Systematic selection of amine mixtures as post-combustion CO2 capture solvent candidates

Journal of Cleaner Production xxx (2016) 1e17

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Systematic selection of amine mixtures as post-combustion CO2 capture solvent candidates Theodoros Zarogiannis a, b, Athanasios I. Papadopoulos a, *, Panos Seferlis a, b a b

Chemical Process and Energy Resources Institute, Centre for Research and Technology-Hellas, Thermi 57001, Thessaloniki, Greece Department of Mechanical Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece

a r t i c l e i n f o

a b s t r a c t

Article history: Received 13 January 2016 Received in revised form 21 April 2016 Accepted 25 April 2016 Available online xxx

A systematic approach is proposed for the preliminary screening of binary amine mixtures as CO2 capture candidates considering several important properties as selection criteria. The approach consists of several decision making stages which account for solventesolvent and solventesolventeCO2 interactions using standard group contribution models as well as equations of state and activity coefficient models for a wide range of molecular structures. A multi-criteria assessment methodology is combined with a systematic uncertainty quantification approach to unveil important trade-offs among several important properties and to propose the mixtures that appear to be promising as CO2 capture candidates. The aim of the proposed approach is to support the identification of few valid amine combinations which may then be evaluated using rigorous prediction models or experiments, while quickly avoiding amine options of poor performance. The proposed method is applied in mixtures resulting from numerous binary combinations of amines which have been previously investigated in their pure aqueous form as CO2 capture solvents. A mixture of 4-diethylamino-2-butanol (DEAB) and 2-amino-1-hexanol (2A1H) appears to be promising among other useful options. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Post-combustion CO2 capture Amine solvents Mixtures Systematic selection

1. Introduction Post-combustion CO2 capture through chemical absorption/ desorption systems is predominantly performed using aqueous solutions of single amines to selectively dissolve and separate CO2 from flue gases (Fong et al., 2016). The globally observed preference towards solvent-based absorption is largely because the technology is well established, the conditions for both absorption and solvent regeneration are relatively easy to meet and the process can be easily retrofitted onto existing plants (Aaron and Tsouris, 2005). Major downsides involve the increased energy required for solvent regeneration (Zie˛ bik et al., 2015), environmental aspects associated with the toxicity of the solvent and solvent derivatives, the presence of contaminants that affect the solvent performance and lead to solvent degradation and the corrosion of equipment caused by the solvent itself (Papadokonstantakis et al., 2015) and so forth. Extensive research efforts which include the computational and experimental screening of numerous different solvents have shown that aqueous solutions of single amines often trade favourable

* Corresponding author. E-mail address: [email protected] (A.I. Papadopoulos).

behaviour in certain desired CO2 capture characteristics for undesired effects in others. For example, amines such as AMP (2-amino2-methyl-1-propanol) (Harbou et al., 2013) or MDEA (methyldiethanolamine) (Liu et al., 2016) have been found to support a low regeneration energy duty in the desorber at the expense of much slower kinetics than MEA (monoethanolamine) hence resulting in the need of a larger volume of packing material. To address such shortcomings these amines are increasingly combined with amines that exhibit fast kinetics such as DEA (diethanolamine) (Harbou et al., 2013), PZ (piperazine) and others (Kim and Svendsen, 2011). This is just one example out of indicative publications given in Table 1 which consider aqueous solutions of binary amine mixtures as alternatives to single amines in order to attain an overall favourable CO2 capture behaviour, avoiding several of the undesired effects. Mixtures are clearly appealing as CO2 capture solvent candidates but the selection of the number of components in the mixture, of the mixture composition (i.e. chemical and physical characteristics of the amines) and concentration (i.e. amount of each amine in the mixture) is a very challenging problem due to a) the highly nonideal mixtureewatereCO2 interactions, b) the availability of countless combinations of amine molecules as potential mixture

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Please cite this article in press as: Zarogiannis, T., et al., Systematic selection of amine mixtures as post-combustion CO2 capture solvent candidates, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.04.110

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T. Zarogiannis et al. / Journal of Cleaner Production xxx (2016) 1e17

Table 1 Key findings from experimental investigation of CO2 capture mixtures in literature. Mixture

Key findings

Reference

MEA/MDEA EDA/AMP MAPA/AEEA

Achieved large heat duty reduction in regeneration column compared to MEA. Indicated higher overall CO2 absorption rate and loadings compared to MEA. Reduced regeneration energy requirements and indicated potential for significant size reduction in desorption column compared to pure solvents. Showed higher CO2 solubility than pure DEEA. Mixture reaction rate constant was found to be twice as high as the direct summation of reaction rate constants of the aqueous single amines Removed 41% more CO2 per cycle per litre solution than 5 M MEA. Showed 128% higher cyclic solvent capacity compared to MEA.

Aroonwilas and Veawab, 2007 Kemper et al., 2011 Arshad et al., 2013

DEEA/PZ PZ/AEPD PZ/TMBPA PZ/AMP

components and c) the need for combined consideration of several properties as criteria for the selection of mixtures with improved CO2 capture performance. Existing published research focuses on the predominant use of experimental approaches to identify useful mixture candidates. Although experiments are indispensable to accurately identify promising mixtures, the sheer number of potential solvent combinations and the need to consider multiple selection criteria result in experimental costs and effort that become prohibitive for the investigation of a large number of mixtures. The use of computer-aided tools can help address these challenges through models that enable accurate predictions of the desired mixture properties and systematic procedures to account for the combinatorial complexity (Papadopoulos et al., 2013). Computer-aided approaches have been successfully applied in the investigation of mixtures for non-CO2 capture applications with recent reviews provided in Jonuzaj et al. (2016) for solvents, in Linke et al. (2015) for heat exchange fluids and in Ng et al. (2015) for other applications. While computer-aided tools have received attention in CO2 capture research (Yong et al., 2016), such approaches have been considered to a limited extent in the field of CO2 capture solvents with few available published works focussing on the development of rigorous property prediction models for mixtures of CO2 and/or water with a single solvent. To this end, Pereira et al. (2011) employed the SAFT-VR equation of state (EoS) (Gil-Villegas et al., 1997) for the prediction of vapoureliquid-equilibria in the design of solvents for physical separation of CO2 from natural gas, whereas Burger et al. (2015) used the more advanced SAFT-g SW EoS (Lymperiadis et al., 2007) for the integrated solvent and process design in a similar application. Bardow et al. (2010) used the PCP-SAFT EoS (Gross, 2005) for integrated solvent and process design in physical absorption of CO2, whereas Oyarzún et al. (2011) extended this work by considering more solvent options, Stavrou et al. (2014) further considered more process design options and Lampe et al. (2015) used a version of PCP-SAFT EoS for simultaneous solvent and process design. Damartzis et al. (2016) investigated several different single amine solvents in the optimum design of chemisorption flowsheets using SAFT-VR. Finally, Mac Dowell et al. (2010) have used SAFT-VR to identify the optimum concentration of an AMP and ammonia mixture together with the optimum chemisorption process characteristics where this solvent was utilized. Rigorous models are indispensable for the accurate representation of solventeCO2 and/or water interactions. However, the reviewed literature shows that previous attempts mostly focus on single solvents and physical CO2 separation, except for Mac Dowell et al. (2010) who investigated the applicability of a mixture in chemisorption process design. Even in these cases, the need to consider multiple different components and properties as selection criteria requires intense effort and time to develop and use models for the prediction of each selection criterion for each one of the candidates. The problem of mixture selection for chemisorption systems is considerably more challenging due to increased combinatorial complexity and to the very non-ideal

