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Artificial Neural Networks (ANN) for Accurate Prediction of Physical Properties of Aqueous Quaternary Systems of Carbon Dioxide (CO2)-Loaded 4-(diethylamino)-2-butanol (DEAB) and Methyldiethanolamine (MDEA) blended with Monoethanolamine (MEA) Fatemeh Pouryousefi, Raphael O. Idem, Teeradet Supap, and Paitoon Tontiwachwuthikul Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.6b03018 • Publication Date (Web): 14 Oct 2016 Downloaded from http://pubs.acs.org on October 18, 2016

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MEA-DEAB-CO2-H2O SYSTEM

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BPNN MODEL RBFNN MODEL EMPIRICAL MODEL

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Artificial Neural Networks (ANN) for Accurate Prediction of Physical

2

Properties of Aqueous Quaternary Systems of Carbon Dioxide (CO2)-

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Loaded 4-(diethylamino)-2-butanol (DEAB) and Methyldiethanolamine

4

(MDEA) blended with Monoethanolamine (MEA)

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Fatemeh Pouryousefi, Raphael Idem*, Teeradet Supap*, Paitoon Tontiwachwuthikul,

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Clean Energy Technologies Research Institute (CETRI), University of Regina, Regina, SK

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S4S 0A2, Canada

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10 11 12 13 14 15 16 17 18

Corresponding Authors

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*Phone: +1-(306) 585-4470; Fax: +1-(306) 585-4855; Email: [email protected]

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*Phone: +1-(306) 337-2468; Fax: +1-(306) 585-4855; E-mail: [email protected]

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1 ACS Paragon Plus Environment

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Abstract

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Physical and heat transport properties such as density, viscosity, refractive index, heat capacity,

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thermal conductivity, and thermal diffusivity of aqueous carbon dioxide (CO2)-loaded and unloaded 4-

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(diethyl amino)-2-buthanol (DEAB) and methyldiethanolamine (MDEA) as single amines and each

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blended with a primary amine (MEA) were measured at different ranges of temperature (25°C - 60°C),

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amine concentrations (0.5M-2M for tertiary amine and 5M for primary amine), and CO2 loading (0-0.6

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mole/mole amine). Results showed an increasing trend of CO2 loading on density, viscosity and

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refractive index, and a decreasing trend on the heat transport properties. Two Artificial Neural Network

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techniques, Back Propagation Neural Network (BPNN) and Radial Basis Neural Network (RBFNN) as

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well as some well-known empirical correlations from literature, were applied to correlate and predict

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these

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MEA+MDEA+WATER+CO2. Results from the correlation showed that Artificial Neural Network

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techniques gave the least deviation for the prediction of all physical properties of both amine systems

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with less than 1%AAD. The correlation coefficient between the experimental and predicted values in

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terms of R2 value was in the range of 0.98 - 0.99.

physical

properties

for

two

quaternary

systems;

38 39 40 41 42 43 44 45 46


MEA+DEAB+WATER+CO2

and


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1. Introduction

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Research and development in the area of carbon dioxide (CO2) capture and storage (CCS)

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technologies for the greenhouse gas mitigation is on the rise because of its potential in allowing

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continued use of fossil fuels with little or no emissions of CO2 to the atmosphere. Chemical absorption

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using aqueous amine solution is a promising technology due to its effectiveness in reducing CO2

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emissions from fossil-fuel power plants, thus helping alleviate the global climate change1. The amine

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solvent allows the process to reactively capture CO2 from such low pressured flue gases typically found

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in coal-fired power plants2. Therefore, knowledge of the amine chemical/physical properties including

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density, viscosity, and thermal property (e.g. heat capacity and thermal conductivity) acquired during

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the plant run is crucial as they help confirm state of the solvent. Any anomalies can also be early

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predicted if the amine properties are starting to deviate from their original values. An accurate

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determination of physical properties of these solvents specifically density, heat capacity, and thermal

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conductivity must be obtained also for a precise calculation of heat duty during CO2 capture. Normally,

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density, viscosity, refractive index, and thermal related properties such as heat capacity, thermal

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conductivity and thermal diffusivity of amines are measured off-line in a laboratory using expensive sets

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of instrument. Also, prior to measurement, it requires plant personnel to collect the amine samples from

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various locations throughout the plant. Then, the samples must be properly handled and kept to preserve

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to as close to their original forms as possible while transporting to a designated laboratory for a detailed

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analysis. Specifically, sensitive samples such as those containing CO2 withdrawn from lean and rich

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streams must be given an extra care because CO2, often can slowly evolve from the amine due to

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temperature and pressure change. Thus density, viscosity, refractive index, and other properties might

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not even represent true loading from the process.


