Systematic selection of mixtures as postcombustion

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1 ARISTOTLE UNIVERSITY of THESSALONIKI Systematic selection of mixtures as postcombustion CO 2 capture solvent candidates T. Zarogiannis, A. I. Papadopoulos, P. Seferlis Department of Mechanical Engineering of (AUTh) and Chemical Process and Energy Resources Institute (CPERI) Center for Research and Technology Hellas (CERTH)

2 CAPSOL (capsol-project.eu) A project funded by the European Commission. Design Technologies for Multi-scale Innovation and Integration in Post-Combustion CO 2 Capture: From Molecules to Unit Operations and Integrated Plants. Addresses post combustion CO 2 capture using solvent-based absorption/desorption systems. Research Academic Partners Engineering Companies Industrial Partners

Cons: Lean stream 40% cost penalties to plant operation. 70% due to solvent regeneration.

3 Solvent-based CO 2 capture CO 2 Monoethanolamine(MEA) Rich stream Flue gas Pros: Well-established technology. Mild conditions, conventional equipment. Easily retrofitted onto existing energy plants. Cons: Lean stream 40% cost penalties to plant operation. 70% due to solvent regeneration. Toxic solvent, corrosion, degradation. MEA High stripping energy. High heat of absorption. Solvent vapor losses due to high vapor pressure. There is a need to seek alternative solvents.

4 What kind of solvents? The majority of existing published research focuses on aqueous solutions of single amines, e.g.: 2-amino-2-methyl-1-propanol (AMP) enables reduction in the regeneration energy to 3.4GJ/ton CO2 from 4.1GJ/ton CO 2 of the standard 30% MEA (Harbou et al., 2013) AMP has slow kinetics hence requiring a large absorption column Bis-(3-dimethylaminopropyl) (TMBPA) has 1 secondary and 2 tertiary amine sites exhibiting a loading of 3 mole CO 2 /mole amine (Aronu et al., 2010) TMBPA has high viscosity with detrimental effects on the mass transfer Harbou I., Mangalapally H.P., Hasse H., 2013, Int J Greenh Gas Cont 18, Aronu U.E., Svendsen H.F., Hoff K.A., 2010, Proceedings of Distillation Absorption 2010, Editors: de Haan A.B, Kooijman H., Górak A.,

5 Why look for mixtures? Mixtures combine favorable properties and may diminish unfavorable behavior (e.g., improve CO 2 loading or kinetics) Amine EAE Mix 1 (EAE+MDEA) Mix 2 (EAE+AMP) Higher loading EAE: N-ethyl-ethanolamine, secondary amine-faster kinetics than MDEA, AMP MDEA: Methyldiethanolamine, tertiary amine-high CO 2 absorption capacity Kumar G., 2013, PhD Thesis, National Institute of Technology, India, <

6 Current state-of-the-art Solvent mixtures The selection of amines in mixtures for CO 2 capture is mainly performed empirically. A small number of solvents is evaluated. Extensive use of experiments is required. The use of systematic computational methods is important: Allows the evaluation of a large number of candidate solvent mixtures (composition and concentration range). Enables the simulation of non-ideal mixture properties and characteristics using reliable and accurate models. Utilize the experience acquired for the selection of aqueous single amines 1,2. There are no systematic methods for the evaluation and selection of aqueous amine mixtures for CO 2 capture. 2 Papadokonstantakis S., Badr S., Hugerbuhler K., Papadopoulos A.I.,Damartzis T., Seferlis P., Forte E., Chremos A., Galindo A., Adjiman C.S., Jackson G.,, Computer Aided Chemical Engineering 36, 2015,

7 Challenges in solvent mixture selection Highly non-ideal mixture behavior, in particular in combination with water and CO 2. Solvent composition and extensive concentration range under investigation increase dramatically the number of combinations. Need to evaluate multiple properties as selection criteria for the mixtures.

