**3.4. Data processing**

The aim of the data processing is to condition the measurement signal by removing as much as possible of unwanted structure from the data. Including noisy spectra in regression model

concentration. By mixing different ratios of CO<sup>2</sup>

per mole solvent.

194 Carbon Dioxide Chemistry, Capture and Oil Recovery

**3.2. Analysis of reference samples**

1 M HCl, barium chloride (BaCl<sup>2</sup>

Determination of true CO<sup>2</sup>

out by the *BaCl<sup>2</sup>*

*of Sampling* [16].

**3.3. Raman spectroscopy**

**3.4. Data processing**

time of 60 s to get a good signal-to-noise ratio.

 *unloaded*), a series of 38–42 different CO<sup>2</sup>

method (refer Section 3.2) was carried out to measure its true CO<sup>2</sup>

concentration (CO2

is popular and a well-established method to analyze CO<sup>2</sup>

10 mL glass reactors. After the solution in each glass reactor reached equilibrium, a titration

T50 were used for the experiment. The titration procedure is discussed in [22]. This titration

both in laboratory and industrial applications. However, the method needs extensive chemical preparation; and it takes more than 2 h to analyze one sample and needs expertise and tedious manual work. The accuracy of the PLSR models is strongly affected by the accuracy and results of this analysis. All the sampling errors during sample extraction from stock solution, chemical preparation, weighing, transferring samples, dilution, filtering and titration were identified using a fish-bone analysis and addressed based on the knowledge from *Theory* 

The Raman spectroscopy is a process analytical technique which can be used for batch-wise experiments where measurements are taken manually such as in laboratory tests, or for continuous operations such as in process plants where measurements can be taken continuously in each time interval. The instrument output is called a Raman spectrum which is a plot of intensity of scattered light (called Raman intensity) versus energy difference (given by wavenumber in cm−1). If the objective is to measure the concentration of a chemical sample using Raman spectroscopy, then the peaks and their intensity in a Raman spectrum indicate information about the type of chemicals and their composition respectively for the measured sample. Kaiser RXN2 Analyzer (as shown in **Figure 2a**) with 785 nm laser wavelength, 400 mW laser power and 100–3425 cm−1 spectral range was the Raman spectrometer used in this experiment. An immersion optic probe was connected to the RXN2 Analyzer via a fiber optic cable (refer **Figure 2b**). During each measurement, the immersion probe was positioned vertically into the 10 mL glass reactor using a stand. The glass reactor was covered by a black box and aluminum foil to avoid interaction with fluorescence from external light sources (as shown in **Figure 2c**). Each scan was acquired as an average of six scans with 10 s over a total exposure

The aim of the data processing is to condition the measurement signal by removing as much as possible of unwanted structure from the data. Including noisy spectra in regression model

 *titration-precipitation method*. 0.1 M Sodium hydroxide (NaOH), 0.1 M HCl,

stock solution (*CO2*

moles CO2

loaded amine solution with the other amine

loading) in alkanolamine samples was carried

) purchased from Merck (99%) and the titrator Mettler Toledo

loaded samples were prepared in

loading in absorption processes

concentration in units of

**Figure 2.** RXN2 analyzer; (a) Raman analyzer with exciting source and detector; (b) Raman immersion probe; (c) sample is in an enclosed sample compartment (black box) and probe, which is mounted vertically, takes measurements.

**Figure 3.** Unprocessed Raman spectrum for four types of CO<sup>2</sup> loaded amine solutions.

calibration results in poor correlation between property to be predicted and measured. The RXN2 analyzer was used to generate a data matrix of n × p where n is number of objects (e.g., different samples or measurements with time) and p is 3326 of Raman wavenumbers. This data matrix contains the chemical fingerprints of the objects and typically different types of noise. This noise comes from interference of other chemical components, laser input variations, noise from fiber optic cable or inadequate path length for the laser.