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To deal with such a complexity and time consuming process, correlation models for predicting

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these physical properties can be formulated and used to replace complicated lab measurement.

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Empirical correlations used extensively are Weiland for density, Nissan Grunburg for viscosity, and

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Gladston-Dale for refractive index. Redlich-Kister is also used extensively to predict and correlate many

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physical properties such as heat capacity, refractive index, and thermal conductivity. Literature work

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correlated density data for MEA+WATER+CO2 at temperatures from 25°C to 80 °C with Weiland

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correlation.3 The maximum relative deviation was 1.6%. A study on the viscosity of carbonated mixed

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aqueous MDEA+MEA at 293.15-343.15 K, 0.2-0.5 amine mole fractions, and 0-0.5 mole CO2/ mole

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amine was also done.4 A modified Grunberg-Nissan equation was also suggested for prediction of the

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viscosities of carbonated MDEA-MEA aqueous solutions. The correlation of the viscosity data using

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Weiland and Grunberg-Nissan correlation for un-loaded aqueous amine solutions were in very good

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agreement between these correlations. For loaded aqueous amine solutions, both Weiland and

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Grunberg-Nissan correlations showed very good agreement in correlating the viscosity of mixed

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conventional alkanolamines solutions. From our previous publication, we measured refractive indices of

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binaries MEA+WATER, MDEA+WATER, and MEA+MDEA and used the Gladston-Dale, Lorentz-

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Lorenz, Weiner, Heller, and Arago-Biot to correlate the experimental data.5 The results showed that the

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Gladstone-Dale correlation fitted the data very well (0.03 % to 0.1 % at different temperatures). For

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ternaries MEA+WATER+CO2 and MDEA+WATER+CO2, also, Gladston-Dale showed the best

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agreement with the experimental data (0.53 % to 1.6 %). Refractive indices of some binaries

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Hexadecane and Heptadecane with n-alkanols was measured and correlated with Gladston-Dale, Heller,

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Lorentz-Lorenz, Arago-Biot, and Weiner equations and they all showed very good agreement with the

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experimental data for her systems.6 Correlation of excess molar heat capacities of aqueous

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alkanolamines such as 3-amino-1-propanol, 2-(methylamino)ethanol (MAE), and 1-amino-2-propanol

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was also done by using the Redlich-Kister equation.7 The maximum average absolute percentage

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deviation was 0.23 %. Again, correlation of excess molar heat capacities for aqueous mixture of 4

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MEA+AMP was done by employing the Redlich-Kister equation.8 The average absolute percentage deviations for binaries and ternary system mentioned were 3.6 % and 13.2 % respectively. However, these correlations mentioned earlier have disadvantages especially when dealing with water based liquid mixtures. The non-ideality of these aqueous amines greatly affects the accuracy of these correlations. In comparison to theoretical based models, Artificial Neural Network (ANN) based methods can be more accurate and faster in predicting the physical properties of both polar and nonpolar liquid mixtures due to its ability to learn from data, classification capabilities, generalization and noise tolerance. In details, the primary advantage of ANN over theoretical and empirical models is that as a black box model, it does not pre-require any governing equation that describes engineering phenomena. Instead, ANN can learn the complex transport processes of a system from given observed data.9 Because of these benefits, ANN shows great potential in helping the CO2 capture plant to predict the physical properties of its solvent system. In 1998, density, viscosity, and refractive index of several ternary and quaternary solvent systems containing water, methanol, acetonitrile and tetrahydrofuran were predicted.10 The relative standard error was less than 1 % for density and refractive index, and 15 % for viscosity. ANN was used also to estimate the density of pure oil-based methyl ester biodiesel. The average absolute percent deviation was 0.29 %.11 As proven success of ANN shown earlier is clear, this work was carried out to formulate correlation models based on artificial neural network techniques to predict density, viscosity, refractive index, heat capacity, thermal conductivity, and thermal diffusivity of amine systems used to capture CO2. Back Propagation Neural Network (BPNN) and Radial Basis Neural Network (RBFNN) were used specifically in this work because of their high accuracy in prediction of physical properties for polar liquid mixtures and faster prediction. Blended systems of monoethanolamine (MEA) with methyldiethanolamine (MDEA) and 4-(diethylamino)-2-butanol (DEAB) were used to showcase the ANN technique applicability and prediction accuracy. MDEA was included in this study based on its being a common blend ingredient with MEA used widely. DEAB specifically developed by the Clean 5 ACS Paragon Plus Environment

