**Author details**

When these models are used for predictions of CO<sup>2</sup>

 **loading range (mol CO2**

**mol amine)**

206 Carbon Dioxide Chemistry, Capture and Oil Recovery

shown in Eq. (11), the CO<sup>2</sup>

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

**Model CO2**

relevant to variable *Xn*

ples with more variations.

Implementation of PAT tools in CO<sup>2</sup>

a Raman spectroscopy to determine CO<sup>2</sup>

**4. Conclusions**

predict CO2

between CO2

CO2

*Xn*

In Eq. (11), *Y* is the predicted CO2

.

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

*Y* = *b*<sup>0</sup> + *b*<sup>1</sup> *X*<sup>1</sup> + *b*<sup>2</sup> *X*<sup>2</sup> + *b*<sup>3</sup> *X*<sup>3</sup> + …+ *b<sup>n</sup> Xn* (11)

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 sam-

laboratory analysis, R&D tasks and full-scale plant operations. One such example is using

During the process of implementation of a process analyzer for such an application, a chemometrics-based calibration model should be prepared. Four PLSR models were developed to

 absorption process. The models predictions are satisfactory where RMSEP are 2.11, 1.86, 2.13 and 1.88% for MEA, 3-AP, 3DMA1P and MDEA, respectively. The target of the present experimental work was to show the PLSR calibration model development for reaction

for the study because they have two different types of reaction mechanisms. Hence, work on secondary alkanolamines, and so on was out of scope. However, we expect that creating

concentration in two primary amines and two tertiary amines solutions during

aqueous amine solutions. Primary amine and tertiary amine type was selected

0

concentration; *b*

loading is predicted.

**/**

**Variable range (cm−1)**

MEA 0–0.4543 1000–1500 2 0.0096 (2.11) 0.995 3-AP 0–0.5149 1000–1500 2 0.0096 (1.86) 0.996 3DMA1P 0–0.7945 1000–1164 3 0.017 (2.13) 0.995 MDEA 0–0.7449 1000–1164 3 0.014 (1.88) 0.997

is the preprocessed *n*th variable (Raman wavenumber) and *b*

loading in future samples, first their

**(RMSEP %)**

*r***2**

is regression coefficient for the intercept,

is the regression coefficient

*n*

**PLS components RMSEP** 

capture process is useful in many ways to accelerate

concentration in alkanolamine solutions real-time.

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

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

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