**Figure 3** shows some raw spectra for four types of CO<sup>2</sup> loaded amines where normal features of Raman spectra are visible such as baseline shifts, scattering and peak overlaps. These features avoid the raw spectra to be used directly for the calibration model. Preprocessing of raw spectra is recommended to improve the predictability of a regression model. There are several preprocessing methods available for PAT applications [23, 24]. The optimal choice of preprocessing method is specific to the application and instrument. In this analysis, the baseline correction based on the Whittaker filter [25] and mean centering was applied which provided the lowest prediction errors as presented by root mean square error of prediction.

find the optimal choice which is not possible and practical. At such instances, variable selection methods such as iPLS (interval partial least squares regression), genetic algorithms (GA), selectivity ratio and jack-knife can be used [26]. Similar to finding the optimal preprocessing method, finding the optimal variable range is specific to the application and is an iterative procedure. In this example, the selection of the variable range for each model was performed which included vibrational modes of important carbon species available in literature [18]. Due to the overlapping of peaks, it was difficult to isolate the exact area for related vibrational modes and therefore 1000–1500 cm−1 and 1000–1164 cm−1 regions were selected for primary

The calibration and validation results for each of the four respective PLSR models for CO<sup>2</sup> concentration in MEA, MDEA, 3-AP and 3-DMA1P solutions are presented in this section. Data preprocessing and model development were implemented in PLS Toolbox 8.21 in the

The model behavior is presented using four graphs for each amine solution (from **Figures 4**–**11**)

sus the measured loading. In the plots, *c* represents calibration samples and *v* represents valida-

The regression coefficient plot is an indication on what weight of each wavenumber contributes to the prediction. The higher the regression weight, the higher the importance of the assigned wavelength to the prediction. The regression plot can be mapped with the vibrational modes of

The score plot gives the overview of the span of the calibration data and validation data. Even

the score plot, one can understand whether the sample is highly or less concentrated with

predicted versus measured plots show how far the measured values deviate with respect to the predicted values for the validation set. This deviation is interpreted with statistical param-

, RMSEP and offset. When *test set validation* is used, this plot gives an image on

. Score plot can also be used to identify outliers which mean samples that show nonrepresentative *x variables.* There can be various reasons for outliers such as impurities in samples, different sample type, instrument error and sampling errors. During the model development stage, if such outliers are identified, they should be carefully analyzed and removed from the model. When the model is used for process monitoring, visualization of such outliers helps in the quick identification of the abnormal behavior in the process. With the increasing number of PLS components, the prediction error of the models varies. A plot of RMSEP versus number of PLS components is used to conclude the required number of PLS components for the model. It is always preferable to select a small number of PLS components to avoid model complexities. An optimum number of PLS components avoids the risk of overfitting of the

loading.

loading (*y variable*), by visualizing the sample location in

loading ver-

Process Analytical Technology for CO2 Capture http://dx.doi.org/10.5772/intechopen.76176 197

concentration.

concentration. The

and tertiary amines respectively which made the models with the lowest RMSEPs.

which are the score plot (t1–t2), regression coefficients, RMSEP and predicted CO<sup>2</sup>

tion samples. Samples are numbered as 1, 2, 3, 4 onwards with decreasing CO2

model. The aim of the model is to use the instrument for predicting CO<sup>2</sup>

carbon species which are chemically important to the total CO2

**3.5. Results**

CO2

eters such as *r*<sup>2</sup>

MATLAB 2016a software (MathWorks Inc.).

without knowing the value of CO<sup>2</sup>

how the model will align with future data.