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Energy Technologies Research Institute (CETRI) of the University of Regina was chosen for this work due to its potential in replacing MDEA for a higher CO2 absorption capacity and reduced regeneration energy12. Physical and heat transport property data based on density, viscosity, refractive index, heat capacity, thermal conductivity, and thermal diffusivity of the 2 blends used for correlating the ANN based models were measured under 25⁰C - 60 ⁰C temperature, 0.5M - 2M tertiary amine +5M primary amine concentrations, and 0-0.6 mole CO2/ mole amine. These conditions were typical of the CO2 capture process. High temperature measurement (e.g. regeneration temperature:120°C and higher) of CO2 loaded amine solvent could lead to errors because CO2 could be released from the liquid solution to the gas phase, thus changing the loading of the solution whose property is being measured. To ensure the accuracy of the measurement and prediction of the ANN models, the on-line determination of all properties in this study was done between 25⁰C - 60 ⁰C. Thus, at this range of temperature, CO2 composition was confidently maintained at the same value during the measurement of physical and heat properties. The experimental data were used to develop the prediction models by using nine data inputs with 70% of the data given to training the systems to predict the properties. The predicted values obtained from BPNN and RBFNN correlations were then compared with those of well-known empirical models consisting of Weiland, Nissan-Grunberg, Gladston-Dale, and Redlich-Kister, developed also with the same data set. The comparison was done to confirm the superiority of ANN derived models in terms of higher prediction accuracy for density, viscosity, refractive index, heat capacity, thermal conductivity, and thermal diffusivity of the amine systems. 2. Theoretical Calculations 2.1 Back Propagation Neural Network (BPNN) & Radial Basis Neural Network (RBFNN) Based Correlations



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The BPNN is one of the most widely used ANN techniques in chemical applications. Other than input and output layers, The BPNN can have multiple hidden layers as well. The hidden layers relate input and output. First, weights for the Neurons are randomly assigned. When the network is being trained, the given prediction is compared to the actual data. Based on the error computed between the predicted and actual data, the adjustment will be calculated and considered (backpropagated) through the network, which provides the weight adjustment. In an ANN technique, a portion of input/output data selected from the initial data set is used to train the network, and then the network iteratively adjusts its connection weights and bias values according to the computed errors, which are calculated between the network output and actual output. This training: testing ratio depends on the size of data set available. If there is a large data set, it is ok to consider a ratio of 75:25 to train a generalized ANN. If the ANN is not a generalized one, it tends to over-fit the data and it will predict bizarre outputs for some inputs which were not observed with the training set. If the size of data set is small, it will be better to go with a ratio of 90:10 for training: testing. After being well trained, the BPNN can then be used to predict the unseen target parameters13. Figure S1 shows a feedforward ANN with input units (X units), hidden units (Z units) and output units (Y units).13 W0k and V0j represent bias having output 1 on unit Yk and Zj respectively. During feedforward in stage 1, each input unit Xi receives input signal and broadcasts to hidden units Zj. The hidden units compute activation and send signals to output units Yk. The output units compute activation and produce the output signal of the net from the input pattern. In stage 2, calculation and backpropagation of error is performed. Each output unit computes its activation Yk and compares it with the target output tk to determine the associated error. Based on the error, the factor δ k is computed. The factor δ k distributes error to the output layer and the hidden layer. The weights are then updated between the output layer and the hidden layer. The hidden layer then computes error factor δ j , but it is not necessary to distributes error to the input layer. The factor

