Description of samples in the calibration and validation set.
Carbon capture and storage, which is also known as CCS, is an obligatory climate change mitigation technology to reduce the carbon dioxide gas emissions to the atmosphere thus limiting the average global temperature increase to 2°C. Process analytical technology is a scientific tool to improve process qualities and performance through timely measurements. This chapter describes how process analytical technology can be imbedded to a carbon capture technology by giving a detailed example of implementation of a process analyzer to CO2 capture by alkanolamine absorption process. Such an implementation requires success in five elements, which are described in this chapter. They are as follows: selecting an appropriate process analyzer, integration between the analyzer and the process, model development to enable the analyzer to predict a process-related chemical or physical attribute, use of the developed model in real-time application and use of the data obtained from the analyzer as an input to a process control unit. Partial least square regression model is a useful chemometric-based method to extract hidden chemical information in measurements from a process analyzer. In this chapter, four partial least square regression models are presented, which are developed to predict CO2 concentration for four different alkanolamine solutions when these amines are used to absorb CO2 from a combustion process.
- climate change
- carbon dioxide capture
- process analytical technology
- process analyzers
- partial least square regression
- Raman spectroscopy
1.1. Carbon dioxide and climate change
The Earth’s atmosphere contains greenhouse gases such as water vapor, methane, ozone, carbon dioxide and nitrous oxide in trace levels, which have the ability to trap heat. By trapping heat, temperature of the atmosphere increases which is referred to
CO2 capture and storage (CCS) is a three-step process consisting of CO2 capture from emission sources; CO2 transportation to storage sites and store in underground geological formations. In combustion processes, CO2 can be captured either in pre-combustion or in post-combustion mode.
1.2. Carbon dioxide capture technologies
CO2 can be separated from flue gas or fuel gas stream by processes such as absorption, adsorption, membrane separation, chemical looping, hydrate based separation, biochemical methods and cryogenic distillation. Among these separation technologies, the absorption process is the most mature because the basic technology is well known. Typical sorbents are alkanolamines and potassium bicarbonate. The most preferred option for retrofitting existing plants is post combustion capture technology (PCC), where CO2 is scrubbed from the flue gas after combustion has taken place.
1.2.1. CO2 capture by aqueous alkanolamine solvents
The basic reaction mechanism between CO2 and pure amines/alkanolamines is the same; hence, both compound classes can be used for CO2 capture. Although theoretically pure amine can enhance the performance of CO2 capture than aqueous amines, pure amines and even high-concentrated aqueous amine solutions show implications in plant operations. Several experiments, pilot plant tests and theoretical simulations have been carried out to investigate the optimum amine concentration for CO2 capture systems [2, 3]. Amine volatility for pure amine is, in general, higher than aqueous alkanolamine volatility. When the amine concentration is increased, amine degradation rate becomes higher  and the cost required for amine oxidation inhibitors is increased. CO2 capture plants using higher amine concentrations require higher reboiler duty  and larger water washing sections. Therefore, aqueous alkanolamine solutions are preferred than pure amines. Furthermore, aqueous alkanolamines and in particular aqueous MEA are the commercial workhorse solvents for the CO2 capture related gas sweetening industry .
The IEA CCS road map requires to achieve 100 or more projects by 2020 to reduce the CO2 capture energy penalty by 7% points by proving CO2 capture technologies ready for large scale and demonstrating retrofit for 85% CO2 capture . The most common and cost-effective technology is absorbing CO2 chemically into aqueous alkanolamine solutions such as monoethanolamine, monoethanolamine-glycol mixtures, diethanolamine, diglycolamine, diisopropanolamine, methyldiethanolamine, and mixed amines (including sterically hindered amines) [8, 9, 10, 11]. Establishing CO2 capture technology by MEA still have challenges, which have to be solved within a short period of time, such as equipment corrosion, solvent degradation, high solvent regeneration energy requirement and possible solvent emissions to the environment.
1.2.2. Chemical reactions of the CO2-alkanolamine-H2O system
A series of parallel equilibrium and kinetic reactions occur during CO2 absorption into aqueous amine solution. This results in an electrolyte solution including ions such as and as shown in reactions from Eqs. (1)–(10). Here R represents an alkyl group such as CH3 or C2H5. Reaction of CO2 with this solution is a Brønsted acid-base neutralization reaction leading to a drop in solution pH as CO2 is absorbed. The chemical composition of this solution varies according to overall solution pH.
