Development of an Online Monitoring Method of a CO2 Capture

Publication Date (Web): June 24, 2011 .... The challenges of using FTIR spectroscopy as a monitoring tool for PCC are the presence of many different s...
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Development of an Online Monitoring Method of a CO2 Capture Process Leon F. G. Geers,*,† Annemieke van de Runstraat,† Ralph Joh,‡ R€udiger Schneider,‡ and Earl L. V. Goetheer§ †

TNO, P.O. Box 155, 2600 AD Delft, The Netherlands Siemens AG, Energie Sector, Fossil Power Generation Division, E F NT CCS R&D, Industriepark Hoechst, 65926 Frankfurt-am-Main, Germany § TNO, P.O. Box 155, 2600 AD Delft, The Netherlands ‡

ABSTRACT: One possible way to reduce our carbon footprint is using postcombustion capture (PCC) processes to remove CO2 from flue gases. Because of the highly dynamic characteristics of such processes, real-time performance monitoring is a very complex task. This paper presents a method for monitoring the concentrations of CO2, SOx, and a CO2 capturing agent (β-alanine) during a process in a PCC pilot plant. A partial least-squares (PLS) model was built to estimate these concentrations from Fourier transform infrared (FTIR) spectra of the capturing solvent during processing in a model PCC plant. The model predicts the species concentrations to within 0.05 mol/L, provided that the concentrations stayed within the calibration window of the model. Next to that, it is paramount that the solutions used for model calibration consist of the same solution matrix as the real process medium. The model was eventually used to monitor an emulated PCC process online during 24 h of processing. This demonstrated that events such as saturation of the capturing agent with CO2, water replenishment, and switching to safety protocols can be followed accurately. The combination of an FTIR spectrometer and a PLS model can be used to extract process information in real-time.

’ INTRODUCTION Power plants are major producers of CO2, one of the main greenhouse gases. Our carbon footprint can be greatly reduced by capturing CO2 from the flue gases of these plants. One possible way to achieve this is using postcombustion capture (PCC). The basis of PCC is the reactive absorption of CO2 with water-based absorbents, mostly amines, for example monoethanol amine (MEA),1 piperazine,2 and amino acids. Typically, PCC consists of three different operation units, as shown in Figure 1. These units are shown in the figure as C-01, C-02, and C-03. The first operation unit is a scrubber for the removal of SOx. This is necessary to diminish the accumulation of heat stable salts in the absorption medium. In the second operation unit the flue gases come in contact with the medium that absorbs CO2. The cleaned gases are purged and the CO2-rich solvent is regenerated thermally in the last unit, the desorber. Eventually, the lean solvent is fed back to the absorber. The desorption step is responsible for the major part of the operational costs, due to its high energy consumption. The apparent simplicity of this process might imply that controlling its performance will be an easy task. There are however a number of complicating factors. The composition and the quantity of the flue gas changes over time, due to large load variations of power plants during the day. Temperature swings cause degradation of the solvent, as well as irreversible chemical reactions of the absorbent with oxygen, SOx, and other aggressive species.3 5 Accumulation of other species originating from the flue gas gradually contaminate the solvent and evaporation of water increases the viscosity. Moreover, the degradation of solvents leads to a diminished process efficiency and can lead to an increased rate of corrosion. This can be reduced by reclaiming the solvent, but that requires a control system to regulate the reclamation. In conclusion, PCC is a very dynamic process and therefore controlling the CO2 capture efficiency is a very complex task. r 2011 American Chemical Society

As a first step toward control and optimization, online analysis of the process streams is necessary to monitor the concentrations of absorbed CO2, active absorbent, and contaminating species. In this way, the capture efficiency can be estimated instantaneously and interventions can be timed to maintain solvent quality. Up to now, only the gas streams were analyzed online to monitor the capture efficiency. However, for adequate process control, it is essential to also monitor the composition of the liquid flows. TNO has developed a method on the basis of online attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy. ATR-FTIR is a well-known analytical technique in chemical laboratories. Nowadays, it is applied more and more as a process analytical technology (PAT) for in situ concentration measurements in chemical production.6 One of the main advantages of IR spectroscopy is that the composition of multicomponent mixtures can be analyzed by using only one probe. Many different sensors need to be combined to achieve the same goal with, for instance, electrochemical sensors. Additionally, IR spectroscopy produces results quickly (order of 1 s), which enables the use of online IR spectra for process control. Titrimetry or chromatography based techniques always introduce a lag time, which complicates control. No references were found regarding online monitoring of CO2 absorption processes with FTIR. Another possibility is to use Raman spectroscopy, which has advantages similar to IR spectroscopy. However, this paper only focuses on IR spectroscopy. The challenges of using FTIR spectroscopy as a monitoring tool for PCC are the presence of many different species in the Received: December 1, 2010 Accepted: June 24, 2011 Revised: June 10, 2011 Published: June 24, 2011 9175