δ j is only used to update the weights


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between the hidden and the input layer. In stage 3, after all the error factors are computed, the weights of all layers are adjusted simultaneously. The adjustment of the weights from the hidden layer to the output layer is based on the error factor and the activation of hidden units. The adjustment of weights from the input layer to the hidden layer is based on the error factor and the activation of the input layer. RBFNN is another type of neural network which has shown better results in compared to BPNN especially in prediction. These networks generally have 3 layers, the input layer, the hidden layer with the RBF non-linearity, and the linear output layer. RBFNN can beat some of the limitation of BPNN because of using rapid training phase, and having a simple architecture. The RBFNN simulates and predicts the aimed phenomena by using Gaussian basis functions in the hidden layer and linear transfer functions in the output layer. The difference between BPNN and RBFNN comes from the hidden layer. In RBFNN, the distance between the input and center is used in the RBFNN learning process. The first layer of the RBFNN collects the input data. Its training process determines the number of hidden neurons, m, which can be larger than that of BPNN to achieve a certain accuracy of prediction.14

For each neuron in the hidden layer, the distance between the input data and the center is activated by a nonlinear radial basis function, as shown in the following equation;13

[

2 Yˆ = exp − ( xi − ci b1)

]

(1)

Where xi is the input vector, and b1 and ci are parameters that represent the bias in the hidden layer and center vector respectively. Each neuron in the hidden layer will produce a value between 0 & 1 according to how close the input is to the location of the center. Therefore, neurons with centers closer to inputs will have more contributions to outputs; however, if neurons have centers far away from inputs, then the outputs would be nullified. Later, the output layer neurons receive the weighted inputs



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from the hidden layer and predict the results by using a linear combination, which is of a similar from to that of the BPNN;

m

ˆ = ∑ωiRi + b2 Υ

(2)

i =1

Where Yˆ is the RBFNN simulation results, wi is the optimized connection weight determined through the training process, and b2 is the bias in the output layer. In RBFNN simulations, the proper initial choice of centers and weights should be regarded as key issues. Various methods are proposed to define the center, but in this study random selection was used to define these parameters. Once the centers have been developed, the weights linking the hidden and output layers should be updated during the training procedure. The training procedure in the RBFNN also determines the number of hidden neurons required for the simulation. The training of RBFNN is initiated by first generating a single neuron in the hidden layer, followed by continuously adding neurons to the hidden layer at a time. In RBFNN, it is very important to determine the number of neuron carefully in the hidden layer because it affects the complexity and the prediction ability of the network. Too many neurons in the hidden layer may cause poor generalization or over-fitting, while insufficient neurons cannot learn the data adequately. In addition, the position of the centers in the hidden layer also affects the network performance significantly, while the nonlinear transfer functions used by RBFNN have little influence.

2.2 Semi-Empirical Correlations Different correlations from literatures have been also applied to correlate the physical and thermal properties measured for both blended amine systems. These correlations were short-listed for this work based on their well-known application in the single property prediction for the polar solutions of the same family type which is amine solutions. Weiland and non-additive equations respectively 9 ACS Paragon Plus Environment

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shown in Equations (3), (4), and (5) were selected for correlation of density data.15 Equations (6) and (7) representing Gladston-Dale16 and Arago-Biot (as used by Sharma et al.17) correlations were chosen for refractive index. Nissan Grunberg18 given in Equation (8) was used to correlate viscosity. Finally, Redlich-Kister19 correlation was applied to model heat capacity, thermal conductivity, and thermal diffusivity which were given in equations 9-15. For density, r

ρ = ∑i =1 xi ρ i 3

∑ ρ

=

( x

i

.M

i

(3)

(4)

)

i = 1

V

Where

V = xAM .vAM + xW .vW + xCO2 .vCO2 + xAM .xW .v* + xAM .xCO2 .v**

(5)

χ i , ρ i , and Μ i are mole fraction, density, and molecular weight of each component respectively in

the amine system. ρ represents the density of the mixture and V is the molar volume of the solution. For an ideal solution, V is the sum of the partial molar volumes of the components multiplied by their respective molar fractions. However, amine solutions loaded with CO2 are not ideal solutions. Thus, extra parameters are needed to account for interactions of amine + H2O and amine + CO2. These parameters are given respectively by ν* and ν** respectively. For refractive index, 236

nm − 1

ρ

r

= ∑i =1

ni − 1

237ωi

(6)

nm = ∑i =1 niφi

(7)

ρi r



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n

Where; respectively.

