Quantitative Relationship between CO2 Absorption Capacity and

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Applied Chemistry

A quantitative relationship between CO2 absorption capacity and amine water system: DFT, statistical and experimental study Jingxing Cheng, Kun Zhu, Houfang Lu, Hairong Yue, Changjun Liu, Bin Liang, and Siyang Tang Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.9b01297 • Publication Date (Web): 09 Jul 2019 Downloaded from pubs.acs.org on July 16, 2019

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Industrial & Engineering Chemistry Research

A quantitative relationship between CO2 absorption capacity and amine water system: DFT, statistical and experimental study Jingxing Cheng1, Kun Zhu1,2, Houfang Lu1,2, Hairong Yue1,2, Changjun Liu1, Bin Liang1,2, Siyang Tang*,1 1. Low-Carbon Technology and Chemical Reaction Engineering Laboratory, School of Chemical Engineering, Sichuan University, Chengdu 610065, China 2. Institute of New Energy and Low-Carbon Technology, Sichuan University, Chengdu 610207, China Corresponding author, Siyang Tang, E-mail: [email protected]

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Abstract: CO2 emissions reduction has become a hot issue to relieve global warming recent years. CO2 absorption with amines aqueous solutions is a promising method for its capture, and increasing CO2 capacity and reducing energy consumption are essential for its application. In this work, a dataset of 29 different amines was regressed with genetic function approximation (GFA) and artificial neural network （ ANN ） algorithm to get a predictive quantitative relationship between amine structure and CO2 absorption capacity. Density functional theory (DFT) method at level of B3LYP and 6311+g (d,p) were used to optimize the structures. Both reactants and products were introduced in regression, and optimal model with best relevance and predictability for CO2 absorption at 313 K was obtained and experimental confirmed. Descriptors analysis showed that reducing number of hydroxyl group, increasing molecular mass and changing steric effect of amine structure would increase its CO2 absorption capacity. Two new amine structure predicted with high absorption capacity were proposed.

Key words: QSAR, CO2 absorption, amine, GFA algorithm

1. Introduction Nowadays, the emissions control of greenhouse gas CO2 has become one of the most challenging environmental issues all over the world.1 And the chemical absorption process using amines aqueous solutions is widely considered for CO2 collection due to its mature technology and low energy consumption2. Various amine-based absorbents was investigated in past decades, including primary amine, secondary amine, tertiary amines and mixed-type amine.3-5 Different type amine systems have been


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commercialized for CO2 capture, such as monoethanolamine(MEA) for primary amines, diethanolamine(DEA) for secondary amines, and N-methyldiethanolamine(MDEA) for tertiary amines.2, However those are still of a high energy consumption, and primary amine such as MEA is considered with a amine loss.6 Recent researches focus on reducing the economic and energy consumption of CO2 capture process, and it is highly relied on behaviors of the amine aqueous solution7,8. The amine/water ratio, desorption temperature, absorption capacity and absorption rate are key factors to evaluate the energy cost. However, with a certain ratio of amine and water, the absorption rate seems barely affect the energy consumption9, while the absorption capacity and desorption temperature seem highly affect the energy cost3. And increase of absorption capacity is believed to be valid to reduce the energy consumption(GJ/t CO2).10 The unpaired electron of N is the key for reacting with H2O and CO2,11 and the structure of amine-based absorbents directly influences amine’s properties and its behaviors, such as CO2 absorption rate, CO2 solubility and desorption energy.12, 13 Chakraborty et al.14, 15 studied the relationships between the structure of amine and its CO2 absorption capacity. It showed that the substitution at α-carbon would bring carbamate instability, a higher absorption rate and an increase amount of bicarbonate. The interaction of π and π* methyl group orbital with the lone pair of the nitrogen reduced N charges, and it weakened the N-H bond and enhance absorption rate. Rowland et al.16 did a series work on CO2 absorption capacity of amines aqueous solutions with isothermal gravimetric analysis (IGA) method. 76 different amines aqueous solutions including