#### *3.4.1. Variable selection*

When CO2 is reacted with an amine, it is converted into different carbon ions as shown in Eqs. (1)–(10). The variables related to all these carbon species should be included as *x variables* for PLSR modeling. However, with reference to **Figure 3**, isolation of these variables from the rest of the spectra is not straightforward. The spectroscopy shows measurements in the wavelength range from 100 to 3425 cm−1. In the model development, only a selected range of wavelength was included. The spectra for all the solvents (**Figure 3**) show noise which means unwanted variation, both in higher and lower frequency ranges. The middle frequency range shows a flat behavior with some offsets between measurements. The wavelength below 400 cm−1, between 1600 and 2600 cm−1 and after 3100 cm−1 was excluded to remove this noise possibly arising either from the instrument, cables or measurement probe. Since these measurements are associated with a chemical reaction, there are frequencies which are assigned to vibrational mode of molecules. The reaction between alkanolamine and carbon dioxide produces carbonate, bicarbonate and carbamate ions for primary and secondary amines. Reaction between CO<sup>2</sup> and a tertiary amine does not form carbamate. For a CO<sup>2</sup> -loaded amine solution, the vibrational modes from carbon species and amine species appear in the Raman spectra collectively which makes it more complex to study. One can understand what kind of chemical species present in a chemical system by observing peaks which vary with different concentrations. On the other hand, knowing what kind of chemical species present in the system helps to identify the peaks which should response differently when concentration changes. This fact is very important when selecting variables for modeling. Having a small number of variables makes the model easier to interpret.

There are disadvantages having unwanted variables in the model. The biggest challenge is overfitting, which makes the model correlates with the property to be predicted (*y* variable) during the model development stage, but future samples predict poorly. For instance, when developing a PLS model to predict total CO2 loading in an aqueous MEA solution, important *x* variables are the vibrational modes coming from carbon species (i.e., carbonate, bicarbonate and carbamate ions). According to the vapor-liquid equilibrium in the system, the protonated MEA and free MEA concentrations correlate almost linearly with CO<sup>2</sup> loading less than 0.5 mol CO2 /mol MEA [9]. Therefore, by mistake, one can include the variables assigned to protonated MEA and free MEA to show a good correlation with CO<sup>2</sup> concentration. However, when this model is used for samples except those are in the calibration and validation sets, predictions will be unreliable.

When there is little or no knowledge on how the *x* variables relate to the *y* variable, variable selection becomes further critical. In principle, all combinations of *x* variables must be tried to find the optimal choice which is not possible and practical. At such instances, variable selection methods such as iPLS (interval partial least squares regression), genetic algorithms (GA), selectivity ratio and jack-knife can be used [26]. Similar to finding the optimal preprocessing method, finding the optimal variable range is specific to the application and is an iterative procedure. In this example, the selection of the variable range for each model was performed which included vibrational modes of important carbon species available in literature [18]. Due to the overlapping of peaks, it was difficult to isolate the exact area for related vibrational modes and therefore 1000–1500 cm−1 and 1000–1164 cm−1 regions were selected for primary and tertiary amines respectively which made the models with the lowest RMSEPs.

## **3.5. Results**

raw spectra is recommended to improve the predictability of a regression model. There are several preprocessing methods available for PAT applications [23, 24]. The optimal choice of preprocessing method is specific to the application and instrument. In this analysis, the baseline correction based on the Whittaker filter [25] and mean centering was applied which provided the lowest prediction errors as presented by root mean square error of prediction.

Eqs. (1)–(10). The variables related to all these carbon species should be included as *x variables* for PLSR modeling. However, with reference to **Figure 3**, isolation of these variables from the rest of the spectra is not straightforward. The spectroscopy shows measurements in the wavelength range from 100 to 3425 cm−1. In the model development, only a selected range of wavelength was included. The spectra for all the solvents (**Figure 3**) show noise which means unwanted variation, both in higher and lower frequency ranges. The middle frequency range shows a flat behavior with some offsets between measurements. The wavelength below 400 cm−1, between 1600 and 2600 cm−1 and after 3100 cm−1 was excluded to remove this noise possibly arising either from the instrument, cables or measurement probe. Since these measurements are associated with a chemical reaction, there are frequencies which are assigned to vibrational mode of molecules. The reaction between alkanolamine and carbon dioxide produces carbonate, bicarbonate and carbamate ions for primary and secondary amines.

is reacted with an amine, it is converted into different carbon ions as shown in

and a tertiary amine does not form carbamate. For a CO<sup>2</sup>

solution, the vibrational modes from carbon species and amine species appear in the Raman spectra collectively which makes it more complex to study. One can understand what kind of chemical species present in a chemical system by observing peaks which vary with different concentrations. On the other hand, knowing what kind of chemical species present in the system helps to identify the peaks which should response differently when concentration changes. This fact is very important when selecting variables for modeling. Having a small