Sutar et al., 2013 Ume et al., 2012 Aronu et al., 2010 Bruder et al., 2011

chemical interactions. Such characteristics limit the selection to the consideration of very few options. While there is no reported implementation of systematic methods for the evaluation of multiple different mixtures as chemisorption solvents, there are few works that address the design and selection of single chemisorption solvents. Recently, Papadopoulos et al. (2014) outlined a systematic approach for the selection of single amines as CO2 capture solvents, while Papadopoulos et al. (2016) proposed the CAMD of single amine solvents considering multiple different criteria pertaining to thermodynamics, reactivity and sustainability. Papadokonstantakis et al. (2015) showed how such systematic approaches can be extended to perform integrated solvent and process design. These authors used mainly group contribution (GC) (Hukkerikar et al., 2012) but also other models in order to calculate several properties accounting for pure amines or amineeCO2 interactions. They also used the SAFT-VR (Gil-Villegas et al., 1997) and SAFT-g SW (Lymperiadis et al., 2007) equations of state to rigorously predict the very non-ideal equilibrium behaviour of several solventewatereCO2 systems. Selected properties (e.g. vapour pressure, solubility, viscosity, cumulative energy demand, ozone depletion potential, etc.) were employed as solvent evaluation criteria in a systematic and fast screening approach. Their selection was based on their ability to capture the molecular chemistry effects on capital and operating expenditures and sustainability impacts of the absorption/desorption CO2 capture process. The widely acknowledged reliability and broad utilization of GC models in the identification of useful solvents for diverse applications (Gani, 2007) and the simultaneous use of several properties as selection criteria compensated for the consideration of only pure amine characteristics or amineeCO2 interactions during the screening and verifiably resulted in few but effective single amines as CO2 capture solvents. The successful verification of the identified amines was based on the SAFT-VR and SAFT-g SW equations of state as well as on experimental results from the literature. Bommareddy et al. (2010) utilized GC predictive models in a computer aided molecular design (CAMD) approach to enable the screening of a wide set of molecular options. The work was extended by Chemmangattuvalappil and Eden (2013) who advanced the original CAMD method. Phase and chemical equilibria were not considered. Furthermore, Salazar et al. (2013) also used an approach of similar rationale to identify few selected single solvents of favourable CO2 capture behaviour which were also investigated in terms of their process performance considering the non-ideal solventeCO2ewater interactions through UNIFAC (Fredenslund et al., 1975) and NRTLbased (Renon and Prausnitz, 1968) equilibrium calculations. The current work proposes for the first time a computer-aided approach which supports the preliminary screening of a large number of binary amine mixtures as CO2 capture candidates for chemisorption systems. The aim is to quickly search among numerous different candidates and to reliably identify potentially promising mixture options which can then be investigated more thoroughly using rigorous property prediction models or

Please cite this article in press as: Zarogiannis, T., et al., Systematic selection of amine mixtures as post-combustion CO2 capture solvent candidates, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.04.110

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experimental work. The proposed approach builds on the works of Papadopoulos et al. (2014, 2016) and Papadokonstantakis et al. (2015) who showed that the evaluation of single amines as CO2 capture options considering only pure component amine properties or amineeCO2 interactions as selection criteria has provided valid results. It is therefore reasonable to extend this approach into the investigation of binary amine mixtures although it is considerably more challenging than in the case of single amines. GC models can be used to support fast screening but the calculation of amineeamine and, amineeamineeCO2 properties which will be used as selection criteria require the utilization of EoS and activity coefficient models (of varying types among different properties) to account for vapour and liquid phase non-idealities. Uncertainty in the predictions made by the available models will also be considered systematically for the first time in the selection of CO2 capture solvent mixtures in order to ensure that the resulting mixture candidates will still represent valid options. Based on these requirements and on the need to combine various different models, the mixture selection procedure will be organized into several decision making stages so that useful insights regarding mixture behaviours can be identified and exploited while avoiding unnecessary computations. Decision making will be supported by a multi-criteria assessment approach (Papadopoulos and Linke, 2006a) whereas a systematic uncertainty quantification approach will also be employed, adapted for the case of mixtures from Papadopoulos et al. (2016).

2.

3.

4.

2. Proposed approach 2.1. Mixture selection criteria 5. Prior to discussing the steps of the proposed approach it is necessary to elaborate on the criteria that will be used for the selection of mixtures since their utilization will determine the implementation of the approach. The early stage selection of binary amine mixtures requires property criteria which are selected based on their potential to reflect on important capture characteristics, on the availability of appropriate models for their calculation and on the availability of sufficient data so that these models may be applied in a wide range of molecular structures. Considering these factors the proposed criteria and the methods required for the calculation are significantly extended from the work of Papadopoulos et al. (2014, 2016) for single amines and discussed as follows:

6.

7. 1. Solubility: The consideration of solubility as a selection criterion aims to identify amines which are miscible with each other and support increased solubility of CO2. It is necessary to obtain a homogeneous binary amine mixture which does not present a liquid phase split in any of the conditions of the absorption/ desorption process. In the case of single amine interactions with CO2 it is sufficient to calculate the Relative Energy Difference Index (RED) (Retief, 2012) which is based on the partial Hansen solubility parameters (HSPs) (Hansen, 1967) of the amine and the CO2. RED is a ratio of the squared differences (distance) between the HSPs of the amine and the CO2 divided by the radius of a Hansen solubility parameter sphere (Stefanis and Panayiotou, 2008; Hansen, 2004). As the difference between the HSPs of the amine and the CO2 increases this indicates that more energy is required for the dissolution of CO2 from the particular amine solvents hence lower values of RED are desired. HSPs may also be employed for the determination of the amineeamine miscibility as well as for the calculation of amineeamineeCO2 miscibility. Their use in the case of mixtures containing more than two components is detailed in Durkee (2007). HSPs can be obtained through GC methods such as Hukkerikar et al. (2012) or Stefanis