8 Proposed approach Goal: Preliminary assessment of a very large number of mixtures in order to select a few promising mixtures based on desired properties, which can then be assessed accurately by models or experiments. Pool of mixture candidates Determination of selection criteria What kind of criteria? What kind of models? Calculation of selection criteria What kind of method? Assessment and selection of mixtures

9 Selection criteria depend on available models Ideally the selection criteria should be based on Mixture-CO 2 -H 2 O behavior Existing models: Focus on very few mixtures for which they were developed They can t be used for other mixtures Generic models exist (e.g. SAFT or enrtl)- Data are available for very few mixtures We intend to use models which: Can provide predictions for different molecular structures Are widely available in commercial software (e.g. ASPEN) Solvent-solvent interactions Solvent-solvent- CO2 interactions Solvent-solvent- H2O interactions

10 Nature of models-group contribution approach Molecular groups e.g. -CH 2 -, OH, -CH 3, -CF 3 -COOH etc. Evaluation of chemical feasibility Molecule e.g. CH 3 -CH 2 -NH 2 Property calculation (GC) Activity coefficient models Equations of state Contribution of functional group to calculate molecular property: Result from experimental data fitted to a model Contribution of a functional group is transferable to different molecules Calculations are fast for both pure component and mixture properties Sufficient to identify few molecular structures of desired properties which can then be investigated through computational chemistry models, EoS or experiments Wide range of molecules may be investigated

11 Selection criteria Solvent property Solubility parameter (δ t -RED) (min) Vapour pressure (P vp ) (min) Density (ρ-v m ) (max) Surface tension (σ) (min) Viscosity (v) (min) Boiling point (T bp ) Melting point (T m ) Impact on absorption/desorption process Ability to dissolve CO 2 / All design & operating parameters Solvent losses Equipment size-capacity Mass transfer coefficients-packing material characteristics Mass transfer coefficients-packing material characteristics Solvent evaporation losses Solvent solidification

12 Models for each property Properties Solubility- Relative Energy Difference (RED) Mixture vapour pressure (P vp ) Density (ρ) Viscosity (n) Surface tension (σ) Pure components GC GC GC GC GC Methods Mixtures Quick screening through Hansen Solubility Parameter Detailed screening ΔG mix /RT VLE (EοS) Equations of state (e.g. cubic) Activity coefficient models Activity coefficient models Mixture melting point (T m ) Mixture boiling point (T bp ) GC Max(T m1,t m2 ) GC VLE (EοS)

13 Methodology Component 1 Primary or secondary amines Separation into groups Binary mixtures Temperature constraints Component 2 Tertiary amines Boiling point (Tbp) of binary mixture Melting point (Tm) of binary mixture Determination of Gibbs free energy of mixing Candidate solvents with Tbp > Tdes and Τm < Tabs Solubility calculation Solvent-solvent solubility (HSP) Immiscible mixtures are removed

14 Methodology Solubility through the RED (Hansen Solubility Parameter) Solvent-solvent-CO 2 solubility Co-solvents dissolving CO2 Properties calculation Vapor pressure (P vp ) Density (p) Viscosity (v) Surface tension (σ) Candidate solvents Mixture evaluation Multi-criteria selection approach

15 Multi-criteria evaluation problem Assuming a set of mixtures G={1,,Ns} and a set of properties Pr={ρ, σ, μ, P vp, RED} the selection problem may be formulated mathematically as follows: max min P,,, RED s.t. vp TT m TT bp Abs Des ig i x i,j : considered scaled property {ρ, σ, μ, P vp, RED} α i,j : represents a unity coefficient (positive for minimization and negative for maximization) Performance trade-offs between the properties. Facilitate the search for multiple solutions. minj ax ij, ij, j P r f 1 Pareto front f 2 J i Pareto front {ρ,σ,μ, P vp, RED}

16 Case study Mixture S4 EMEA selection S19 HEXA S5 MMEA S20 3DMA1P ID Short name Molecular Structure ID Short name Molecular Structure S1 BEA S16 DPE S6 MPA S21 2A1PN S2 DEEA S17 ND1B S7 2AP S22 2A1B S3 DMMEA S18 DBA S8 DEAB S23 2A1H S4 EMEA S19 HEXA S9 1M2P S24 IPAE S5 MMEA S20 3DMA1P S10 2P12P S25 3DAP S6 MPA S21 2A1PN S11 4AP S26 4D1B S7 2AP S22 2A1B A pool of 26 aliphatic, acyclic amines The amines were identified as optimum CO 2 capture candidates using a systematic approach 1 S8 DEAB S23 2A1H S9 1M2P S24 IPAE 198 binary mixtures or 1782 at concentrations of S10 2P12P S25 3DAP 1 A.I. Papadopoulos, S. Badr, A. Chremos, E. Forte, T. Zarogiannis, P. Seferlis, S. Papadokonstantakis, C.S. Adjiman, A. Galindo, G. Jackson, 2016, Molecular Systems Design and Engineering, 1, S11 4AP S26 4D1B