φ

m

i

and

φ

i

are volume fraction of pure component i and refractive index of mixture,

is calculated from xi Vi /

∑ xiVi which ni and ωi are refractive index of component i

and weight fraction of component i

For viscosity, ln η mix = χ i ln ηi + χ i χ j d

(8)

Where d is the adjustable parameter independent on operational parameters18, η i and ηmix are the viscosities of component i and the mixture, and X i and

χ j are the mole fractions of component i and

j

For heat capacity, CP

n

E 12

/( J / mol . K ) = χ 1 χ 2 ∑ i =1 Ai ( χ 1 − χ 2 ) i −1

(9)

Where; the temperature dependence of Ai is assumed to follow the temperature relation as follows;

Ai = ai,0 + ai,1(T / K)

(10)

E

It should be noted that the excess experimental molar heat capacities ( CP ) were calculated from the experimental molar heat capacity values by:


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C P /( J / mol.K ) = C P − ( χ1C P ,1 + χ 2C P ,2 )

E

n

K E 12 /( KW / m . K ) = χ 1 χ 2 ∑ i =1 Ai ( χ 1 − χ 2 ) i −1

Ai = ai,0 + ai,1(T / K)

n

D E 12 /( m 2 / s ) = χ 1 χ 2 ∑ i =1 Ai ( χ 1 − χ 2 ) i −1

Ai = ai,0 + ai,1(T / K)

(11)

(12)

(13)

(14)

(15)

Where Ai and ai are adjustable and temperature dependent parameters.

3. Experiments 3.1. Chemicals and Equipment DEAB was synthesized according to the procedure described in the literature20. The chemicals used for DEAB synthesis were methyl vinyl ketone (MVK, 95%), sodium borohydride (NaBH4, 98%), methanol (HPLC grade) and diethylamine (99.5 %). All chemicals were obtained from Sigma Aldrich (Sigma- Aldrich Canada Co, Ontario), except MVK from Fisher Scientific (Fisher Scientific Company, Ontario). Structural confirmation of the DEAB were carried out by nuclear magnetic resonance spectrometer (NMR and Varian Mercury plus 300M Spectrometer). The purity of DEAB was also determined by the NMR to be in the range of 98%-99%. The MEA and MDEA were purchased from Sigma Aldrich (99% purity). Desired blend ratios of MEA+MDEA and MEA+DEAB were prepared by mixing predetermined weight of each amine in the blend. The rest of volume was made up using deionized water (DI water). Total concentration of the mixture was confirmed by titration with 1 N Hydrochloric Acid (laboratory 12 ACS Paragon Plus Environment


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grade from Fisher Scientific) to the end point of methyl orange indicator (0, 1 % indicator, from Sigma Aldrich). For CO2 loaded solutions, research grade CO2 (from Praxair Canada Inc.) was passed into the desired amine solution. The exact CO2 loading was measured also by HCl-methyl orange titration and displacement technique described in details in the literature.20,21 Density, viscosity, and refractive index measurement of all amine solutions were straightforward. Density and viscosity were measured simultaneously with a densitomer coupled with a viscometer (from Anton Paar) using approximate 10 mL sample size. Refractive indices at the wavelength of 589 nm were measured on a separate instrument using automatic refractometer purchased from Anton Paar. Thermal conductivity, thermal diffusivity, and heat capacity were determined by liquid thermal conductivity meter (THW-LAMBDA, ThermTest Inc, Ontario) operated by a transient hot wire technique. The instrument used a thin platinum wire immersed in the amine sample which the heat resistance profile of wire is measured with respect to time and a temperature-time profile of amine sample were generated. These plots were then used to determine thermal conductivity, thermal diffusivity, and heat capacity. Thermal conductivity and diffusivity were measured specifically at the beginning when temperature become stable, and then using the correlation of thermal diffusivity and heat capacity (D= K / (ρ Cp)), heat capacity will be computed at different time intervals. It should be noted that measurement of heat capacity required an input of predetermined density values of the sample at 3-4 temperatures for the software to be able to plot the density-temperature profile for later computation of heat capacity at different temperatures. These were provided using density data described earlier. At least 3 repeated measurements of density, viscosity, refractive index, and thermal related properties were taken for each sample which their averaged values were used and reported. Prior to actual test, all physical and heat measurement techniques and instruments were validated at 25⁰C - 60