There are disadvantages having unwanted variables in the model. The biggest challenge is overfitting, which makes the model correlates with the property to be predicted (*y* variable) during the model development stage, but future samples predict poorly. For instance, when

*x* variables are the vibrational modes coming from carbon species (i.e., carbonate, bicarbonate and carbamate ions). According to the vapor-liquid equilibrium in the system, the proton-

when this model is used for samples except those are in the calibration and validation sets,

When there is little or no knowledge on how the *x* variables relate to the *y* variable, variable selection becomes further critical. In principle, all combinations of *x* variables must be tried to

/mol MEA [9]. Therefore, by mistake, one can include the variables assigned to

ated MEA and free MEA concentrations correlate almost linearly with CO<sup>2</sup>

protonated MEA and free MEA to show a good correlation with CO<sup>2</sup>

loading in an aqueous MEA solution, important


loading less than

concentration. However,

*3.4.1. Variable selection*

196 Carbon Dioxide Chemistry, Capture and Oil Recovery

Reaction between CO<sup>2</sup>

0.5 mol CO2

predictions will be unreliable.

number of variables makes the model easier to interpret.

developing a PLS model to predict total CO2

When CO2

The calibration and validation results for each of the four respective PLSR models for CO<sup>2</sup> concentration in MEA, MDEA, 3-AP and 3-DMA1P solutions are presented in this section. Data preprocessing and model development were implemented in PLS Toolbox 8.21 in the MATLAB 2016a software (MathWorks Inc.).

The model behavior is presented using four graphs for each amine solution (from **Figures 4**–**11**) which are the score plot (t1–t2), regression coefficients, RMSEP and predicted CO<sup>2</sup> loading versus the measured loading. In the plots, *c* represents calibration samples and *v* represents validation samples. Samples are numbered as 1, 2, 3, 4 onwards with decreasing CO2 concentration. The regression coefficient plot is an indication on what weight of each wavenumber contributes to the prediction. The higher the regression weight, the higher the importance of the assigned wavelength to the prediction. The regression plot can be mapped with the vibrational modes of carbon species which are chemically important to the total CO2 loading.

The score plot gives the overview of the span of the calibration data and validation data. Even without knowing the value of CO<sup>2</sup> loading (*y variable*), by visualizing the sample location in the score plot, one can understand whether the sample is highly or less concentrated with CO2 . Score plot can also be used to identify outliers which mean samples that show nonrepresentative *x variables.* There can be various reasons for outliers such as impurities in samples, different sample type, instrument error and sampling errors. During the model development stage, if such outliers are identified, they should be carefully analyzed and removed from the model. When the model is used for process monitoring, visualization of such outliers helps in the quick identification of the abnormal behavior in the process. With the increasing number of PLS components, the prediction error of the models varies. A plot of RMSEP versus number of PLS components is used to conclude the required number of PLS components for the model. It is always preferable to select a small number of PLS components to avoid model complexities. An optimum number of PLS components avoids the risk of overfitting of the model. The aim of the model is to use the instrument for predicting CO<sup>2</sup> concentration. The predicted versus measured plots show how far the measured values deviate with respect to the predicted values for the validation set. This deviation is interpreted with statistical parameters such as *r*<sup>2</sup> , RMSEP and offset. When *test set validation* is used, this plot gives an image on how the model will align with future data.

**Figure 4.** Results from PLSR model for MEA; (a) score plot of PLS components 1 vs. 2 showing calibration and validation samples; (b) regression coefficients based on a 2-component PLSR model.

their concentration increases, they move toward the negative side of PLS component 1. **Figure 4(b)** is the plot of regression coefficients between the wavenumber 1000–1500 cm−1. Wavenumbers having positive and negative regression coefficients contribute positively and negatively respectively for the predicted property. **Figure 5(a)** shows the variation of RMSEP with increasing number of PLS component. According to this plot, RMSEP becomes the lowest at fourth PLS component. Having a higher number of PLS components in the model increases model complexity and include more noise to the model. There is not much difference in the prediction error between PLS component 2 and PLS component 4. Therefore, two PLS components were selected for the prediction model. **Figure 5(b)** shows how well the model fits

**Figure 5.** Results from PLSR model for MEA; (a) RMSEP with respect to number of PLS components and (b) predicted

concentration using their Raman spectra.