cyclic and aromatic amines, alkanolamines, polyamines were investigated. 7 amines(1 primary amine, 3 secondary amines, and 3 tertiary amines) with similar backbone of hydroxyl function group substituting at 2nd or 3rd carbons position showed best CO2 absorption capacity. Singh et al.17 found the CO2 absorption capacity of increase with the number of N group in amine molecule up to 3.03 moles CO2/mole amine (teraethylenepentamine). A quantitative research conducted by Rezaei et al18 also showed that the number of the hydroxyl group and N atom significantly impacted on the CO2 absorption capacity. Although the structure of amine does affect the absorption behaviors, the quantitative relationship between structure and its behavior still remains unclear. Quantitative structure property/activity relationship(QSPR/QSAR) trial was first conducted by Momeni et al.19, 20. QSPR/QSAR methods were developed on the basis of assumption that the physic-chemical properties or activities of a compound can be expressed as a function of chemical structure, and it is widely used to build a model predicting molecular properties21, algorithm

22.

Momeni et al. built a multiple linear regression-genetic

models with 23 amine-based CO2 absorbents and identified the main

parameters influencing the capacity of amines for CO2 absorption. 19 The dataset used in the model are limited to primary (nRNH2) and secondary (nRNHR) amines. 10 out of 23 amines were polyamines. With no further experimental verification, the model’s application, especially for tertiary amines, seems limited. QSPR/QSAR methods introduce a statistic criterion to sift the key factors for the property, and it is effective to provide a predict model for further selecting amine at the


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molecular level. In this work, most reported amine/water system (mostly monoamine with hydroxyl group) were collected as a data set (29 amines). 2D QSAR methods were considered to regress the absorption capacity. In the data set, CO2 capacity form and the amine structure set used for regression were fully expended and discussed. 1 model shows best prediction performance among all the genetic function approximation (GFA) algorithm and artificial neural network （ ANN ） algorithm models. 4 amines were selected for experimental confirming model predictability, and 2 new amine predicted with high absorption capacity was proposed.

2. Computational and experimental details 2.1 QSAR calculation The main steps of a typical QSPR/QSAR study are as follows22: collect a data set, calculate molecular structures, extract mathematical descriptors, data regression, validate the predictability and stability, and explain the descriptors selected in the model. Sometimes, new kind molecular structures could be proposed. 2.1.1 Data set As a mathematical and statistical method, QSAR/QSPR are regression or classification models obtained on the basis of some assumptions, and it is quite strict with the data set. CO2 absorption is very sensitive to the experimental conditions, the samples selected for the QSAR/QSPR study should be performed under the same experimental conditions. The data set of 29 amines (Fig. 1) and the CO2 absorption capacities of their aqueous system were extracted from the literatures, all the capacities were performed



at 313 K with 2 M amine aqueous solution (Table 1).2, 7, 23-25 This data set consists of most reported monoamine and 1 diamine (piperazine, PZ), including alkanolamines and sterically hindered amines. This data set consists of primary amines, secondary amines, and tertiary amines.

Fig 1. The QSAR data set of 29 amine structures

For some regression algorithm, the dataset activity distribution should be or adjusted to be normally distributed to obtain model with best predictability26. And a numerical transformation is useful to achieve a normal distribution. Absorption capacities in four


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numerical forms including two unit forms (y1 in mol CO2/mol amine and y2 in g CO2/g amine) and two log transforms (Y1 = ― log y1 and Y2 = ― log y2) were involved and compared to establish a better model (Table 1). Detailed calculation process was illustrated in supporting information Table S1. Table 1 Experimental CO2 absorption capacities of 29 amine aqueous systems in different form

a.

No.

y1a

Y1b

y2c

Y2d

No.