3

and Panayiotou (2008). In the case of amineeamine miscibility it is also possible to use a method that considers the Gibbs free energy of mixing through the use of the UNIFAC activity coefficient model (Conte et al., 2011a). Vapour pressure: This is an important parameter for absorption/desorption post-combustion CO2 capture systems because it is associated with the solvent losses. The property may be calculated through GC or other models in the case of pure components (Papadopoulos et al., 2014), but requires vapoureliquid equilibrium (VLE) calculations in order to determine the pressure of the amineeamine mixture. Such calculations can be performed using the SAFT-VR or SAFT-g-SW EoS, but this work employs cubic EoS with UNIFAC activity coefficient models due to the wider availability of the models themselves and the data required to investigate a large number of different amines. Boiling point temperature: This temperature acts as a constraint in the selection of mixtures to avoid selecting amines that will evaporate at temperatures below the desorption conditions hence resulting in increased solvent losses. Although the calculation of the pure component boiling point temperature may be performed directly from GC methods (Papadopoulos et al., 2014), mixture boiling point calculations require the consideration of the VLE between the amines in a similar manner as in the case of vapour pressure. Density: Mixtures of low density are required as they support reduced solvent flowrates and equipment sizes. Density of mixtures can be calculated from an EoS, whereas it may be calculated using GC methods in the case of pure components (Papadopoulos et al., 2014). Viscosity and surface tension: Low mixture liquid viscosity and surface tension are desirable to enable improved mass transfer in the packing material and hence lower equipment volume. Activity coefficient models employing groups of UNIFAC type can be used for calculation of these two properties in the case of mixtures (Conte et al., 2011b; Cao et al., 1993) as well as different correlations. All these are analysed in the following sections. Melting point temperature: The calculation of this temperature is important to avoid solidification of the mixed solvent at absorption conditions. For the case of mixtures it is sufficient to simply ensure that the highest melting point temperature of the two components remains below the absorption temperature (Papadopoulos et al., 2014). Reactivity: In the case of single amines Papadopoulos et al. (2014, 2016) proposed the maximization of the amine basicity (pKa) as a quantitative index of the solvent ability to exhibit fast kinetics in the reaction with CO2. A potential option could be to use a linear mixing rule to provide a quantitative index for the reactivity of mixtures, however no evidence of this or of other ways of using pKa for mixtures has been observed in published literature. An empirical observation is therefore adopted from experimental work by Versteeg et al. (1996) who note that primary or secondary amines react faster than tertiary amines, whereas tertiary amines tend to have higher capacity for CO2. An exception exists here in the case of hindered primary and secondary amines (Sartori et al., 1987) which behave more like tertiary amines in terms of reaction rates and absorption capacities, especially in the case of severe hindrance (Couchaux et al., 2014). A qualitative empirical rule is therefore proposed which states that for the generation of CO2 capture solvent mixtures unhindered or slightly hindered primary and secondary amines should only be combined with tertiary amines or moderately and severely hindered primary and secondary amines. It is assumed that the issue of reactivity can be investigated quantitatively in a later stage.

Please cite this article in press as: Zarogiannis, T., et al., Systematic selection of amine mixtures as post-combustion CO2 capture solvent candidates, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.04.110

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2.2. Mixture screening methodology The properties considered as mixture screening criteria are implemented within a systematic methodology illustrated in Fig. 1. The methodology consists of 6 stages which aim to implement the criteria that need to be satisfied by the mixtures prior to their consideration as CO2 capture candidate components. The mixtures that satisfy these criteria after stage 5 are rank-ordered through a scaled index described in the next section in order to select the ones with the highest performance in all the criteria simultaneously. The proposed stages are implemented as follows: 1) In Stage 1 the initial pool of amines is separated into a group of primary or secondary amines and a group of tertiary amines. Binary combinations of amines between the two groups are only allowed. This stage serves to ensure that the developed mixtures are balanced in terms of absorption capacity and kinetics. Note that the group of primary and secondary amines considered in this work is not further separated with respect to steric hindrance. Only amines S14, S15 and S24 out of the 29 amines of Table 2 may exhibit steric hindrance based on the qualitative rules of Sartori et al. (1987) but it is not possible to ascertain whether it is low, moderate or high, as discussed above. 2) In Stage 2 the pairs that result from stage 1 are evaluated in terms of boiling and melting point temperatures to ensure that mixture evaporation and solidification is avoided at the desired absorption and desorption process temperatures (TAbs, TDes). 3) The identification of miscible binary co-solvents is established in stage 3 where two methods may be used; a) the HSP method which is easier to implement and calculate and b) the Gibbs free energy approach which requires the use of activity coefficient models. In this work, both methods are tested and it is found that both propose the same amine mixtures as options that satisfy the miscibility criterion.

4) Stage 4 involves the evaluation of solventesolventeCO2 solubility through the RED index. This is an important stage because mixtures are identified which are able to dissolve CO2. The RED values are used in stage 6 to rank-order the mixtures, although alternatively options that exhibit very low CO2 solubility compared to reference mixtures may be directly removed from the set. 5) In stage 5 the remaining properties are calculated for the mixtures; namely vapour pressure (Pvp), density (r), viscosity (n) and surface tension (s). The calculation is performed using different models for each property, including RED, so that the performance of the mixtures can be assessed using a systematic uncertainty quantification approach. 6) In stage 6 the mixtures are evaluated and selected through the combined consideration of uncertainty and a multi-criteria selection approach (Mavrou et al., 2015). The ones that are part of the generated Pareto fronts are rank-ordered through a scaled index that is calculated considering multiple different models for each property using the proposed uncertainty approach, in order to select the mixtures with the highest performance in all the criteria simultaneously. In all cases the Pareto optimal mixtures form a clear front which represents the mixture performance that needs to be sacrificed in one criterion in order to increase or reduce the mixture performance in another criterion (Papadopoulos and Linke, 2006a,b). The dominated mixtures represent inferior solutions simultaneously in all the considered performance criteria.

2.3. Evaluation of mixtures This section describes the index and the multi-criteria screening approach employed for the evaluation of the mixtures in stage 6. The proposed index J merges the properties under a unified criterion. Considering a set of mixtures G ¼ {1,…Ns} and a set of properties Pr ¼ {1,…Np}, the selection problem may be formulated as follows (Papadopoulos et al., 2014, 2016):

min Ji ¼ i2G

X

ai;j $x*i;j

(1)

j2Pr

where x*i;j represents the considered scaled property (e.g., r, RED, h, s, Pvp) for each mixture i, and ai,j represents a unity coefficient that is positive for properties that need to be minimized and negative for those to be maximized. Based on Eq. (1), the selection of mixtures with increased performance translates to the minimization of index Ji. Scaling gives equal importance to each property employed in Eq. (1). In this work it is realized through the following standardization method:

x*i;j ¼

xi;j  mG j n oNs  * G stj $max xi;j 

(2)

i¼1

Fig. 1. Mixture screening stages.

G where xi;j represents the original value of the property, mG j and stj represent the mean and standard deviation of the considered property, calculated over the entire set of mixtures G. Note that if it is necessary to prioritize specific properties (e.g. there is prior knowledge that specific properties are more important than others as mixture performance indicators) then it is possible to give different weights to properties though coefficient ai,j or by scaling the properties unevenly. The multi-criteria selection problem solved then includes the identification of the non-dominated mixtures by generation of a Pareto front per property j, considering the index J for all i2G against each one of the properties

Please cite this article in press as: Zarogiannis, T., et al., Systematic selection of amine mixtures as post-combustion CO2 capture solvent candidates, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.04.110

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Table 2 Solvents employed in the considered binary mixtures (CAS registry numbers and full names are reported in Appendix B) (Papadopoulos et al., 2016). ID

Short name

ID

Short name

S1

BEA

Molecular structure

S16

DPE

S2

DEEA

S17

ND1B

S3

DMMEA

S18

DBA

S4

EMEA

S19

HEXA

S5

MMEA

S20

3DMA1P

S6

MPA

S21

2A1PN

S7

2AP

S22

2A1B

S8

DEAB

S23

2A1H

S9

1M2P

S24

IPAE

S10

2P12P

S25

3DAP

S11

4AP

S26

4D1B

S12

1EDB

S27

PAE

S13

TMEDA

S28

4A2B

S14

DsBA

S29

5AP

S15

DIBA

e

e

represented through their values xi;j . This results in 5 plots of J against properties r, RED, h, s and Pvp enabling the identification of the impact that each property has on the index and revealing tradeoffs among the different mixtures in the Pareto fronts. Details

Molecular structure

e

regarding the implementation of a multi-criteria selection approach in the design and selection of solvents for separation systems can be found in Papadopoulos and Linke (2006a,b) and for reactive separation systems in Papadopoulos and Linke (2009).