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Figure 1. Example of an industrial scale PCC plant.

Figure 2. Influence of absorbed CO2 on the spectrum of 2 mol/L K-β-Ala.

solution and the (chemical) interactions between these species. Moreover, water absorbs infrared light significantly over a wide spectral range, which may result in the obscuration of absorption peaks from other species. Next to that, peaks in the spectra of the different species overlap and may even shift to higher or lower wavenumbers,7,8 so there is no one-to-one relation between the height of specific peaks and species concentrations. As an example, Figure 2 shows the effect of an increasing CO2 concentration on the infrared spectrum of 2 mol/L solution of β-alanine and KOH (equimolar). Around 1550 and 1650 cm 1, strong peaks appear due to N H bend and CdO stretch, respectively. At increasing CO2 concentrations, the N H bend peak shifts to higher wavenumbers, while the CdO stretch peak grows. In a solution of only CO2 in water, two peaks would appear at 1350 and 1450 cm 1 for the asymmetric and symmetric bands of HCO3 . However, the presence of β-alanine causes a characteristic C N stretching peak to appear around 1400 cm 1 that drowns the bicarbonate peaks. Despite the difficulties mentioned above, species concentrations can be determined from FTIR spectra using chemometric methods like partial least-squares regression9,10 (PLSR). PLSR was specifically developed to analyze spectra of multicomponent mixtures to retrieve the concentrations of the species forming

the mixture. A PLSR model is the result of a calibration procedure in which the FTIR spectra are determined for predefined stock solutions with different (known) concentrations. Consequently, the model is used to transform spectra from online measurements in process streams into component concentration values. Contrary to ordinary least-squares regression, in PLSR the number of calibration samples does not restrict the number of wavenumbers used in the model.11 In addition, collinearity between variables (i.e., correlations between spectra of different components) is a problem for regular least-squares modeling, whereas it is a stabilizing advantage for PLSR.6 An important note regarding PLSR is that the technique can only model linear or very weakly nonlinear relations between variables, which is the case for the analysis presented here. For strongly nonlinear relations between variables, techniques such as neural networks12 or support vector machines13 can be used. This paper reports on the development of a quantitative model to calculate the concentrations of CO2, SOx, and a capture agent from FTIR spectra of a model absorption medium. The capture agent of choice was an equimolar solution of βalanine and potassium hydroxide (KOH). This model system was chosen as a representative absorption medium. It is expected that the principle will be the same for alcohol amines such as MEA and components like piperazine. The model was demonstrated in a small scale continuous CO2 capture plant during processing.

’ MODEL DEVELOPMENT To calibrate and validate a model for monitoring the relevant species in a PCC process, 52 stock solutions were made. These solutions each contained different concentrations of the potassium salt of β-alanine (dubbed K-β-Ala, for convenience), CO2, and SOx, within the ranges expected during process operation. Figure 3 is a graphical representation of the complete matrix of stock solutions. The three axes denote the concentrations of the three species. Of the 52 solutions, 37 were used to construct the PLSR model (depicted as solid dots in the figure) and the remaining 15 were used to validate it (open dots). Both ensembles of solutions were picked from the full working range of concentrations (1.5 2.5 mol/L K-β-Ala, 0.1 1.1 mol/L CO2, and 0.0 0.2 mol/L SOx). 9176

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concentrations of K-β-Ala, CO2, and SOx to within 0.05 mol/L (95% confidence interval). At this point, no distinction is made between dissolved CO2, HCO3 , CO32 , and CO2 bound to the amino acid. Only the total sum of all these concentrations is determined. In a similar way, the SOx concentration comprises HSO3 , SO32 , and possibly also SO42 . Finally, a PLS model with four latent variables was constructed of the DOSC filtered data on the basis of the full ranges of wavenumbers (600 4000 cm 1). The number of components for the DOSC filter and the number of latent variables of the PLS model were determined in an iterative procedure to minimize the RMSEP.