17 Pareto optimal front Density vs Index J S25 (3DAP) S8 (DEAB) S25 appears in 6 out of the 7 mixtures S8 (4-Diethylamino-2-butanol/DEAB) is structurally very similar to S25 and appears in the 7th mixture (i.e. M90) Notice that the change in the density increases significantly from left-to-right but the overall performance index changes very little. 17

18 Pareto optimal front Solubility vs Index J S25 (3DAP) S2 (DEEA) S18 (DBA) S25 appears in 2 out of the 3 mixtures S2 (N,N-diethyl-2-amino-ethanol/DEEA) is structurally very similar to S25 and appears in the mixture M20 S18 is the first component in M173 and M20 18

19 Pareto optimal front Viscosity and Surface Tension vs Index J S17 (ND1B) S25 (3DAP) S14 (DsBA) S17 (N,N diethyl-1-butanamine/nd1b) Appears in all mixtures between M145 and M169 (triangles) I structurally very similar to S25 M173 and M147 (circles) both consist of S25 at increasingly high concentrations In Surface tension S14 appears in all 3 areas 19

20 Pareto optimal front Vapour Pressure vs Index J S25 (3DAP) S8 (DEAB) S25 appears in 7 out of the 9 mixtures S8 (4-Diethylamino-2-butanol/DEAB) is structurally very similar to S25 and appears in the first two mixtures (i.e. M90, M3, cross marks) 20

21 Selection of highly performing mixtures The 198 mixtures distributed in the 5 Pareto diagrams are further refined based on their frequency of appearance. It is desired to select mixtures which: 1) Appear in as many Pareto diagrams as possible because this indicates that the performance trade-offs between property and overall index values is favorable in many properties. 2) Their cumulative frequency of appearance from diagram to diagram is as high as possible. 3) 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 unfavorable performance in either the overall index or the investigated property). 21

22 Selection of highly performing mixtures At extreme points of Pareto fronts, mixtures not selected Mixture ID Component 1 Component 2 M90 (20%, 10%) S23 (2A1H) S8 (DEAB) M173 S18 S25 (10%, 40%, 50%) (DBA) (3DAP) We already visually highlighted components S25 S8 S2 S18 S17 S14 M195 (10%, 20%) M118 (30%, 40%) M18 (50%, 60%, 70%) S29 (5AP) S11 (4AP) S14 (DsBA) S25 (3DAP) S17 (ND1B) S2 (DEEA) 22

23 Comparison against reference mixtures The performance of the mixtures of is compared against selected reference mixtures which exhibit favorable CO 2 capture characteristics based on literature sources. MEA MDEA EMEA MDEA MMEA MDEA 23

24 Are really the selected mixtures useful? Mixture M90 contains two components: 4-Diethylamino-2-butanol/DEAB and 2-Amino-1-hexanol/2A1H In published literature they have both been shown to be better than MDEA either in mixtures or as pure components 2A1H with MDEA has also been shown to enable lower corrosion and degradation The replacement of MDEA by DEAB in mixtures with 2A1H is clearly a good choice. Di-sec butylamine (DsBA in M18) and Di-n-butilamine (DBA in M173) are known phase-change solvents which enable very low regeneration energy requirements. Mixtures M173 (DBA and 3DAP) and M195 (5AP and 3DAP) exhibit favorable performance in most considered properties compared to reference mixtures MEA/MDEA, EMEA/MDEA and MME/MDEA 24

25 Conclusions Mixtures of high performance considering both their composition and concentration. A fast, efficient, and eventually reliable screening of numerous molecules and mixture concentrations. Mixture: o M90 (4-Diethylamino-2-butanol/DEAB and 2-Amino-1- hexanol/2a1h) Worth of further investigation through experiments. T. Zarogiannis, A. I. Papadopoulos, P. Seferlis, 2016, Systematic selection of amine mixtures as post-combustion CO 2 capture solvent candidates, Journal of Cleaner Production,136,

26 Acknowledgments The research leading to these results has received funding from the European Commission under grant FP7-ENERGY CAPSOL. Thank you for your attention!

Computer-aided Molecular Design of Phase-Change CO 2 Capture Solvents considering Thermodynamics, Reactivity and Sustainability

Computer-aided Molecular Design of Phase-Change CO 2 Capture Solvents considering Thermodynamics, Reactivity and Sustainability A.I. Papadopoulos, P. Seferlis Centre for Research and Technology Hellas