Please cite this article in press as: Zarogiannis, T., et al., Systematic selection of amine mixtures as post-combustion CO2 capture solvent candidates, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.04.110

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2.4. Consideration of uncertainty The use of GC models or other empirical correlations involves uncertainty. This means that the use of two or more different models for the prediction of the same property may result in different values. If the screening is performed independently in two or more iterations using a different model for each property in every iteration then it will result in different selected mixtures hence it will be biased by the employed models. It is therefore important to address and avoid this issue at this early screening stage in order to increase the probability of obtaining mixtures which are good CO2 capture options regardless of the employed property prediction models. The issue of uncertainty has been previously addressed in CAMD-based methods using systematic approaches in applications including solvent design and selection (Kim and Diwekar, 2002; Xu and Diwekar, 2005) polymer design (Maranas, 1997), design and selection of solvents for optimum reaction rate constants (Folic et al., 2007) and design of mixtures used in systems transforming heat to power (Papadopoulos et al., 2013). An uncertainty quantification method for property predictions through GC was also recently proposed (Hukkerikar et al., 2012). In this work the aim of screening is to enable the fast identification of a rich set of mixtures with favourable trade-offs among the properties used as performance criteria, while avoiding options with obviously poor performance. Under this consideration, a systematic method is used to address uncertainty during the selection procedure. The method is adapted for mixtures from Papadopoulos et al. (2016) where it has been applied for the case of single amine solvents. The main idea is that each property used as a performance criterion is calculated through several different prediction models. Index Ji is then calculated for each solvent mixture for every possible combination of the employed models and all mixtures are rank-ordered as per utilized model combination. Highly performing mixtures are selected based on how frequently they rank in the top positions using different models for the calculation of Ji. Let each property j2Pr calculated using a set of N md

j from a total of Njmd models per available models Mj ¼ fmj;l gl¼1

property. Index Ji is now modified to Ji,k with k counting the number of combinations resulting from the selection of a single model of the general form mj,l in each iteration. For example, assume that RED is property j ¼ 1 and it may be calculated using 2 different models (i.e., N1md ¼ 2), namely m1,1 and m1,2. Also assume that Pvp is property j ¼ 2 and it may be calculated using 3 different models (i.e., N2md ¼ 3), namely m2,1, m2,2 and m2,3. Ji,k will result from the exhaustive enumeration of all Nc possible model combinations among all properties, equalling to:

Nc ¼

Y

Njmd

(3)

j2Pr

In the case of the example the number of combinations will be 6 hence for each mixture index Ji,k will receive 6 different potential values. Index Ji,k may therefore be represented as a matrix J of dimensions (Ns  Nc) with each model combination k (column of J) representing the performance of each mixture i (row of J). Each column of J is then ranked in ascending order implementing min Ji;k independently for every model combination k. In i2G other words, matrix J is now transformed to a matrix J* of independently rank-ordered columns. The rank of each solvent mixture within each column is then used to find the frequency of appearance of a mixture within a desired range (e.g. top 5 positions) for all Nc combinations. In the case of the example, index J will be calculated for all mixtures using first the combination of models m1,1 and m2,1 (this will be column 1 of matrix J), m1,1 and

m2,2 (this will be column 2 of matrix J), m1,2 and m2,1 (this will be column 3 of matrix J) and so forth until the 6th model combination (i.e., column 6 of matrix J) which will be the combination of models m1,2 and m2,3. Each one of the 6 columns will then be rank-order independently based on the Ji,k values to form matrix J*. In each column it is likely that the order of the mixtures will be different if the models employed for properties RED and Pvp of the example, or all properties in the general case, provide different predictions. The mixtures ranking with a higher frequency in the top positions are then selected. The multi-criteria screening approach discussed in the previous section is therefore applied on the selected mixtures that occupy the top positions using an average Ji, value from all models and an average property value in place of xi,j. A more detailed example with tabulated data is presented in Appendix A. Note that prior knowledge regarding the accuracy of some models can be incorporated in Eq. (1) through coefficient ai,j which could be used to give higher weightage to models of better accuracy as it is implemented for each model combination k. This procedure allows the identification and selection of mixtures within the top ranking positions regardless of the predictions provided by different models. Uncertainty is quantified as a distribution of mixture ranks resulting from the use of different predicted values for the same properties. The distribution of ranks is used as a sampling tool accounting for mixtures which may rank at the top with one property prediction model but may also rank lower with another. This allows for the refinement and reduction of the original data set without focussing only on the top options which may be biased due to uncertainty in the predictions. The frequent appearance of several mixtures at the top implies an agreement in the predictions made by multiple different models. On the other hand, even solvent mixtures of lower frequency at the top range may also be captured and considered. With the proposed method assumptions related to accuracy are avoided by considering both accurate and less accurate predictions in the selection stage. 3. Implementation 3.1. Investigated mixtures The mixtures considered in this work are derived from single amines previously identified as useful CO2 capture candidates. This is not a prerequisite for the proposed method which can be implemented for any set of available single solvents regardless of prior knowledge regarding their performance. Papadopoulos et al. (2014, 2016) identified 29 single amine solvents as potentially useful CO2 capture options considering numerous criteria associated with thermodynamics, reactivity and sustainability. These solvents are reported in Table 2 and they are used to develop mixtures by exhaustively generating all possible binary combinations (based on the constraint of stage 1 of the proposed method) at all concentrations from 10% to 90% of one solvent in the other with a step of 10%. In this respect, the term “mixture concentration” used in the elaboration of the results represents the amount of the first component in the binary mixture. The total number of unique combinations developed from Table 2 is 198 or 1782 when the entire concentration range is considered with the reported concentration change step. Furthermore, the set of generated mixtures is supplemented with 4 unique mixtures or 36 considering different concentrations (Table 3) which were investigated by Kumar (2013) as potentially useful CO2 capture options. After stage 4 of the proposed approach the total number of investigated mixtures was 202 or 1744 because some did not satisfy the temperature or solubility constraints.

Please cite this article in press as: Zarogiannis, T., et al., Systematic selection of amine mixtures as post-combustion CO2 capture solvent candidates, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.04.110

T. Zarogiannis et al. / Journal of Cleaner Production xxx (2016) 1e17

7

Table 3 Mixtures obtained from literature (Kumar, 2013) (CAS registry numbers and full names are reported in Appendix B). Mixture

Component 1

Component 2

MR1

MEA

MDEA

MR2

EMEA

MDEA

MR3

DEA

MDEA

MR4

MMEA

MDEA

Table 4 Different models utilized for each property. ID 1a 2a 3 4a 5 6a 7a 8 9a 10a 11 12 a b c

Model j used

r

Pvp

s

n

Njm

3

3

3

3

18

American Petroleum Institute (API, 1997) RKS (Soave, 1972) þ WS (Wong and Sandler, 1992) RKS (Soave, 1972)b þ Luo (Luo et al., 2007) Andrade (Aspen Plus®, 2013) Cao et al. (1993) MacLeodeSugden (Aspen Plus®, 2013) HakimeSteinbergeStiel (Aspen Plus®, 2013) Conte et al. (2011b) UNIFAC (Fredenslund et al., 1975) þ RKS (Soave, 1972) UNIFAC (Fredenslund et al., 1975) þ RKS (Soave, 1972) þ WS (Wong and Sandler, 1992) UNIFACc þ RKS (Soave, 1972)b þ Luo (Luo et al., 2007) HSP for mixtures (Durkee, 2007).