Carbon dioxide gas physically dissolves in water to give a solvated form of carbon dioxide which is, CO2(aq) (Eq. (2)). Thereafter, CO2(aq) reacts chemically with water to give carbonic acid (Eq. (4)) which forms equilibria with bicarbonate (Eq. (5)) and carbonate (Eq. (6)) ions according to the respective pKa values. Amines are categorized as primary (RNH2), secondary (R2NH), tertiary (R3N) and sterically hindered amines (e.g., t-RNH2) based on their chemical structure. Amine reactivity of the CO2-alkanolamine-water system is determined by the N atom lone electron pair in a Zwitterion reaction mechanism . Accepting a proton at the N lone electron pair, amines react as Brønsted bases according to a Brønsted acid–base reaction type. The N lone electron pair can also react with, for example, the carbon atom of the CO2 molecule according to a Lewis acid-base reaction type. Carbonic acid reacts in aqueous solution with all amines in a 1:1 manner by a Brønsted acid-base reaction type to form protonated amine bicarbonate. Primary and secondary alkanolamines may also react rapidly with CO2 through a Lewis acid-base reaction type to form amine carbamate. During carbamate formation, two amine molecules react with one CO2 molecule; therefore, aqueous amine solutions forming protonated amine bicarbonate only achieve a higher CO2 loading than aqueous primary or secondary amine solutions forming amine carbamates only.
These equilibrium equations (Eqs. (1)–(10)) can be manipulated for amine and absorbed CO2 considering the carbon and amine balance. The total number of moles of the CO2 is the sum of moles of carbonate, bicarbonate, carbamate ions and molecular CO2(aq). The total number of amine moles is the sum of moles of protonated amine, free amine and carbamate ions. The mole ratio of CO2:amine is known as CO2 loading. This value represents the CO2 absorption capacity by the amine and is considered as a crucial technology-related value. The reaction pathway for primary, secondary and tertiary amines is described in . Primary and secondary amines react with CO2 to form carbamates which limits their theoretical CO2 absorption capacity to 0.5 mol CO2/mol amine. Tertiary amines do not possess free proton in the nitrogen atom and hence do not directly react with CO2. Their reaction pathway first involves increasing the hydroxyl ion concentration which contributes to react with CO2 to produce bicarbonate. Therefore, they have a higher theoretical CO2 absorption capacity which can go upto 1 mol CO2/mol amine.
2. Process analytical technology and CO2 capture
With reference to ,
2.1. Implementation of a PAT tool for a CO2 capture process
A successful and robust implementation of PAT is required to gain its widespread benefit to a process. It also saves money, time and resources. Since PAT applies to an entire product cycle, the implementation process should be modeled with simultaneous approach to submodels such as business, production process, management, engineering, health-safety & environment and customer perspective. Although process analytical technologies improve the speed of the analysis, the stage of introducing such a technology requires knowledge, time and tremendous scientific work. Five elements are identified regarded to this stage and described in this section. Figure 1 shows these five elements of a PAT implementation approach.
Element 1: Selecting a suitable process instrument for an application is challenging due to the variability of the availability. To determine a particular attribute of the process such as an analyte concentration, several simple to sophisticated measurement technologies are available. The vendors claim the general features of the instruments they sell but how deeply, accurately and precisely they response to our own application and the limitation of usage are unknown until we purchase the instrument and experience its measurements. Since PAT tools are expensive compared to traditional chemical analysis systems, the user may wish to use it for different applications. For instance, an R&D institution wants to use an analyzer to measure transparent liquid samples but later they also wish to analyze high turbid samples. An example is analysis of alkanolamine samples from an industrial CO2 capture plant where samples are normally transparent when the fresh amines are used but will gradually become opaque due to thermal and oxidative degradation when the plant is operated continuously. If the user purchases an analyzer which only responses to transparent samples and is disturbed due to the fluorescence effect of turbidity in a sample, then this analyzer will stop its function when the solvent degradation starts. Therefore, if the investment requirement is to use this instrument throughout several days of plant operation, then the instrument selection becomes invalid and the whole implementation becomes unsuccessful. To avoid such disappointments in later stages of a PAT implementation, one should always thoroughly assess the selection criteria of the PAT tool. To ease this operation, the process can be started with categorizing available analyzers based on their selectivity, performance, limit of detection, complexity of usage, how it fits to the indoor and outdoor industrial environment as well as the budget. Analyzers can measure physical properties such as refractive index, thermal conductivity and viscosity. There are electrochemical analyzers measuring conductivity, pH and spectroscopic analyzers measuring concentration or any other attribute which is related to electromagnetic interaction. A detailed description about different types of process analyzers can be found in .
