a CO2 sorbent, has been developed. In addition, a more general model for calculating the maximum average conversion of solids reaching the carbonator has been accomplished. Finally, the experimental data obtained from pilot plant experiments have been validated with a carbonation reactor model and the trends of the experimental results are shown to agree with the expected trend predicted by the model.