√ √ √ e e e e e e e e e

e e e e e e e e √ √ √ e

e e e e e √ √ √ e e e e

√ e e √ √ e e e e e e e

e e e e e e e e e e e √

RED

Implemented through Aspen Plus® (2013). Implemented based on Assael et al. (1996). Weidlich and Gmehling (1987) for hydroxylamines, Fredenslund et al. (1975) for all others.

3.2. Employed models Table 4 indicates the number of models ðNjmd Þ utilized to predict each property j required in the proposed uncertainty analysis approach and the corresponding models. The API Technical Data Book (1997) contains a collection of property prediction methods which are selected automatically by Aspen Plus® depending on the property selected by the user. In all properties except RED the number of models used in the calculations corresponds to the number of methods marked in Table 2. RED was calculated using the HSP model (Durkee, 2007) which obtains as input the solubility of the pure components in the mixture and the volume fractions. The pure component solubility was calculated once through Stefanis and Panayiotou (2008) and once through Hukkerikar et al. (2012). In each of these cases the molar volume was calculated using the density models (1), (2) and (3) from Table 4. The HSP model is a fraction of densities multiplied by volume fractions (Durkee, 2007). Different combinations of density models were used in the numerator and denominator of the faction hence the 18 models reported in Table 4 for RED. Experimental data are used as input for the pure component parameters required in all other cases. Based on Eq. (3) the total number of Ji;k values resulting from Table 4 is k ¼ 1458 for each mixture i (i.e., the total number of model combinations). The approach used in this work accounts for uncertainty in predictions to a reasonable extent since the amount of

the generated model combinations is high but more models can be considered for each property if available.

3.3. Evaluation of results and selection of mixtures The evaluation and selection of mixtures is organized in the presented results based on the following steps, which also represent the sub-sections of Section 4: 1 Multi-criteria assessment of mixture candidates 1.1 After the uncertainty analysis approach reported in Section 2, the mixtures that appear in the top 5 positions are selected. These include 85 unique mixtures or 214 including concentrations because some mixtures are repeated in several different concentrations 1.2 As noted in Section 2.3 the results obtained from the calculations are organized into 5 Pareto diagrams that indicate trade-offs between the index J which is minimized and properties Pvp, s, n and RED which are minimized, whereas r is maximized 2 Selection of highly performing mixtures The 214 mixtures distributed in the 5 Pareto diagrams are further refined based on their frequency of appearance. It is desired to select mixtures which:

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a. Appear in as many Pareto diagrams as possible because this indicates that the performance trade-offs between property and overall index values is favourable in many properties. b. Their cumulative frequency of appearance from diagram to diagram is as high as possible. c. Indicate a good balance between overall performance (index J values) and individual property performance (i.e. mixtures lying in the two extremes of the diagrams are not desired due to their strongly unfavourable performance in either the overall index or the investigated property). 3 Analysis of selected mixtures The mixtures selected in step 2 are further analysed and their performance is discussed in terms of their potential for use in chemisorption CO2 capture systems considering information from literature sources. 4 Comparison of selections against reference mixtures The performance of the mixtures of the refined set obtained from step 2 is then compared against: a. Selected pure components which appear often in the selected mixtures in order to investigate the performance improvement attained from the use of mixtures. b. Selected reference mixtures (Table 2) which exhibit favourable CO2 capture characteristics based on literature sources.

structure of the first component causes the density to increase, however the overall change in the index value is very small. This indicates that all other mixture properties except density change very little. It is also worth noting that M147 and M152 which share S25 also consist of di-sec butylamine (DsBA) and diisobutylamine (DIBA), respectively, which are very similar structures. Fig. 3 illustrates the trade-offs between RED and Ji;k . M20 (S18 þ S2), M173 (S18 þ S25) and M147 (S14 þ S25) are the mixtures on the Pareto front with M147 and M173 sharing S25 (3-diethylamino-propanol/3DAP). Molecule S2 (N,N-diethyl-2-aminoethanol/DEEA) of M20 has a structure which is only different by one carbon atom from S25 in the carbon chain between the tertiary amine and the hydroxyl group (see Table C.2). Also M173 and M20 both have S18 as the first component. These characteristics are clearly reflected in the trends of Fig. 3. M20 and M173 at concentrations of 60e70% in S18 are closely lumped at values of RED below 0.5. As the concentration in S25 increases above 50% in M173 the RED also increases until 90% in S25 which is observed in M147. Fig. 4 illustrates the trade-offs between viscosity and Ji;k . Based on the structures that participate in the mixtures as well as the scattering of the points in the figure there are two distinct areas which are affected by two different molecules. The first area is for very low viscosity but high index values (top left area of Fig. 4)

4. Results and discussion 4.1. Multi-criteria assessment of mixture candidates This section presents results from steps 1.1 and 1.2. Fig. 2 illustrates an optimum set of mixtures (Pareto front), highlighting the trade-offs between density and index Ji;k . In an order of increasing density the mixtures participating in the Pareto front (with their individual components included in brackets) are the following: M147 (S14 þ S25), M152 (S15 þ S25), M191 (S24 þ S25), M120 (S11 þ S25), M189 (S23 þ S25), M195 (S29 þ S25), M90 (S23 þ S8). Table C.1 of Appendix C illustrates all molecular structures that participate in the mixtures of the Pareto front. It is worth noting that S25 (3-diethyl-amino-propanol/3DAP) appears in 6 out of the 7 mixtures, whereas S8 (4-diethylamino-2-butanol/ DEAB) is structurally very similar to S25 and appears in the 7th mixture (i.e. M90). From M147 to M195 solvent S25 appears at 90% concentration in the mixture which indicates that the change in the

Fig. 2. Trade-offs between index Ji;k and density r; circular markers indicate that S25 (3-diethyl-amino-propanol/3DAP) appears in all these mixtures, red colour indicates that S23 (2-amino-1-hexanol/2A1H) is present in these mixtures, the percentages indicate the amount of the first component in the mixture, the grey dash markers indicate mixtures of suboptimal performance (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).

Fig. 3. Trade-offs between index Ji;k and RED; circular markers indicate that S25 (3diethyl-amino-propanol/3DAP) appears in these mixtures, the percentages indicate the amount of the first component in the mixture, the grey dash markers indicate mixtures of suboptimal performance.

Fig. 4. Pareto front between viscosity n and index Ji;k ; triangular markers indicate that S17 (N,N diethyl-1-butanamine/ND1B) appears in these mixtures, circular markers indicate that S25 (3-diethyl-amino-propanol/3DAP) appears in these mixtures, green colour indicates that S14 (di-sec butylamine/DsBA) appears in these mixtures, the percentages indicate the amount of the first component in the mixture, the grey dash markers indicate mixtures of suboptimal performance (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).