y1a

Y1b

y2c

Y2d

1

0.550

0.260

0.396

0.402

16

0.670

0.174

0.392

0.503

2

0.730

0.137

0.360

0.443

17

0.730

0.137

0.274

0.503

3

0.490

0.310

0.181

0.742

18

0.790

0.102

0.297

0.413

4

0.300

0.523

0.109

0.963

19

0.830

0.081

0.499

0.401

5

0.570

0.244

0.334

0.476

20

0.440

0.357

0.162

0.717

6

0.600

0.222

0.296

0.528

21

0.670

0.174

0.331

0.444

7

0.494

0.306

0.289

0.539

22

0.880

0.056

0.330

0.454

8

0.580

0.237

0.349

0.457

23

0.120

0.921

0.030

1.438

9

0.670

0.174

0.499

0.302

24

0.020

1.699

0.004

2.353

10

0.490

0.310

0.205

0.688

25

0.440

0.357

0.132

0.942

11

0.700

0.155

0.421

0.376

26

0.680

0.167

0.186

0.933

12

0.570

0.244

0.343

0.465

27

0.850

0.071

0.257

0.690

13

0.950

0.022

0.398

0.679

28

0.190

0.721

0.056

1.206

14

0.690

0.161

0.341

0.468

29

0.850

0.071

0.363

0.455

15

0.910

0.041

0.465

0.333

y1 is CO2 absorption capacity in the unit mol CO2/mol amine. b. Y1 is for transformed y1 using

Y1 = ― log y1. c. y2=y1*M1/M2 is absorption capacity in the unit g CO2/g amine, M1 is the molecular



mass of CO2, M2 is molecular mass of amine. d. Y2 is the transformed y2 using Y2 = ― log y2.

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All

the data were performed at 313 K with 2 M amine aqueous solution.

2.1.2 Geometry optimization All the molecules were first calculated and optimized with density functional theory (DFT) method at the level of B3LYP functional and 6-311+ G(d,p) basis set using Gaussian09 software.27,28 To get a highly relevant reaction relationship between amine and its performance, both molecular structures before and after reacted with CO2 were considered to construct the structure set. According to the mechanism reported, the two kind products could be formed with different reactants.29 Under a high amine/water ratio, two primary amine or secondary amine molecules react with one CO2 molecule to product 1(P1). And at a low amine/water ratio, one primary amine, secondary amine or tertiary amines molecule reacts with one CO2 molecule and one H2O molecule to a product 2(P2).(Fig.2) Both P1 and P2 were optimized separately. Structural optimization is based on the principle of minimum energy. Vibrational frequencies were carried out along with the molecular geometry optimizations. (supporting information Table S2). After conducting a potential energy surface (PES) scan and optimizing the molecular stability structure, Natural Bond Orbital (NBO) charge analysis is also performed.


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Fig. 2 Two mechanism of amine/water system absorbing CO2 with EEA as example. A is representing amine structure before reaction. P1 is representing the product of mechanism 1; P2 is representing the product of mechanism 2. The grey ball presenting for C atom, white ball presenting for H atom, red ball presenting for O atom, blue ball presenting for N atom.

2.1.3 Descriptor generation and selection After molecule structures optimized and calculated, the structure sets consisted of amine and/or products (A, P1P2, P2, A+P2) were used for molecular descriptors calculation to transfer the chemical information into a numerical codes(including constitutional descriptors, topological descriptors, geometrical descriptors, electrostatic descriptors, atomistic descriptors, spatial descriptors, thermodynamic descriptors, fast descriptors) with QSAR module of Material Studio 8.0.30 Some other basic geometry



descriptors are extracted additionally, such as bond length, bond angle and dihedral angle of carbon and nitrogen atoms from optimized structure. And some quantum chemical descriptors, including dipole moment, sum of the electronic and thermal free energies, atomic charges, HOMO energy (highest occupied molecular orbital energy), LUMO energies (lowest unoccupied molecular orbital energy) etc, are also added to the descriptor set. Consequently, a total number of 31 quantum chemical descriptors were calculated for each molecule. Over 300 descriptors for each molecule were calculated. Since too many numerical descriptors data may cause inaccuracy and complexity in further mathematical regression, descriptor selection was required according to the following rule: for all the molecular structures, constant and near constant descriptors are removed; all collinear descriptors (descriptors with a correlation coefficient >0.9531) except one are removed. After interactivity configuration, 34 auto generation descriptors and 31 additional descriptors formed a descriptor matrix (29 structures ×65 descriptors) for regression. 2.1.4 Model developing and evaluating Model establishment was performed in the QSAR module of Material Studio 8.0 software. GFA32 and ANN31 were introduced as statistical modeling algorithm building. GFA apply the genetic algorithms to function approximation, and it gives a large number of relative potential factors to find the subset of terms that correlates best with the response. It tries different-type basic functions in the development of models at one regression, for example, splines, Gaussian or higher-order polynomials32. ANN algorithm does not impose any constraints in the modeling. Linear and non-linear