Process Analytical Technology for CO2 Capture http://dx.doi.org/10.5772/intechopen.76176 199

when using for the validation data set to predict CO<sup>2</sup>

versus measured CO2

loading.

**Figure 4(a)** is the score plot of PLS component 1 vs. 2 for MEA model. According to this plot, PLS component 1 describes more than 90% of variation of data while PLS component 2 describes around 3% of variation. The sample with the highest CO<sup>2</sup> concentration (*c*1) and that with the lowest CO2 concentration (*c19, v18*) appear isolated in the score plot implying they have extreme concentration values. The samples are spread in the plot as a pattern where they move from right to left in the direction of PLS component 1 as the concentration decreases. When this model is used for new data, they will appear in the same data swarm area. Samples with high CO2 concentration will appear in the positive side of PLS component 1 and when

**Figure 5.** Results from PLSR model for MEA; (a) RMSEP with respect to number of PLS components and (b) predicted versus measured CO2 loading.

their concentration increases, they move toward the negative side of PLS component 1. **Figure 4(b)** is the plot of regression coefficients between the wavenumber 1000–1500 cm−1. Wavenumbers having positive and negative regression coefficients contribute positively and negatively respectively for the predicted property. **Figure 5(a)** shows the variation of RMSEP with increasing number of PLS component. According to this plot, RMSEP becomes the lowest at fourth PLS component. Having a higher number of PLS components in the model increases model complexity and include more noise to the model. There is not much difference in the prediction error between PLS component 2 and PLS component 4. Therefore, two PLS components were selected for the prediction model. **Figure 5(b)** shows how well the model fits when using for the validation data set to predict CO<sup>2</sup> concentration using their Raman spectra.

**Figure 4(a)** is the score plot of PLS component 1 vs. 2 for MEA model. According to this plot, PLS component 1 describes more than 90% of variation of data while PLS component 2

**Figure 4.** Results from PLSR model for MEA; (a) score plot of PLS components 1 vs. 2 showing calibration and validation

with the lowest CO2 concentration (*c19, v18*) appear isolated in the score plot implying they have extreme concentration values. The samples are spread in the plot as a pattern where they move from right to left in the direction of PLS component 1 as the concentration decreases. When this model is used for new data, they will appear in the same data swarm area. Samples

concentration will appear in the positive side of PLS component 1 and when

concentration (*c*1) and that

describes around 3% of variation. The sample with the highest CO<sup>2</sup>

samples; (b) regression coefficients based on a 2-component PLSR model.

198 Carbon Dioxide Chemistry, Capture and Oil Recovery

with high CO2

**Figure 6.** Results from PLSR model for MDEA; (a) score plot of PLS components 1 vs. 2 showing calibration and validation samples and (b) regression coefficients based on a 3-component PLSR model.

Similarly, in **Figure 6(a)**, score plot for the MDEA model shows a data swarm with a pattern moving from positive to negative side of PLS component 1 when the concentration decreases in MDEA samples. Plot of regression coefficients between wavenumber 1000 and 1164 cm−1 as shown in **Figure 6(b)** indicates that there are both positively and negatively correlated

frequencies for the model in this range. Since the model shows the lowest RMSEP at PLS component 3 as given in **Figure 7(a)**, three components were selected for the model and model

**Figure 7.** Results from PLSR model for MDEA; (a) RMSEP with respect to number of PLS components and (b) predicted

of 0.995.

Process Analytical Technology for CO2 Capture http://dx.doi.org/10.5772/intechopen.76176 201

predictions for validation data set are shown in **Figure 7(b)** resulting an *r*<sup>2</sup>

versus measured CO2

loading.