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where molecule S17 (N,N diethyl-1-butanamine/ND1B) appears at concentrations between 60% and 90%. Note that the second components in M145 and M163 are S14 and S18 which appear in concentrations below 20%. These two components appear in M173 and M147 at similar concentrations (i.e. below 20%) in the other end of the viscosity axis (i.e., right end of Fig. 4). In that end, M173 and M147 both consist of S25 at increasingly high concentrations. So the difference in the two ends of this figure is due to the existence of a hydroxyl group in S25 (3-diethyl-amino-propanol/3DAP), whereas S17 (N,N diethyl-1-butanamine/ND1B) is very similar to S25 with the only difference being the absence of the hydroxyl group. Furthermore, molecule S17 appears in all mixtures between M145 and M169. Details are reported in Table C.3. Fig. 5 illustrates the trade-offs between vapour pressure and Ji;k . M90 (S23 þ S8), M3 (S1þS8), M195 (S29 þ S25), M189 (S23 þ S25), M120 (S11 þ S25), M191 (S24 þ S25), M10 (S1þS25), M173 (S18 þ S25), M147 (S14 þ S25) appear in the Pareto front with S8 (4diethylamino-2-butanol/DEAB) included in the first two mixtures and the structurally similar S25 included in all others. Details are reported in Table C.4. Fig. 6 illustrates trade-offs between surface tension and Ji;k . The mixtures that appear in the Pareto front exhibit structural similarities with the mixtures observed in the case of viscosity. M145 (S14 þ S17) appears in the top left end together with M163 (S18 þ S17) and M8 (S1þS17). Mixture M18 (S14 þ S2) appears in the middle of Fig. 6 and then M173 (S18 þ S25) and M147 (S14 þ S25) appear toward the bottom right end of Fig. 6, with S2 (N,N diethyl-2-aminoethanol/DEEA) exhibiting structural similarity with S25 (3-diethyl-amino-propanol/3DAP). Details are reported in Table C.5.

9

Fig. 6. Trade-offs between index J and surface tension s; triangular markers indicate that S17 (N,N diethyl-1-butanamine/ND1B) appears in these mixtures, circular markers indicate that S25 (3-diethyl-amino-propanol/3DAP) appears in these mixtures, green colour indicates that S14 (di-sec butylamine/DsBA) appears in these mixtures, the percentages indicate the amount of the first component in the mixture, the grey dash markers indicate mixtures of suboptimal performance.

4.2. Selection of highly performing mixtures This section presents results based on step 2. The mixtures that appear in all Pareto diagrams are shown in the frequency diagram of Fig. 7. The total number of appearances of each mixture and the number of Pareto diagrams in which the mixtures are included are the first two criteria (based on steps 2a and 2b of Section 3.3) used to refine the initial mixture set. The cut-off point is set to 3, meaning

Fig. 7. Mixtures included in all Pareto diagrams and their frequency of appearance.

that desirable mixtures are the ones that appear at least 3 times in all Pareto diagrams (i.e. a mixture may appear twice in one diagram at different concentrations) or in at least 3 of them. The appearance of a mixture in 3 Pareto diagrams indicates that it exhibits improved behaviour in more than 3 desired properties, compared to the dominated mixtures. The mixtures that satisfy these criteria are M18 (S14 þ S2), M90 (S23 þ S8), M118 (S11 þ S17), M147 (S14 þ S25), M163 (S17 þ S18), M173 (S18 þ S25) and M195 (S29 þ S25). The set of mixtures identified based on Fig. 7 is further refined based on criterion 2c of Section 3.3 in order to select mixtures that exhibit balanced trade-offs between the overall index and the individual properties (i.e. to avoid mixtures lying in the extreme ends of the Pareto diagrams). Mixtures that satisfy this criterion are shown in Table 5 together with the corresponding concentrations. 4.3. Analysis of selected mixtures Fig. 5. Pareto front between vapour pressure Pvp and index Ji;k ; cross markers indicate that S8 (4dDiethylamino-2-butanol/DEAB) appears in these mixtures, circular markers indicate that S25 (3-diethyl-amino-propanol/3DAP) appears in these mixtures, red colour indicates that S23 (2-Amino-1-hexanol/2A1H) appears in these mixtures, aqua colour (M3, M10) indicates that S1 (2-(butylamino)ethanol/BEA) appears in these mixtures, the percentages indicate the amount of the first component in the mixture, the grey dash markers indicate mixtures of suboptimal performance (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).

This section presents a discussion of the results based on step 3. Fig. 8 shows the molecules comprising the selected mixtures based on the total number of appearances of each molecule and the number of Pareto diagrams in which the molecule is included in the overall results. It appears that S25, S18, S14 and S23 are the most influential molecules as they simultaneously appear in 3 or more Pareto diagrams and with a high total frequency.

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Table 5 Selected mixtures. Mixture ID

Component 1

Component 2

M90 (20%, 10%)

S23 (2A1H)

S8 (DEAB)

M173 (10%, 40%, 50%)

S18 (DBA)

S25 (3DAP)

M195 (10%, 20%)

S29 (5AP)

S25 (3DAP)

M118 (30%, 40%)

S11 (4AP)

S17 (ND1B)

M18 (50%, 60%, 70%)

S14 (DsBA)

S2 (DEEA)

Fig. 8. Molecules included in the proposed mixtures.

Among the mixtures of Table 4, M90 (S23 þ S8) appears to be one of the most interesting options due to the molecules that participate in it. S8 (4-diethylamino-2-butanol/DEAB) (Tontiwachwuthikul et al., 2011) was designed specifically to exhibit higher CO2 capacity, improved reaction rate and lower regeneration energy than MDEA (Sema et al., 2012). DEAB has a hydroxyl attached to the carbon chain three carbon atoms away from the amine group and this enables high CO2 absorption capacity (Yamada et al., 2013). S23 (2-amino-1hexanol/2A1H) is an interesting solvent because it has been experimentally observed to exhibit high stability and low corrosiveness with respect to carbon steel in mixtures with MDEA (Rooney, 2000). According to Rooney (2000) mixtures comprising a tertiary alkanolamine and a primary alkanolamine with a secondary carbon atom attached to the amino group (i.e. such as S23) exhibit unexpectedly low degradation and corrosiveness. So the replacement of MDEA with DEAB which is specifically known to perform better than MDEA in M90 is clearly promising and also indicates that the proposed approach is able to identify good CO2 capture mixture candidates. S25 (3-diethyl-amino-propanol/3DAP) is similar to S8 with the hydroxyl group in the same distance from the amine group but at the end of the molecule, not branched one carbon atom away from

the end as in S8. S25 also resembles S20 (3-dimethyl-amino-1propanol/3DMA1P) which has been shown to exhibit favourable reaction kinetics compared to MDEA (Kadiwala et al., 2012). S2 (N,N-diethyl-2-aminoethanol/DEEA) is also very similar with S25 and incorporates a hydroxyl group two carbon atoms away from the amine. According to Yamada et al. (2013) such structures also enable high absorption capacities, although lower than in structures such as S25 or S8. S2 is combined in M18 with S14 (di-sec butylamine/DsBA) which is a phase-change solvent (Zhang et al., 2012a). Such solvents exhibit liquideliquid phase separation which enables non-thermal extraction of a rich and a lean-CO2 phase prior to desorption. As a result, the reduction of the flowrate of the stream entering the desorption process and the much lower temperature than 120  C in which desorption may take place for some phase-change amines enable reduced energy consumption. S2 is also known to have very low heat of absorption at 40  C, at the same time it shows very high absorption capacity at 120  C indicating that less energy may be needed to reverse the chemical reaction between these amines and CO2 in the stripper (Kim and Svendsen, 2011). S14 exhibits a liquideliquid phase separation temperature in a 3 M aqueous solution at 60  C and over 95% regenerability at 80  C (Zhang et al., 2012b). S18 (di-N-butylamine/DBA) is also another phase-change amine which has been investigated by Zhang et al. (2012a). S11 (4-amino-pentanol/4AP) is similar to 5-amino-pentanol which was used in experiments reported in Singh et al. (2007, 2009a, b) and is expected to have higher absorption and desorption capacities because it contains four carbon atoms between the amine group instead of the five of 5-amino-pentanol. S17 (N,N diethyl-1-butanamine/ND1B) is similar to 3-(diethyl-amino)propanol, where the hydroxyl is replaced from a methyl group. S17 is expected to exhibit lower CO2 absorption capacity than S25 due to the absence of hydroxyl. Finally, S29 (5-amino-pentanol/5AP) has lower absorption and desorption capacities than hydroxylamines of three and four carbons atoms (Singh, 2011). It should be noted here that the main aim of the performed analysis is to identify the components which might be good CO2 capture mixture options. The concentration reported for each component in the mixture is indicative and may change when they are investigated in the presence of water and CO2.