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models can be built without any pre-assumptions on the deterministic relationship between input and output. ANN is considered as a valuable effective modeling method used for chemical reaction process33. Three-layered feed forward network was used in ANN regressions in this work. In both GFA and ANN regression, data set is randomly divided into training set and test set. The training set is used to develop the model and adjust the structure and parameters of the model, while the test set is used to evaluate the prediction and generalization of the model. Due to that the ratio of number of compounds to the number of descriptors in the QSAR/QSPR study is suggested to be at least 5:134, model involving 4 descriptors was constructed to describe the relationship of structure and capacity of CO2 absorption. The quality of the correlation of QSAR model was evaluated in accordance with the coefficient of determination(R2), adjusted R2 and the F-test (F). The stability of the correlation is tested against the coefficient of leave-oneout cross validation (R2cv)35. And with a R2 over 0.90, R2cv over 0.6 and F over 100, regression model could be considered of a high statistical correlation and a good predictability26. 2.2 Experimental section For experimental verification, 4 commercially available monoamines out of the data set was used to validate the regression models for CO2 absorption preferentially. These amines have similar skeletons to those in data set for regression, and those CO2 absorption behaviors have barely been reported. Experimental conditions remain consistent with data set conditions.



2.2.1 Materials 1-dimethylamino-2-propanol(DMA-2P) with purity of >97wt% was purchased from

aladdin, China. 1-diethylamino-2-propanol(DEA-2P) with purity of 98wt% was purchased from Alfa-Chemistry, USA. Diisopropanolamine(DIPA) with a purity of 98wt% and 2-(isopropylamino)ethanol (IPAE) with purity of >99wt% was purchased from aladdin, China. The amine solutions were prepared to the desired concentration using deionized water. Commercial-grade CO2 and N2 (with a purity of >99%) were supplied by Chengdu Dongfeng Gas Co., Ltd., China. All of the chemicals above were used without purification. 2.2.2 CO2 absorption and desorption The absorption experiment was performed with equipment shown in Fig. 3. 25 mL absorbent(2M amine aqueous solution) was added and heated to 313 K. N2 and CO2 controlled by mass flow meters was mixed then bubbled into a 100 mL round bottom flask absorber. Then unabsorbed gas passes through the condenser, the pickling bottle and the drying tube in turn, and then enters the CO2 infrared gas analyzer. The total volumetric flow rate of the mixed gas is 200 mL/min, and the volume fraction of carbon dioxide is 12%. The two gas CO2 and N2 flow from the two cylinders are controlled by two-channel mass flowmeters (MFC, Beijing Sevenstar Electronics Co. Ltd., China), respectively. When the volume fraction shown in the CO2 analyzer is consistent with pre-formulated CO2, the reaction is terminated. At the end of the experiment, the amount of carbon dioxide absorbed in the aqueous amine solution was determined by titration. And detailed titration process is illustrated in supporting information.


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Fig. 3 Experimental device for absorbing carbon dioxide by amine/water system

3. Results and discussion 3.1 Regression method evaluation Considering the reaction mechanism, different structure sets consist of reactants or/and products(including both 2 mechanism products) were used for descriptor generation for model comparison and selection. CO2 capacities in different unit (mole capacities and weight capacity) and transform capacities32 were also involved as regression goal to obtain a better regression model. Best QSAR models regressed with different structures set, capacities forms and regression algorithms were listed in Table 2.



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Table 2 QSAR models regressed with different structure sets and CO2 absorption capacity forms in GFA and ANN method Structures