**Figure 7.** Results from PLSR model for MDEA; (a) RMSEP with respect to number of PLS components and (b) predicted versus measured CO2 loading.

Similarly, in **Figure 6(a)**, score plot for the MDEA model shows a data swarm with a pattern moving from positive to negative side of PLS component 1 when the concentration decreases in MDEA samples. Plot of regression coefficients between wavenumber 1000 and 1164 cm−1 as shown in **Figure 6(b)** indicates that there are both positively and negatively correlated

**Figure 6.** Results from PLSR model for MDEA; (a) score plot of PLS components 1 vs. 2 showing calibration and

validation samples and (b) regression coefficients based on a 3-component PLSR model.

200 Carbon Dioxide Chemistry, Capture and Oil Recovery

frequencies for the model in this range. Since the model shows the lowest RMSEP at PLS component 3 as given in **Figure 7(a)**, three components were selected for the model and model predictions for validation data set are shown in **Figure 7(b)** resulting an *r*<sup>2</sup> of 0.995.

**Figure 8.** Results from PLSR model for 3-AP; (a) score plot of PLS components 1 vs. 2 showing calibration and validation samples and (b) regression coefficients based on a 2-component PLSR model.

**Figure 8(a)** shows the movement of samples from positive to negative side of PLS component 1 as the CO2 concentration decreases in CO2 loaded 3-AP solvent. The plot of regression coefficients between 1000 and 1500 cm−1 as given in **Figure 8(b)** shows negatively and positively correlated wavenumbers to the model predictions. According to RMSEP variation with respect to number of PLS components, two PLS components (**Figure 9(a)**) were selected for the model. **Figure 9(b)** shows how well model predicts for the test set samples. The model results

**Figure 9.** Results from PLSR model for 3-AP; (a) RMSEP with respect to number of PLS components and (b) predicted

Process Analytical Technology for CO2 Capture http://dx.doi.org/10.5772/intechopen.76176 203

versus measured CO2

loading.

**Figure 9.** Results from PLSR model for 3-AP; (a) RMSEP with respect to number of PLS components and (b) predicted versus measured CO2 loading.

correlated wavenumbers to the model predictions. According to RMSEP variation with respect to number of PLS components, two PLS components (**Figure 9(a)**) were selected for the model. **Figure 9(b)** shows how well model predicts for the test set samples. The model results

**Figure 8(a)** shows the movement of samples from positive to negative side of PLS component

**Figure 8.** Results from PLSR model for 3-AP; (a) score plot of PLS components 1 vs. 2 showing calibration and validation

ficients between 1000 and 1500 cm−1 as given in **Figure 8(b)** shows negatively and positively

loaded 3-AP solvent. The plot of regression coef-

concentration decreases in CO2

samples and (b) regression coefficients based on a 2-component PLSR model.

202 Carbon Dioxide Chemistry, Capture and Oil Recovery

1 as the CO2

**Figure 10.** Results from PLSR model for 3DMA1P; (a) score plot of PLS components 1 vs. 2 showing calibration and validation samples and (b) regression coefficients based on a 3-component PLSR model.

components were selected (**Figure 11(a)**) for solvent 3DMA1P, and the model predicts the

**Figure 11.** Results from PLSR model for 3DMA1P; (a) RMSEP with respect to number of PLS components and (b)

A summary of the model details for each amine solution is presented in **Table 2**. For all the

loading range which is given in **Table 2**.

is ≥0.995. Each PLSR model

Process Analytical Technology for CO2 Capture http://dx.doi.org/10.5772/intechopen.76176 205

of 0.995 (**Figure 11(b)**).

predictions models, RMSEP percentages are less than 2.13% and *r*<sup>2</sup>

loading.

validation samples with *r*<sup>2</sup>

predicted versus measured CO2

is valid only for the CO<sup>2</sup>

for the tertiary amine 3DMA1P are shown in **Figures 10** and **11**. Similar to other solvents, the score plot shows a systematic variation of data swarm (**Figure 10(a)**) while **Figure 10(b)** shows the most and least important variables between 1000 and 1164 cm−1 wavenumbers. Three PLS

**Figure 11.** Results from PLSR model for 3DMA1P; (a) RMSEP with respect to number of PLS components and (b) predicted versus measured CO2 loading.

components were selected (**Figure 11(a)**) for solvent 3DMA1P, and the model predicts the validation samples with *r*<sup>2</sup> of 0.995 (**Figure 11(b)**).