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4.4. Comparison of selections against reference mixtures This section presents results based on step 4. Fig. 9 illustrates the performance of M90, M173 and M195 compared to reference mixtures 10% MR1, 20% MR2 and 90% MR4 which were selected as they exhibit better performance than other concentrations or combinations. The investigated mixtures appear in Fig. 9a to have better properties than 10% MR1 (MEA/MDEA) in surface tension, viscosity and CO2 solubility (RED). Their vapour pressure is very similar to 10% MR1, whereas 40% M173 is the only mixture showing slightly lower density. Fig. 9b shows a similar overall picture except for density where 20% MR2 (EMEA/MDEA) seems to be better. Finally, Fig. 9c shows that the selected mixtures perform considerably better than 90% MR4 (MMEA/MDEA) in density, viscosity and RED, whereas their surface tension and vapour pressure performance is practically similar in most cases. Fig. 10 compares the performance of different concentrations of M173 (S18 þ S25) with the case of using only S25 as a pure component. The results show that as S25 is reduced, the mixture surface tension, viscosity and RED increase, while density and vapour pressure also increase. This figure illustrates the trade-offs from using a mixture instead of a pure component. 5. Conclusions The presented work assessed the performance of mixtures that appear to be promising as CO2 capture solvents, considering both their composition and concentration. The proposed co-solvents were obtained in this work using mixture properties as performance criteria which enable a fast, efficient and eventually successful screening of the available database. A methodology for the evaluation of the aforementioned proposed models considering their uncertainty was established. The effect of model uncertainty

Fig. 10. Comparison of different concentrations of M173 (S18 þ S25) with pure S25; blue bars pointing right indicate improvement in performance compared to S25, red bars pointing left indicate deterioration in performance (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).

was investigated considering an initial inclusive set of mixtures which was systematically refined to few highly performing options. The proposed approach seems to result in useful mixture combinations which exhibit favourable performance as CO2 capture options. Key findings include the following:  Mixture M90 contains two components (4-diethylamino-2butanol and 2-amino-1-hexanol/DEAB and 2A1H) which have been verified experimentally to exhibit better performance than the well investigated CO2 capture solvent MDEA, either in mixtures or as pure components. 2-Amino-1-hexanol (2A1H) exhibits improved CO2 capture performance in mixtures with MDEA while exhibiting reduced corrosiveness and degradation. 4-Diethylamino-2-butanol (DEAB) has shown improved overall CO2 capture performance compared to MDEA. In this respect, the replacement of MDEA by 4-diethylamino-2-butanol (DEAB) in mixtures with 2-amino-1-hexanol (2A1H) as indicated by the proposed approach is clearly a good choice.  Molecules di-sec butylamine (DsBA) and di-n-butylamine (DBA) are known phase-change solvents which enable very low regeneration energy requirements. The use of these and other such amines in mixtures is definitely worth investigating.  Mixtures M173 (DBA and 3DAP) and M195 (5AP and 3DAP) exhibit favourable performance in most considered properties compared to reference mixtures MEA/MDEA, EMEA/MDEA and MME/MDEA. Furthermore, the other identified mixtures also incorporate components which have attracted research attention as single amine options and appear to be promising. Overall, the proposed method intends to support the screening of mixtures through a hierarchical selection approach which remains independent of the models used in this work. In case that additional, different models are available they can be used in the same way. Nomenclature AEEA AEPD AMP EAE EDA EoS G HSP J

Fig. 9. Performance of selected mixtures against mixtures a) 10% MR1 (MEA/MDEA), b) 20% MR2 (EMEA/MDEA) and c) 90% MR4 (MMEA/MDEA). Blue bars pointing right indicate improvement in performance compared to MR1, MR2 and MR3, red bars pointing left indicate deterioration in performance (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).

J*

N-(2-aminoethyl)ethanolamine 2-amino-2-ethyl-1,3-propanediol 2-amino-2-methyl-1-propanol N-ethyl-ethanolamine ethylenediamine equation of state set of mixtures Hansen solubility parameter matrix of dimensions (Ns  Nc) containing values of index Ji,k matrix of dimensions (Ns  Nc) containing values of index Ji,k in an ascending order, in independently rank-ordered columns

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T. Zarogiannis et al. / Journal of Cleaner Production xxx (2016) 1e17

Indices i j k l

Ji performance index J for mixture i MAE 2-(methylamino)-ethanol MAPA 3-methylaminopropylamine mj set of prediction models for property j mj,l model l used for the calculation of property j n viscosity (cp) Nc total number of model combinations Njmd total number of prediction models for property j Np total number of properties Ns total number of mixtures PCP-SAFTpurturbed-chain polar SAFT pKa amine basicity Pr set of properties Pvp vapour pressure (Pa) PZ piperazine RED relative energy difference SAFT-VR statistical associating fluid theory for potentials of variable range SAFT-g-SW SAFT for square well potentials stjG standard deviation of property j overall mixtures in set G TAbs temperature in absorption column (K) Tbp boiling point TDes temperature in desorption column (K) Tm melting point TMBPA bis-(3-dimethylaminopropanol) VLE vapoureliquid equilibrium x*i;j scaled value of property j for mixture i xi;j original value of property j for mixture i

mixture property model combination number model

Appendix A. Example of implementation of uncertainty approach Tables A.1eA.3 provide data input and results as an example of the presented uncertainty quantification approach. Five mixtures are selected randomly and evaluated considering viscosity (n) and surface tension (s) as the selection criteria. Table A.1 shows the predictions obtained for each mixture using the proposed models. The predictions obtained by each model are close but clearly different. Table A.2 shows the calculated index value (Ji,k) for each combination of models. Table A.3 shows the ordering of the mixtures based on the obtained index values. The differences in the ordering of the mixtures are small in this case due to the limited number of data, however they are sufficient to illustrate the proposed concept. Notice that 50% M18 ranks 4th in the first 3 model combinations, it then ranks 3rd in the next two combinations and then goes back to being 4th. In all cases it swaps positions with 80% M173. If it was desired to select the mixtures ranking in the top 3 positions then 50% M18 would only appear twice after the 3rd model combination, whereas 10% M4 and 80% M104 appears 6 times. Also notice that there are 4 mixtures that appear in the top 3 positions. This shows how the proposed approach takes into account the uncertainty in the model predictions during the selection procedure and provides an opportunity to consider mixtures for which the different predictions affect their overall performance and ranking.