When selecting a suitable process analyzer, it becomes crucial whether the instrument can be easily integrated to the process system. Some instruments require advance mechanical modifications to the existing system or process stream in the installation process, which make them abatement only to that function. For example, if the requirement is plugging a sensor to a high-flow process stream, the engineer should assure that the plugging mechanism can withstand the maximum pressure of the system. Meanwhile, the process flow should not damage to the sensor. This kind of plugging modifications is normally fixed or permanent, and if the sensor is required to remove from the system, the process flow should be stopped and demand time. These practical problems restrict the analyzer only to this application. If we consider an insertion probe in a liquid stream, there are some critical factors affecting to the quality of the signal. They are angle of insertion, penetration depth, and turbulence of the process flow (as at some flow rates there can be bubble formation). If a probe, which has a higher penetration depth than the sample height of the measurement in an experimental set up, is selected to use, the set up should be modified to increase the sample height than the required penetration depth. If the probe is going to be inserted to a small diameter pipe, where the penetration depth is higher than the diameter, and the probe cannot be inserted to the pipeline directly. In such a case, a flow cell with higher diameter should be included to the existing line. The volume of the new flow cell should not be larger to create stagnant places, dead zones, and inhomogeneous mixture. This can be challenging because the plant needs to be shut down before the modification. The required mechanical interface may not be easy or straightforward at some instances. Therefore, the engineer experiences the dilemma whether he should select an analyzer, which fits to the system, or he should change the system to fit to the analyzer. Normally, process analytical instruments are superior in giving fast responses. If the interest is online monitoring of concentration changes or reaction kinetics, the analyzer should give fast responses such as measurements in every minute. Frequency of calibration/maintenance of the analyzer, cleaning requirement between successive scans, required training or expertise to operate the instrument, whether the instrument can be operated continuously throughout an entire process or an experiment cycle are also limiting factors.
Element 2: Process integration implies the method of measurement taken from the application. After selecting the required process analyzer, the sample measurement is carried out inline, at line or online. Examples for inline are: an immersion probe which directly contact with the sample inside the process equipment, process line or flow cell. In such an instance, sample dilution or probe cleaning prior to each measurement is impossible. On the other hand, the user has the ability to do so if there is an autonomous sampling system, which is called on-line. There are some process analyzers, which cannot be physically integrated to the process such as liquid chromatography. Samples that are withdrawn from the process (grab samples) either manually or automatically are measured offline and called at-line measurements. Grab samples associate with the highest percentage of sampling errors.
Element 3: Process analytical instruments give both qualitative and quantitative analysis. Qualitative analysis gives the first impression to understand whether it is meaningful and worth to develop a method for quantitative analysis by a particular instrument. Process analytical instruments are calibrated and validated before they are used to demonstrate that it is suitable for intended purpose. Normally, calibration means to ensure that the instrument readings are accurate with reference to established standards. Calibration can be used before using an instrument such as a weighing balance or a pH meter. Vendor specifies when and how frequently the equipment is needed to calibrate. Normally, the validation refers that the equipment installed correctly and performing without an error comparably to the one used before. These vendors’ specified calibration and validation are straightforward.