A summary of the model details for each amine solution is presented in **Table 2**. For all the predictions models, RMSEP percentages are less than 2.13% and *r*<sup>2</sup> is ≥0.995. Each PLSR model is valid only for the CO<sup>2</sup> loading range which is given in **Table 2**.

for the tertiary amine 3DMA1P are shown in **Figures 10** and **11**. Similar to other solvents, the score plot shows a systematic variation of data swarm (**Figure 10(a)**) while **Figure 10(b)** shows the most and least important variables between 1000 and 1164 cm−1 wavenumbers. Three PLS

**Figure 10.** Results from PLSR model for 3DMA1P; (a) score plot of PLS components 1 vs. 2 showing calibration and

validation samples and (b) regression coefficients based on a 3-component PLSR model.

204 Carbon Dioxide Chemistry, Capture and Oil Recovery


**Table 2.** Summary of PLSR models.

When these models are used for predictions of CO<sup>2</sup> loading in future samples, first their Raman spectra are preprocessed using Whittaker filter and mean centering. The required variable range is selected for each model and using the regression coefficient equation as shown in Eq. (11), the CO<sup>2</sup> loading is predicted.

$$Y = b\_0 + b\_1 X\_1 + b\_2 X\_2 + b\_3 X\_3 + \dots + b\_n X\_n \tag{11}$$

a model for secondary amines would be similar to the reported case of primary amine since

The financial support provided by the PhD scholarship program in Process, Energy and Automation Engineering of University College of Southeast Norway is greatly acknowledged.

Applied Chemometrics Research Group (ACRG), University College of Southeast Norway,

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tion from power plant flue gas streams. International Journal of Greenhouse Gas Control.

[4] Supap T, Idem R, Tontiwachwuthikul P, Saiwan C. Kinetics of sulfur dioxide- and oxygen-induced degradation of aqueous monoethanolamine solution during CO<sup>2</sup>

[5] Brigman N, Shah MI, Falk-Pedersen O, Cents T, Smith V, De Cazenove T, et al. Results of amine plant operations from 30 wt% and 40 wt% aqueous MEA testing at the CO<sup>2</sup> technology Centre Mongstad. Energy Procedia. 2014;**63**:6012-6022. DOI: https://doi.org/

Control. 2015;**41**:127-141. DOI: https://doi.org/10.1016/j.ijggc.2015.07.003

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absorption process. International Journal of Greenhouse Gas

removal with

absorp-

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Process Analytical Technology for CO2 Capture http://dx.doi.org/10.5772/intechopen.76176

both primary and secondary amines produce carbamate when reacted with CO2

**Acknowledgements**

**Conflict of interest**

**Author details**

Porsgrunn, Norway

on Climate Change; 2013

the post-combustion CO2

10.1016/j.egypro.2014.11.635

**References**

The authors in this paper declare no conflicts of interest.

M.H. Wathsala N. Jinadasa, Klaus-J. Jens and Maths Halstensen\*

\*Address all correspondence to: maths.halstensen@usn.no

In Eq. (11), *Y* is the predicted CO2 concentration; *b* 0 is regression coefficient for the intercept, *Xn* is the preprocessed *n*th variable (Raman wavenumber) and *b n* is the regression coefficient relevant to variable *Xn* .

In PAT, chemometric modeling does not end once a model is calibrated and validated to achieve a targeted prediction accuracy and precision. The model is needed to undergo continuous improvement or remodeling. Some suggestions are assessing the current model performance using new validation data, using additional calibration data to remodel the existing model, improving data preprocessing methods, improving sampling methods, moving to more accurate reference analysis, different *x* variable ranges and including calibration samples with more variations.
