The Influence of SO2 and HCl Concentrations on the Consumption of Sodium Bicarbonate during Flue Gas Treatment.

0552161 - ÚCHP 2022 RIV US eng J - Článek v odborném periodiku
Zach, Boleslav - Šyc, Michal - Svoboda, Karel - Pohořelý, Michael - Šomplák, R. - Brynda, Jiří - Moško, Jaroslav - Punčochář, Miroslav
The Influence of SO2 and HCl Concentrations on the Consumption of Sodium Bicarbonate during Flue Gas Treatment.
Energy and Fuels. Roč. 35, č. 6 (2021), s. 5064-5073. ISSN 0887-0624. E-ISSN 1520-5029
Grant CEP: GA TA ČR TE02000236; GA MŠMT(CZ) EF16_026/0008413
Institucionální podpora: RVO:67985858
Klíčová slova: flue gas * deacidification * sodium bicarbonate
Obor OECD: Energy and fuels
Impakt faktor: 4.654, rok: 2021
Způsob publikování: Open access s časovým embargem

The paper focuses on the competitive influence of SO2 and HCl during their removal from flue gas by sodium bicarbonate. The experimental investigation was conducted at a unique combination of conditions: the experiments were conducted on a large scale, with real flue gas, and the removal of SO2 and HCl was studied simultaneously. The aim was to ensure that experimental conditions are reasonably realistic and therefore transferable to practice. The results from experiments at various temperatures within the range from 170 to 255 °C did not indicate a significant influence of temperature. The sorbent conversion varied significantly with the lowest achieved conversion of 26% and the highest conversion of 85%. There was a clear trend showing that higher concentrations of HCl make it more difficult to achieve the selected target concentration of SO2 in clean flue gas, suggesting the preferred reaction of formed sodium carbonate with HCl. The influence of HCl on the removal of SO2 was quantified and is presented in the form of regression models. The found regressors were the initial concentrations of HCl and SO2 in raw flue gas and the target concentration of SO2 in the clean flue gas. The regression models are in good agreement with the experimental data and can be used for the prediction of the behavior of a flue gas treatment system or its optimization.
Trvalý link: http://hdl.handle.net/11104/0327307