Figure 3. The distribution of calibration (b) and validation samples (O) within the working range of the three species.

The solutions were created individually by first dissolving the correct amounts of KOH and β-alanine in water. Then CO2 was added to contained amounts of the absorption liquid samples. The total amount of absorbed CO2 was determined by administering a small sample of the absorption medium to a hot concentrated phosphoric acid solution and monitoring the CO2 concentration in the head space.14,15 Finally, similar to the CO2 addition, predefined amounts of SO2 were added very slowly, ensuring all of the gas was absorbed before spectra could be taken of the stock solution. A Nicolet 6700 FTIR spectrometer (Thermo Electron Corp.) with an ATR cell was used to acquire spectra of all the samples. The wavenumber range of the resulting spectra was 600 4000 cm 1. Prior to creating the PLSR model, a number of preprocessing algorithms was tested for their effect on the rootmean-square error of prediction (RMSEP) of the resulting model. These methods were windowing, uninformative variable elimination (UVE),16,17 multiplicative scatter correction (MSC), standard normal variate (SNV),18 and direct orthogonal signal correction (DOSC). In windowing, only a subrange of wavenumbers is used to develop the model. In the present work, this subrange was between 800 and 1800 cm 1, because it contains the most prominent absorption peaks for the CO2 absorption process. UVE is a sophisticated mechanism to select only the most informative points in the spectra for PLS modeling. Both MSC and SNV are well-known techniques in near-infrared spectroscopy, because they correct for light reflections and scattering that affect the spectra. In fact, MSC and SNV are mathematical transformation techniques that remove signal background and trends from the spectra, so they might also work for ATR-FTIR spectra. And the last technique, DOSC, removes variations from the spectra that are unrelated to the concentrations of the species of interest,19 prior to the construction of the PLS model. Detailed results of the preprocessing tests are not presented here for conciseness and focus. However, some general conclusions will be given. It appeared that windowing and UVE had a negative effect on the correlation coefficient of the model and the root-mean-square error of prediction (RMSEP) slightly increased. MSC and SNV filtering both had similarly small effects. Best results were obtained with a DOSC filter with three components. DOSC filtering the data improved the RMSEP for the PLS model by a factor of 2.5 and the root-mean-square error of calibration (RMSEC) by the same amount. The model is able to predict the

’ MODEL ASSESSMENT Before the model is used to monitor a PCC process during processing, its ability to estimate concentrations is first tested in a relevant environment. For this purpose, a PCC process was emulated in a CO2 capture miniplant. The goal was to determine the validity of concentration values estimated by the PLS model from IR spectra acquired online. Figure 4a shows a schematic representation of the setup and Figure 4b shows a photograph of the rig. A detailed description of this plant is given in Feron and Jansen.20 Artificial flue gas (air with added CO2) is fed to a membrane absorber in which a solvent extracts the carbon dioxide from the gas stream. Clean exhaust gases are vented, the loaded (rich) solvent is heated and subsequently fed to the desorber. At high temperature the absorbed carbon dioxide is stripped from the solvent and isolated in a condenser, while the lean solvent is cooled and fed back to the absorber. This process is operated continuously and automatically. The online measurements in the miniplant were conducted with the same ATR-FTIR spectrometer that was used for the construction of the PLS model. The ATR crystal is covered by a flow cell that allows taking spectra from flowing liquid media. The flow cell is connected to either the rich or the lean stream at the positions indicated by the small black circles in Figure 4a. Spectra are recorded of the fluid passing the flow-cell and the PLSR model converts the spectra into concentration values. At the same time, samples are taken from the fluid and the CO2 concentration is determined with the phosphoric acid method described above. The pilot plant was loaded with 5 L of a 1.74 mol/L K-β-Ala solution, which circulated through the absorber and the desorber at a rate of 4.92 L/h. The absorber was operated at 40 °C and ambient pressure, the desorber at 120 °C and 1.7 bara. During a three day trial, gases with different composition were fed to the plant to remove CO2. On the first day the gas stream (600 L/h) contained 10%-v/v CO2. On day 2, 12%-v/v CO2 and gradually 2%-v/v SO2 were administered. The same gas composition was used on the final day, but also 0.036%-v/v NO2 was added. This was done to see the effect of nitrous oxide compounds on the degradation of K-β-Ala . Additionally, it provided the possibility to assess the robustness of the PLS model against interference of naturally occurring species in flue gases that are not covered in the model. After each day the plant was shutdown to be restarted the next day. Alternatingly, the rich and the lean stream were monitored. For conciseness, only the rich stream results are reported here. Figure 5 and 6 present CO2 and SOx concentrations in the rich stream. In total, 20 samples were taken during the trial. The graphs are subdivided into three regions, one for each day. 9177