⁰C range of temperature against literature values using water and pure amines (e.g. MEA, MDEA, and DEAB). The confirmation of validity and accuracy can be viewed in our previous work.20 Detailed information of MDEA measurement (e.g. viscosity, density and refractive index) previously done in the 13 ACS Paragon Plus Environment

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other works5, particularly in the mixture with MEA, CO2 and H2O also confirms the validity of the data presented in this study. Table S1also shows all specifications related to the equipment used to measure these physical properties.

4. Results and Discussions

4.1 Measurement of Density, Viscosity, Refractive Index, Heat Capacity, Thermal Conductivity, and Thermal Diffusivity This section reports density, viscosity, refractive index, heat capacity, thermal conductivity, and thermal diffusivity data of pure amines and water, binary, ternary, and quaternary systems containing DEAB + H2O, DEAB + CO2 + H2O, and DEAB + MEA + CO2 + H2O and MDEA + MEA + CO2 + H2O, respectively. These data were needed later for development of correlation models using ANN techniques and empirical equations described earlier in section 2. For pure amine systems, density, viscosity, and refractive index shown in Figure S2 (a) – (c) were measured at 25 °C from 60 °C, while 25 °C to 50 °C temperature range was used to determine heat capacity, thermal conductivity, and thermal diffusivity, given in Figure S3 (a), (b), and (c), respectively. For binary DEA and water system, density, viscosity, and refractive index in Figure S4 (a) – (c) were measured from 25 °C to 40 °C for aqueous solutions of DEAB prepared at 0.5M, 1M, 1.25M, 1.5M, and 2M corresponding to 0.01 – 0.05 mole fraction range of DEAB in the system. The same range of DEAB mole fraction and temperature were also used to determine all thermal properties of the binary system shown in Figure S5 (a), (b), and (c) respectively for heat capacity, thermal conductivity, and thermal diffusivity. For ternary system containing DEAB, H2O, and CO2, density, viscosity, and refractive index of 1.5M DEAB loaded with CO2 from 0 to 0.70 mole CO2/mole DEAB reported in Figure S6 (a) to (c) were measured at temperatures from 22 °C to 42 °C. Heat transport properties of the same solution but loaded with 0 to 0.50 mole CO2/mole and measured at temperatures ranging from 25 °C to 40 °C were shown in Figure 14 ACS Paragon Plus Environment


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6S (a), (b), and (c) respectively for heat capacity, thermal conductivity, and thermal diffusivity. Quaternary systems used 5 M aqueous MEA solution mixed with 1.5 M of either DEAB or MDEA as a based solution. Both mixtures were then loaded with various CO2 loading in range of 0 – 0.6 mol CO2/mol amine and measure for all properties between 25 °C to 50 °C. Density, viscosity, and refractive index of quaternary DEAB and MDEA based systems can be found in Figure S7 to S12. Heat capacity, thermal conductivity, and thermal diffusivity data of the same systems can also be viewed in Figure S13 to S18.

4.2 Prediction of Density, Viscosity, Refractive Index, Heat Capacity, Thermal Conductivity, and Thermal Diffusivity ANN based Models and Empirical Correlations Two methods of artificial neural network known as BPNN and RBFNN were used to correlate data obtained from Section 4.1. MEA + DEAB + H2O + CO2 system was used to showcase the applicability and goodness of these ANN based techniques in terms of precision and accuracy in prediction of density, viscosity, refractive index, and thermal transport properties required during the capture process of CO2. Well-known empirical correlations namely, Weiland and non- additive equations for density, Nissan-Grunberg for viscosity, Gladston-Dale and Arago-Biot for refractive index, and Redlich-Kister for heat capacity, thermal conductivity, and thermal diffusivity also used to correlate the experimental data for prediction of the mentioned physical properties were also compared for accuracy to the ANN based methods. DEAB based system never reported was chosen for the discussion in this section due to its high efficiency as a solvent mixture to capture CO2 (e.g. CO2 equilibrium solubility and regeneration heat duty). Also, this solvent has never been reported elsewhere on this aspect, thus introducing new knowledge to the CO2 capture research community. Figure 1 and 2 show architectural diagrams respectively for BPNN and RBFNN, for the quaternary system. Mole fractions and measurement temperature were considered as inputs while physical properties of the system were considered as outputs. Party chart plotted between predicted and 15 ACS Paragon Plus Environment