Greek symbols ai,j coefficient used in Eq. (1) mGj mean of property j overall mixtures in set G r density (kg/m3) s surface tension (dyne/cm)

Table A.1 Some mixtures and their properties considering 2 models for viscosity and 3 models for surface tension. Mixtures

10% 50% 80% 80% 90%

M4 M18 M104 M173 M195

n1

n2

s1

s2

s3

Andrade (Aspen Plus®, 2013)

Cao et al. (1993)

HakimeSteibeckeStiel (Aspen Plus®, 2013)

MacLeodeSugden (Aspen Plus®, 2013)

Conte et al. (2011b)

0.41 1.20 0.40 0.73 0.81

0.82 1.00 0.64 0.76 3.00

22 23 20 25 36

21 22 20 25 35

21 24 21 24 31

Table A.2 Values for index Ji,k considering all potential (6) model combinations. Mixtures

Ji,1 (n1 þ s1)

Ji,2 (n1 þ s2)

Ji,3 (n1 þ s3)

Ji,4 (n2 þ s1)

Ji,5 (n2 þ s2)

Ji,6 (n2 þ s3)

10% 50% 80% 80% 90%

1.39 1.11 1.74 0.01 2.01

1.45 1.09 1.72 0.09 1.99

1.61 1.30 1.75 0.11 1.95

0.91 0.61 1.43 0.53 3.48

0.97 0.63 1.40 0.45 3.47

1.13 0.42 1.45 0.43 3.43

M4 M18 M104 M173 M195

Table A.3 Rank-ordering of mixtures based on the index values of Table A.2. Mixtures

Ranks 1 (n1 þ s1)

Ranks 2 (n1 þ s2)

Ranks 3 (n1 þ s3)

Ranks 4 (n2 þ s1)

Ranks 5 (n2 þ s2)

Ranks 6 (n2 þ s3)

10% 50% 80% 80% 90%

2 4 1 3 5

2 4 1 3 5

2 4 1 3 5

2 3 1 4 5

2 3 1 4 5

2 4 1 3 5

M4 M18 M104 M173 M195

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13

B. Chemical names and CAS registry number of molecules considered for the generation of mixtures

Table B.1 Names and CAS registry numbers of employed molecules. ID

Name

Abbrev.

CAS number

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24 S25 S26 S27 S28 S29

2-(Butylamino)ethanol N,N-Diethyl-2-aminoethanol N,N-Dimethylaminoethanol Ethylaminoethanol 2-(Methylamino)-ethanol 3-Amino-1-propanol 2-(Amino)-propanol 4-Diethylamino-2-butanol 1-Methylamino-propan-2-ol N,N,N0 ,N0 ,2-Pentamethyl-1,2-propanediamine 4-Amino-pentanol 1-Ethyl-N,N-dimethylbutylamine N,N,N0 ,N0 -Tetramethyl-1,2-ethanediamine Di-sec butylamine Diisobutylamine Di-N-Propylethylamine N,N Diethyl-1-butanamine Di-N-butylamine Hexanamine 3-(Dimethylamino)-1-propanol 2-Amino-1-pentanol 2-Amino-1-butanol 2-Amino-1-hexanol 2-(Isopropylamino)-ethanol 3-(Diethyl-amino)-propanol 4-(Dimethylamino)-1-butanol 2-Propylamino-ethanol 4-Amino-2-butanol 5-Amino-pentanol

BEA DEEA DMMEA EMEA MMEA MPA 2AP DEAB 1M2P 2P12P 4AP 1EDB TMEDA DsBA DIBA DPE ND1B DBA HEXA 3DMA1P 2A1PN 2A1B 2A1H IPAE 3DAP 4D1B PAE 4A2B 5AP

111-75-1 100-37-8 108-01-0 110-73-6 109-83-1 156-87-6 6168-72-5 5467-48-1 16667-45-1 68367-53-3 927-55-9 24552-03-2 110-18-9 110-96-3 626-23-3 20634-92-8 4444-68-2 111-92-2 111-26-2 3179-63-3 4146-04-7 96-20-8 5665-74-7 109-56-8 622-93-5 13330-96-6 16369-21-4 39884-48-5 2508-29-4

Table B.2 Names and CAS registry numbers of molecules in the reference mixtures. ID

Name

Abbrev.

CAS number

R1 R2 R3 R4 R5

Monoethanolamine Methyldiethanolamine Diethanolamine Ethylaminoethanol 2-(Methylamino)-ethanol

MEA MDEA DEA EMEA MMEA

141-43-5 105-59-9 111-42-2 110-73-6 109-83-1

C. Mixtures identified in the Pareto fronts for each selection criterion.

Table C.1 Mixtures in Pareto front of index against density. Density ID

Component 1

Component 2

M147

S14 (DsBA)

S25 (3DAP)

M152

S15 (DIBA)

S25 (3DAP)

M191

S24 (IPAE)

S25 (3DAP)

(continued on next page)

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T. Zarogiannis et al. / Journal of Cleaner Production xxx (2016) 1e17

Table C.1 (continued ) Density ID

Component 1

Component 2

M120

S11 (4AP)

S25 (3DAP)

M189

S23 (2A1H)

S25 (3DAP)

M195

S29 (5AP)

S25 (3DAP)

M90

S23 (2A1H)

S8 (DEAB)

Table C.2 Mixtures in Pareto front of index against RED. RED ID

Component 1

Component 2

M20

S18 (DBA)

S2 (DEEA)

M173

S18 (DBA)

S25 (3DAP)

M147

S14 (DsBA)

S25 (3DAP)

Table C.3 Mixtures in Pareto front of index against viscosity. Viscosity Mixture

Component 1

Component 2

M145

S14 (DsBA)

S17 (ND1B)

M163

S18 (DBA)

S17 (ND1B)

M8

S1 (BEA)

S17 (ND1B)

M118

S11 (4AP)

S17 (ND1B)

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T. Zarogiannis et al. / Journal of Cleaner Production xxx (2016) 1e17

15

Table C.3 (continued ) Viscosity Mixture

Component 1

Component 2

M169

S27 (PAE)

S17 (ND1B)

M173

S18 (DBA)

S25 (3DAP)

M147

S14 (DsBA)

S25 (3DAP)

Table C.4 Mixtures in Pareto front of index against vapour pressure. Vapour pressure ID

Component 1

Component 2

M90

S23 (2A1H)

S8 (DEAB)

M3

S1 (BEA)

S8 (DEAB)

M195

S29 (5AP)

S25 (3DAP)

M189

S23 (2A1H)

S25 (3DAP)

M120

S11 (4AP)

S25 (3DAP)

M191

S24 (IPAE)

S25 (3DAP)

M10

S1 (BEA)

S25 (3DAP)

M173

S18 (DBA)

S25 (3DAP)

M147

S14 (DsBA)

S25 (3DAP)

Please cite this article in press as: Zarogiannis, T., et al., Systematic selection of amine mixtures as post-combustion CO2 capture solvent candidates, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.04.110

16

T. Zarogiannis et al. / Journal of Cleaner Production xxx (2016) 1e17

Table C.5 Mixtures in Pareto front of index against surface tension. Surface tension Component 1

Component 2

M145

S14 (DsBA)

S17 (ND1B)

M163

S18 (DBA)

S17 (ND1B)

M8

S1 (BEA)

S17 (ND1B)

M18

S14 (DsBA)

S2 (DEEA)

M173

S18 (DBA)

S25 (3DAP)

M147

S14 (DsBA)

S25 (3DAP)

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