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Figure 4. The PCC miniplant.

Figure 5. Predicted (+) and measured (.) concentrations of CO2 in the rich stream.

The values predicted by the PLSR model are plotted with error bars signifying the 95% confidence intervals. The open dots present the measured concentration of CO2 and the dosed concentration of SO2, respectively. Since no direct method was available to measure the absorbed SO2 concentration, it was assumed that all supplied SO2 is absorbed in the solution and none was vented during the trials. Thermodynamic simulations (OLI) proved the affinity of SO2 for the liquid phase is about 7 million times that for the gas phase at desorber conditions. Hence, the assumption that all SO2 is absorbed by the absorption medium is valid. To designate the valid area for the model predictions of the SOx concentration, the calibration range is depicted in Figure 6. From the figures it appears that the model adequately predicts the concentrations of both gases in the absorption liquid. On the last day of the trial, the quality of the model is tested in severe conditions. An unrealistically high amount of SO2 was administered to the absorption liquid, as well as a large amount of NOx, a compound that is not covered by the PLS model. Moreover, the amount of SO2 exceeded the limits of the calibration range. It appears that the predictive quality of the model is decreasing; not only the SO2 prediction is affected, but also the prediction of the CO2 concentration. NOx is not covered as a component in the

Figure 6. Predicted (+) and dosed (.) SOx concentrations for the rich stream. The calibration area is indicated as a darker area.

calibration, its contribution to the FTIR spectra is considered noise. Moreover, the set of stock solutions used to construct the model did not contain any NOx at all, so its contribution to the spectra is not removed by the DOSC filter. In conclusion, the model is able to accurately estimate the concentrations of CO2 and SO2 during a process in the miniplant, as long as the concentrations stay within the calibration range. Next to that, it is paramount that the solutions used for model calibration consist of the same solution matrix as the real process medium.

’ CONTINUOUS TRIAL As proof of concept, the next step was to monitor species concentrations in a continuous fashion; at each moment during the process, the concentrations are reconstructed from the spectra. To this end, a trial was started that ran for one week continuously. The results during the last 24 h of the trial are presented here to illustrate the potential to anticipate unwanted events during processing. The pilot plant was fed with a total gas flow of 500 L/h with 12%-v/v CO2 and a maximum concentration SO2 of 0.13%-v/v. The liquid recirculated at 4.92 L/h on average, the conditions in the absorber were 40 °C and atmospheric pressure, and the desorber was kept at 120 °C and 1.7 bara. 9178

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Figure 7. Results of monitoring the rich stream during a day in a week long trial.