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experimental density obtained from BPNN and RBFNN prediction is shown in Figure 3. Densities of the same DEAB system predicted using Weiland and non-additive equation based empirical correlations are also plotted in sample Figure for comparison. BPNN and RBFNN could clearly predict density of the amine mixture very accurately, confirmed by very low %AAD averaged to only 0.09 and 0.12%, respectively. The empirical models on the other hand were much inferior to the ANN based models. Specifically, density values predicted by Weiland equation deviated from the actual values by as much as 76.56%. Though, non-additive model gave an acceptable 5.28%AAD. This model was still far less accurate than BPNN and RBFNN that gave very small % error, virtually close to zero. For viscosity prediction shown in Figure 4, BPNN and RBFNN still did superbly well showing %AAD between the predicted and experimental viscosity values of only 3.10 and 2.52%, respectively, compared to 16.27%AAD obtained from commonly used Nissan Grunberg based correlation. Figure 5 is used to compare refractive index predicted also from ANN techniques to those obtained from Gladston-Dale and Arago-Biot correlations. Prediction accuracy indicated by %AAD is in the order of 0.05, 0.04, 0.5, and 4.025%, respectively for BPNN, RBFNN, Gladston-Dale, and Arago-Biot. Very small deviations of refractive index prediction once again, confirm the applicability of the ANN based models generated in this study. Figures 6, 7 and 8 respectively compare heat capacity, thermal conductivity, and thermal diffusivity obtained by BPNN and RBFNN to those of the empirical Redlich-Kister model. It is clear that BPNN and RBFNN gave the most accurate prediction for DEAB based solvent with less than 1% deviation for all thermal properties. Redlich-Kister on the other hand, showed very high inaccuracy of the predicted values clearly indicated by 27.88%, 45.21%, and 21.93% deviations for heat capacity, thermal conductivity, and thermal diffusivity, respectively. ANN based models were also used to predict all properties for MDEA quaternary system (MEA-MDEA-CO2-H2O). Figures 9 and 10 show clearly how accurately ANN based model could predict density and heat capacity of MDEA quaternary based system. Also, viscosity, refractive index, thermal conductivity, and thermal diffusivity were also 16 ACS Paragon Plus Environment


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predicted accurately as shown in Figures S19, S20, S21, and S22, respectively. For all measurements, the level of accuracy was still exceptional for both BPNN and RBFNN shown by the maximum of 1.6%AAD compared to as high as 65% from the empirical model predictions. The measurement and prediction accuracy of a known amine like MDEA also confirm the validity of DEAB data presented in this study.

5. Conclusions This study presented for the first time experimental data on physical properties of new alcohol amine solvent, 4-(Diethylamino)-2-buthanol (DEAB) in pure and aqueous solution with/without CO2 under CO2 capture range conditions. Results showed that the density and viscosity and thermal properties of this new solvent were lower than those of conventional MEA and MDEA, which could be considered beneficial for the CO2 capture process in terms of easy operation and energy efficient. The Artificial Neural Network (ANN) was used to correlate the experimental data for an accurate prediction of density, viscosity, refractive index, heat capacity, thermal conductivity, and thermal diffusivity. Using DEAB-MEA-CO2-H2O and MDEA-MEA-CO2-H2O systems, the results have confirmed that ANN based BPNN and RBFNN model were very accurate to predict all properties of the quaternary amine systems with an overall deviation of less than 1%. The empirical correlations commonly used, including Weiland and non-additive equation for density, Nissan Grunberg for viscosity, Gladston-Dale, and Arago-Biot for refractive index, and, Redlich-Kister for prediction of heat capacity, thermal conductivity, and thermal diffusivity, were incomparable to the ANN models and far less accurate in predicting these physical properties indicated by as high as 76%AAD value. Thus, ANN based techniques have proven themselves to be a useful tool for an accurate prediction of amine system’s physical properties required during the operation of CO2 capture process, which could not be predicted accurately by the existing empirical correlations commonly presented in the literatures. It must be noted that the effect of impurities such as amine degradation products was also an important factor 17 ACS Paragon Plus Environment

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which could alter the predicted physical and heat property values of amine from the actual values. To evaluate this effect thoroughly, experimental plan and schedule have been set up to incorporate various degradation products and flue gas impurities into building a prediction model which will be reported in our future works.

