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8.1 Impurities in the solvent stream vs model predictivity
The calibration models which predict liquid speciation in real-time plant operation become invalid when the solvent stream contains impurities. In a CO2 capture plant, these impurities are introduced either from the flue gas stream entering to the absorber, or from the degradation products and heat stable salts or from make-up water. Although most of the research, simulation and modelling work are carried out on synthetic flue gas compositions where CO2, O2 and N2 are the main components, CO2 emission sources such as power plants and manufacturing facilities include several other chemical components. According to (Zhang et al., 2017) flue gas from a 555 MWe natural gas combined cycle (NGCC) power plant includes 4.1% CO2, 7.9% moisture, 12.1 % oxygen and 75.9% nitrogen from the exit of heat recovery steam generator while 550 MWe pulverized coal (PC) has 13.8% CO2, 7.5% moisture, 3.6 % oxygen and 75.1% nitrogen from the exit of flue gas desulpharization unit. The flue gas from the NGCC power plant has a much higher O2 concentration at 12.1% comparing with 3.6% for the PC case which may cause greater oxidative degradation problems for the amine based capture plant.
Lee et al. (2008) found that flue gas from 500 MW pulverized coal combustion power plants could result in impurities of 500 and 3000 parts per million by volume (ppmv) of SO2, 5-60 ppmv of SO3, 10-40 ppmv of NO2 and 5-100 ppmv of HCl. Apart from CO2, NOx, SO2, CO, and small quantities of VOC, ammonia (NH3), chlorine, and HCl may be emitted in the manufacture of Portland cement (Last et al., 2011). emissions from natural gas-fired boilers and furnaces include CO, NOx, CO, CO2, CH4, N2O, VOCs, trace amounts of SO2, and particulate matter (Last et al., 2011). Chloride, nitrate and nitrite are sources for heat stable salts coming from make up water.
Technologies have resulted significant emission reductions such as selective catalytic reduction, flue gas desulphurization and activated carbon filtering. Therefore, the concentration of impurities in flue gas is considerably very low when entering the capture plant. However if they contaminate the solvent stream, the chemistry between CO2 and amine is affected making adverse impact on absorption and desorption performance. For instance, SO2, SO3 and NO2 (in the range of 10 – 40 ppmv) react with MEA and form heat-stable salts such as isothiocynatoethane and tetrahydrothiophene (Lee et al., 2009). Heat stable salts are connected to the amine degradation. Amine degradation can be oxidative or thermal. It is an irreversible transformation of an absorbent solution into other compounds. Ammonia, aldehydes and carboxylic acids are primary oxidative degradation compounds and HEIA (N-(2-hydroxyethyl) imidazolidinone), HEEDA (N-hydroxyethyl) ethylenediamine) and OZD (2-Oxazolidinone) are three main thermal degradation products. Anions of strong carboxylic acids (eg: formic acid and oxalic acid,) form heat stable salts (HSS) (Fytianos et al., 2016). Common HSS species present in amine based gas units are nitrate, nitrite, formate, oxalate, acetate, sulfate, sulfite, phosphate, thiosulphate, thiocyanate and
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glycolate (Stewart et al., 1994). Source of flue gas and plant operational conditions mainly determine the amount of above unwanted chemical components in the capture plant.
The in-situ speciation predicted by the models presented in Table 7.1, may not be reliable when these chemical components present in the system. The solution is two-fold. 1).
New models can be developed (calibration validation demonstration) using samples containing degraded compounds and heat stable salts. 2). Existing models can be recalibrated with the same samples used in this study but with a different variable range.
This range should exclude the Raman active bands related to those impurities. In the PAT perspective, solution 1 is more reliable than solution 2. An experiment was performed to determine the impact of these impurities to the multivariate models developed in this study. Eleven aqueous solutions containing potassium acetate, sodium oxalate, sodium formate, potassium sulphate, sodium hydrogen sulphite, sodium chloride, glycine, sulphanilic acid, nitric acid, sulphuric acid and formaldehyde solution were prepared and their Raman spectra were examined. Fig. 8:1 shows these spectra together with Raman spectra for loaded and unloaded MEA solutions. Spectra shown are baseline corrected and offset for clarity. The dashed boundary show the Raman variable range used for the multivariate models in this study. Almost all solutions exhibit one or more Raman active band in the areas within dashed lines. These areas contain Raman bands which are rich with chemical information for amine species (MEA, MEAH+, MEACOO-) and carbon species (CO32-, HCO3-, MEACOO-) and these bands were used for developing speciation models (refer Table 5.4).
Fig. 8:2 compares the Raman signal between a loaded 30% MEA, an unloaded 30% MEA, aqueous potassium acetate, potassium acetate in loaded MEA and potassium acetate in unloaded MEA. The Raman signals are shown from 780 cm-1 to 3420 cm-1. The purpose of this comparison was to understand the presence of acetate in the amine solvent system to multivariate models. Three Raman active bands ([935, 1350 and 1417]cm-1) were observed for all the acetate containing mixtures in the fingerprint region. Fig. 8:3 shows a similar spectral investigation when oxalate is introduced the amine system. Presence of oxalate introduces several Raman active bands in the fingerprint and low frequency region of the MEA-CO2-H2O system. Similarly Fig. 8:4 to Fig. 8:9 show the effect of sulphate, sulphite, glycine, nitrate, nitrite and aldehyde. This investigation covers only a few number of compounds. However, there are hundreds of such degraded compounds and HSSs in a commercial scale amine based capture plant.
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Fig. 8:1. Intersection of Raman active bands in an MEA-CO2-H2O system by HSSs
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Fig. 8:2. Effect of acetate on CO2 loaded aqueous MEA system
Fig. 8:3. Effect of oxalate on CO2 loaded aqueous MEA system
8.1 Impurities in the solvent stream vs model predictivity
97 Fig. 8:4. Effect of sulphate on CO2 loaded aqueous MEA system
Fig. 8:5. Effect of hydrogen sulphite on CO2 loaded aqueous MEA system
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Fig. 8:6. Effect of glycine on CO2 loaded aqueous MEA system
Fig. 8:7. Effect of nitrate on CO2 loaded aqueous MEA system
8.1 Impurities in the solvent stream vs model predictivity
99 Fig. 8:8. Effect of nitrite on CO2 loaded aqueous MEA system
Fig. 8:9. Effect of aldehyde on CO2 loaded aqueous MEA system
The conclusion from this investigation is that the presence of impurities in the solvent stream reduces the robustness of the multivariate models developed in this study. The speciation will be over predicted or under predicted. It is recommended to perform a similar experiment with thermal and oxidative degradation products which were not included in this experiment. In addition samples from pilot plant facilities which contain degraded solvents can be used for the analysis. New multivariate models should be developed addressing these scenarios.
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