However, in the PAT terminology,
Element 4: Data processing and chemometrics are a subcategory of model development stage. When it comes to the implementation stage of a process analyzer to the plant, the researcher has experience from laboratory experiments or batchwise experiments on what kind of data treatment is needed. Usually raw data coming out from a process analyzer such as a spectrometer contain noise. The information in the data is hidden within this noise. Preprocessing makes data easier to read, understand and interpret. There are also some instances where univariate methods are implemented and/or data processing is not essential. After preprocessing, the data can be used as input to a chemometric model such as a principle component analysis (PCA) model or partial least square regression (PLSR) model. Different chemometric tools have different advantages. For example, PCA can be used to identify trends of variation in the data with respect to time or process conditions, and PLSR can be used to predict process features based on indirect measurements. PLSR is widely used in process applications for quantitative purposes. One such example can be found in  where it shows the use of PLSR to quantify all chemical ions present in a CO2 capture laboratory rig operated by aqueous amine. Another example of PLSR model development and ion speciation is shown in  for CO2 capture by ammonia process. A detailed description for PLSR theory, calibration and validation is described in the literature [19, 20].
Element 5: Data acquisition and instrument control of a process analyzer is an essential part to integrate analyzer measurements with an automative control system. In the case of univariate analysis, implementation of automation control system is easy. However, when it comes to multivariate methods, communication between the process analytical instrument and the control system is challenging. There is a gap between these two types. The reasons are that the analyzer has different file formats and sometimes the control system has been implemented in a different file format in a different flat form. For example, in the analyzer, the data may be saved in csv file format and the control system implemented in MATLAB/LabVIEW interface needs data only from a wavelength range and needs data in txt or mat format as inputs.
3. An experiment on developing a full calibration model using Raman spectroscopy
The scope in this section is to present the methodology on how a process analytical instrument is implemented to replace traditional chemical analysis. The example described in this section is specific to CO2 absorption process by amines, but a similar approach can be applied for any chemical analysis in laboratory or process plant. Analysis of CO2 capture solvents is necessary almost in every R&D tasks and CO2 process plants to optimize the absorption and desorption process. Some examples are investigating the effects of different types of solvents, blends, catalyzers, process parameters and equipment configuration on CO2 absorption and desorption capacity.
In this example, it is shown how a Raman spectroscopy-based full spectrum calibration is performed using a laboratory experiment for four types of amines which are reacted with CO2. Sample types are described in Table 1. Two primary and two tertiary amines were used for preparing four regression models. The primary amines are 2-aminoethanol (MEA) and 3-amino-1-propanol (3-AP). Tertiary amines have a different chemical mechanism than primary and secondary amines when absorbing CO2 and they can reach CO2 loading capacity up to 1 mol CO2/mol amine. 3-dimethylamino-1-propanol (3DMA1P) and methyl diethanolamine (MDEA) are the two tertiary amines used in this experiment.
3.1. Sample preparation
For each model, a calibration and validation set was prepared to facilitate
3.2. Analysis of reference samples
Determination of true CO2 concentration (CO2 loading) in alkanolamine samples was carried out by the
3.3. Raman spectroscopy
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 time of 60 s to get a good signal-to-noise ratio.
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 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 CO2 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  and mean centering was applied which provided the lowest prediction errors as presented by root mean square error of prediction.
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
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 (
When there is little or no knowledge on how the
The calibration and validation results for each of the four respective PLSR models for CO2 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 CO2 loading versus the measured loading. In the plots,
The score plot gives the overview of the span of the calibration data and validation data. Even without knowing the value of 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 CO2 concentration (
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 predictions for validation data set are shown in Figure 7(b) resulting an
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 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 components were selected (Figure 11(a)) for solvent 3DMA1P, and the model predicts the validation samples with
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
|Model||CO2 loading range (mol CO2/mol amine)||Variable range (cm−1)||PLS components||RMSEP (RMSEP %)|
When these models are used for predictions of CO2 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 CO2 loading is predicted.
In Eq. (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
Implementation of PAT tools in CO2 capture process is useful in many ways to accelerate laboratory analysis, R&D tasks and full-scale plant operations. One such example is using a Raman spectroscopy to determine CO2 concentration in alkanolamine solutions real-time. 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 predict CO2 concentration in two primary amines and two tertiary amines solutions during CO2 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 between CO2 aqueous amine solutions. Primary amine and tertiary amine type was selected 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 a model for secondary amines would be similar to the reported case of primary amine since both primary and secondary amines produce carbamate when reacted with CO2.
The financial support provided by the PhD scholarship program in Process, Energy and Automation Engineering of University College of Southeast Norway is greatly acknowledged.
Conflict of interest
The authors in this paper declare no conflicts of interest.