Supporting Information Backpropagation network diagram, data of density, viscosity, refractive index, and thermal properties of pure amines and mixtures of DEAB-H2O, DEAB-CO2-H2O, MEA-DEAB-CO2-H2O and MEA-MDEA-CO2-H2O; parity plot for prediction of viscosity, refractive index, and thermal conductivity and diffusivity in MEA-MDEA-CO2-H2O mixture; equipment for measurement of all physical properties.

Acknowledgements The financial support provided by the Natural Science and Engineering Research Council of Canada (NSERC), Canada Foundation for Innovation (CFI), the Clean Energy Technologies Research Institute (CETRI), and Faculty of Graduate Studies and Research (FGSR), University of Regina is gratefully acknowledged.



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References (1) Zhang, Y.; Chen, H.; Chen, C.-C.; Plaza, J. M.; Dugas, R.; Rochelle, G. T. Rate-Based Process Modeling Study of CO2 Capture with Aqueous Monoethanolamine Solution. Ind. Eng. Chem. Res. 2009,

48, 9233. (2) Chakma, A. Separation of CO2 and SO2 from flue gas streams by liquid membranes. Energy Convers. Manage. 1995, 36, 405. (3) Amundsen, T. G.; Eimer, D. A. Density and Viscosity of Monoethanolamine + Water + Carbon Dioxide from (25 to 80) C. J. Chem. Eng. Data. 2009, 54, 3096. (4) Fu, D.; Chen, L.; Qin, L. Experiment and Model for the Viscosity of Carbonated MDEA−MEA

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(20) Pouryousefi, F. Development of On-Line Analytical Technique for Determination of Composition of CO2-Loaded Formulated Amine Solvents Based on the Liquid Thermo physical Properties for a PostCombustion CO2 Capture Process. PhD Thesis, University of Regina, 2015.

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List of Figures Figure 1 Architecture of BPNN for thermal property prediction of MEA+DEAB (MDEA) +WATER+ CO2 system

Figure 2 Architecture of RBFNN for thermal property prediction of MEA+DEAB (MDEA) +WATER+ CO2 system

Figure 3 Parity chart for density: MEA+DEAB+water+CO2 system Figure 4 Parity chart for viscosity: MEA+DEAB+water+CO2 system Figure 5 Parity chart for refractive index: MEA+DEAB+water+CO2 system Figure 6 Parity chart for heat capacity: MEA+DEAB+water+CO2 system Figure 7 Parity chart for thermal conductivity: MEA+DEAB+water+CO2 system Figure 8 Parity chart for thermal diffusivity: MEA+DEAB+water+CO2 system Figure 9 Parity chart for density of MEA+ MDEA+water+ CO2 system Figure 10 Parity chart for heat capacity of MEA+ MDEA+water+ CO2 system TOC/GRAPHIC



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Figure 1: Architecture of BPNN technique for thermal property prediction of MEA+DEAB (MDEA) +WATER+CO2 system


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Figure 2: Architecture of RBFNN for thermal property prediction of MEA+DEAB (MDEA)+WATER+ CO2 system



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Figure 3: Parity chart for density: MEA+DEAB+water+CO2 system


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Figure 4: Parity chart for viscosity: MEA+DEAB+water+CO2 system



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Figure 5: Parity chart for refractive index: MEA+DEAB+water+CO2 system


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Figure 6: Parity chart for heat capacity: MEA+DEAB+water+CO2 system



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Figure 7: Parity chart for thermal conductivity: MEA+DEAB+water+CO2 system


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Figure 8: Parity chart for thermal diffusivity: MEA+DEAB+water+CO2 system



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Figure 9 Parity chart for density of MEA+ MDEA+water+ CO2 system


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Figure 10 Parity chart for heat capacity of MEA+ MDEA+water+ CO2 system


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