The results are shown in Figure 7. The concentration of K-βAla, CO2, and SOx in the rich stream are plotted as a function of time. Between 16 and 18.5 h, no data was available, since the measurement was temporarily interrupted. During this period the gas flows were switched off. After 18 h only the supply of SOx in air was switched back on. The process was running stable for up to 15 h. The K-β-Ala concentration shows fluctuations between 1.5 and 1.8 mol/L. The K-β-Ala concentration can be seen to periodically rise and drop back down (e.g., at 4.5 and 6 h). The rise is due to water evaporating from the absorption medium. A level sensor monitors the liquid level in the apparatus and replenishes whenever the level is too low. As a consequence, the K-β-Ala concentration sharply drops. However, after 10 h the value steadily increases, not to drop down again before the temporary shutdown. This was due to a failing level sensor, so no water was replenished. During the shutdown period, water was replenished again and the concentration drops accordingly. The CO2 concentration sharply rises after startup, illustrating the saturation of the system with CO2. Apart from a discontinuity between 3 and 4 h, the concentration is stable around 0.9 mol/L. Hence the load is between 0.53 and 0.6 mol CO2 per mol K-β-Ala . According to vapor liquid equilibrium data of CO2 in a 2 mol/L K-β-Ala solution, the maximum load in the absorber (i.e., the rich stream) should be 0.63 mol CO2 per mol K-β-Ala . Hence, the loading of the solvent almost reaches equilibrium. After the temporary shutdown, the feed gas does not contain CO2 anymore and the concentration sharply drops to about 0.3 mol/L and further decays to almost zero. The discontinuity between 3 and 4 h can be understood by looking at Figure 8 in which the liquid recirculation flow rate is plotted for the first 10 h of the trial. After startup the flow rate is decreasing linearly in the first 4 h for reasons yet unknown. As a result, the pressure on the liquid side of the membrane in the absorber will drop. This triggers a safety mechanism that shuts off the membrane unit and redirects the liquid flow through a bypass channel. Hence, no CO2 will be absorbed and the concentration dramatically decreases while the K-β-Ala concentration is unaffected. When the liquid flow resets to its preset value, the membrane unit is switched on again and normal processing resumes. As a further illustration, Figure 9 presents the pH during the first 10 h of processing. During CO2 absorption, the pH drops from around 11 to 9.7 9.8, but in the period of bypassing it increases to 11.3.

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Figure 8. Liquid recirculation flow rate during first 10 h of continuous monitoring experiment.

Figure 9. pH during first 10 h of continuous monitoring experiment.

The SOx concentration monotonically increases from the start of the trial, up to the temporary shutdown. This means that SOx is accumulating in the system and it is not (or not completely) removed from the system. The SO2 supply was 0.663 L/h = 0.0296 mol/h during 15 h (16 h minus the time the flow was bypassed). If it is assumed no SOx is removed from the liquid, in total 0.444 mol of SO2 accumulated in 5 L of liquid. Hence, the concentration of SOx after 16 h has increased by an amount of 0.0888 mol/L. From the online measurements the total amount of SOx accumulated in the liquid during this time equals 0.11 ( 0.05 mol/L. The prediction is still within the 95% confidence interval of the calculated value.

’ CONCLUSIONS A method was presented for monitoring the concentrations of CO2, SOx, and a CO2 capturing agent during a process in a PCC pilot plant. An FTIR spectrometer equipped with an ATR flow cell was connected to the plant to acquire spectra of either the lean or the rich stream of the capturing solvent. The capture agent of choice was an equimolar solution of β-alanine and potassium hydroxide (dubbed K-β-Ala). This model system was chosen as a representative absorption medium. It is expected that the principle will be the same for alcohol amines (e.g., MEA) and piperazine, since both substances have a distinct IR 9179

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Industrial & Engineering Chemistry Research absorption spectrum and the mechanism of CO2 absorption is similar to β-alanine. The monitoring system was based on a PLS model created from 37 stock solutions with known concentrations of CO2, SOx, and K-β-Ala. Validation of the model was done with 15 stock solutions. It appeared that the root-mean-square errors of calibration and prediction decreased by a factor of 2.5 when DOSC filtering was applied. The model was able to predict species concentrations from spectra to within 0.05 mol/L (95% confidence interval). As a proof of principle, the validity of the determined concentration values was estimated from spectra acquired online in a PCC pilot plant. From these spectra, species concentrations were estimated and compared to the measured values from samples of the liquid that were taken at the same time. The model predicted values were all within the 95% confidence interval of the measured values, provided the species concentrations remained within the calibration range of the model. Next to that, it is paramount that the solutions used for model calibration consist of the same solution matrix as the real process medium. The model was eventually used to monitor an emulated PCC process online during 24 h of processing. This showed that events such as saturation of the capturing agent with CO2, water replenishment, and switching to safety protocols can be followed accurately. The combination of an FTIR spectrometer and a PLS model can be used to extract process information in real-time.

’ AUTHOR INFORMATION

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Corresponding Author

*E-mail: [email protected].

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