Capacities

seta

formb

1

A

2

Algorithm

R2

R2cv

F

y1

GFA

0.826

0.756

28.530

A

y1

ANN

0.710

0.471

3

A

Y1

GFA

0.948

0.921

4

A

Y1

ANN

0.770

0.115

5

A

y2

GFA

0.896

0.843

6

A

y2

ANN

0.949

0.680

7

A

Y2

GFA

0.987

0.975

8

A

Y2

ANN

0.949

0.680

9

P1P2

y1

GFA

0.794

0.720

10

P1P2

y1

ANN

0.892

0.110

11

P1P2

Y1

GFA

0.777

0.559

12

P1P2

Y1

ANN

0.651

0.376

13

P1P2

y2

GFA

0.937

0.899

14

P1P2

y2

ANN

0.894

0.190

15

P1P2

Y2

GFA

0.885

0.775

16

P1P2

Y2

ANN

0.996

0.413

17

P2

y1

GFA

0.812

0.730

18

P2

y1

ANN

0.885

0.293

19

P2

Y1

GFA

0.818

0.432

20

P2

Y1

ANN

0.984

0.498

21

P2

y2

GFA

0.930

0.891

22

P2

y2

ANN

0.945

0.710

23

P2

Y2

GFA

0.895

0.644

24

P2

Y2

ANN

0.992

0.493

25

A+P2

y1

GFA

0.841

0.763

26

A+P2

y1

ANN

0.832

0.350

27

A+P2

Y1

GFA

0.953

0.923

28

A+P2

Y1

ANN

0.557

0.109

29

A+P2

y2

GFA

0.930

0.891

30

A+P2

y2

ANN

0.658

0.442

31

A+P2

Y2

GFA

0.987

0.975

32

A+P2

Y2

ANN

0.949

0.680

33

A

Y2

GFA

0.987

0.974

190.149

34

A

Y2

GFA

0.987

0.974

190.149

35c

A

Y2

GFA

0.984

0.974

284.281

Model


109.495 51.473 192.654 23.147 20.847 88.917 45.973 25.924 26.985 79.177 51.330 31.633 122.529 79.177 192.654

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a. A for using amine structures before reaction as the structure set; P1P2 for using product structures in mechanism 1 for primary amine and secondary amine/water system, and product structures in mechanism 2 for tertiary amine/water system; P2 for using product structures in mechanism 2 for all amine/water system; A+P2 for mixing descriptors of amine structure before reaction and product structure in mechanism 2 for all amine/water system. b. y1, transformed Y1, y2, transformed Y1 was used as the dependent variable of the model, respectively. c. The model was established with dataset of 28 amines without No. 24 amine DBAB.

As shown in Table 2, R2 and R2cv of GFA models are better than those of ANN models, standing that GFA could provide a more stable model for prediction in this work. The success of ANN modeling depends on a large data set28, 36. Due to the limitations of CO2 absorption reaction, the data set is usually small and not enough to applicate to in ANN modeling. Since ANN algorithm introduce a black box for correlating of the descriptors, the results of ANN is also harder to relate to the CO2 absorption mechanism. In this work, GFA method is effective to apply to reduce the dimensionality of variables to remove the effect of chance correlation. CO2 absorption capacities form

4 kind CO2 capacities forms were used for

regression. As shown in Table 2, almost all the transformed capacities models are with higher F value (A: Y2>Y1>y2>y1, P1P2: y2>Y2>y1>Y1, P2: y2>Y2>Y1>y1, A+P2: Y2>Y1>y2>y1), but just few regression models of transformed CO2 absorption capacities (Model 3,7,27,31) are effective for prediction. Univariate analysis26 was carried out to evaluate the similarity of the data set to normally distributed data (Fig. 4), and transformed capacity Y2 is the closest to the normal distribution. Overall, the weight capacities (y2 and Y2) shows better relationships than mole capacities. And from



A+P2 models, the regression of Y2 shows best plot distribution along the line y=x (Fig. 5).

50 Normal Distribution (percent)

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y1 Y1

40

y2 Y2 normal distribution

30 20 10 0

2

4

6

8

Distribution bin Fig. 4 Univariate analysis of 4 CO2 absorption capacities forms


10

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Fig. 5 Graphical representation of experimental versus predicted absorption capacity values of 4 CO2 capacity forms with A+P2 structure set. Black squares mean the data of training set, and the red triangle meaning the data of the test set.

Structures set evaluation

Different structure set considering reactant and products

in different mechanism, including A, P1P2, P2 and A+P2, were shown in Table 2 (y1: A+P2>A>P2>P1P2,

Y1:

A+P2>A>P2>P1P2,

y2:

P1P2>A+P2=P2>A,

Y2:

A+P2=A>P2>P1P2 ). The model of structure set of products such as P1P2 and P2 shows good relativities (R2CV>0.6) but poor predictabilities (F value

Quantitative Relationship between CO2 Absorption Capacity and

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