Presenter: Xinyan Liua,b

Supervisor: Xiaodong Lianga; Georgios Kontogeorgisa; Rafiqul Gania

Xiangping Zhangb; Suojiang Zhangb

a Department of Chemical and Biochemical Engineering, DTU, Lyngby, Denmark b State Key Laboratory of Multiphase Complex System, Beijing Key Laboratory of Ionic Liquids Clean

Process, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, China

Energy Efficient Hybrid Gas Separation with Ionic Liquid

1

• Background – Gas separation processes – Overview of traditional separation technologies – Solution proposal & objective

• Framework – Three-stage methodology – Multi-scale concept

• Database & model library • Case study • Conclusion and future work

Outline

2

Background

3

Petroleum C5+ C4 C3 C2 CH4…

Purification

Chemical Production

Gas

Separation

Processes

Decarbonization Desulfuration Denitrification …

Biomass Gasification Methanol to Olefin …

Gas Separation Processes

4

Find alternative technology

Novel solvent for absorption

Distillation ✘High energy consumption

Non-volatile

Stability

Designable

Adjustable

Large capacity

Good recycle

Low energy

Widely application

Advantages Feature

Ionic Liquid

Membrane ✘Lower purity of product, low flow

Solvent absorption

✘Chemical: High energy, toxicity

✘Physical: Lower recovery

Gas separation with ionic liquid is a new way to be energy efficient and environmentally friendly.

Overview of traditional separation technologies

5

Natural gas Light hydrocarbon gas mixture

Method Application Advantages and disadvantages

Acid gas removal (CO2,H2S…)

Synthesis gas separation (H2…) Acid gas removal

✔Flexible and high purity

✔Economical, energy-saving

✔Mature, widely used

Adsorption

• Propose a systematic method for ILs screening for various kinds of raw gas, then develop the most efficient hybrid gas separation process.

• High solubility • High selectivity • Low viscosity, easy for industrialization

Problem： 1. How to find out the promising IL

2. How to separate any gas from gas mixtures for high energy efficiency, low environmental impact and increased profit.

Objective：

6

Solution proposal & objective For gas mixtures (>2) separation system

Hybrid gas separation processes (use technologies where they are most efficient)

IL-based absorption Traditional technologies: distillation or membrane

Combine with

Framework

7

Objective

Research

Application

Establish IL-based hybrid gas separation processes for various kinds of gas mixtures (Natural gas or shale gas…)

Stage 1:ILs screening Problem definition Screen ILs based on solubility and selectivity. Gas mixture analysis Screen IILs based on physical properties

Stage 2:Process design

Generate all possible flowsheets. Preliminary evaluation. Select flowsheet.

Apply a screening method to determine an environmentally friendly and economical hybrid gas separation scheme with low energy consumption

Stage 3:Process simulation and evaluation

Rigorous IL-based hybrid gas separation process simulation. Energy consumption and economical cost evaluation of the process. Compare this IL-based gas separation with conventional process.

8

Three-stage methodology

Objective

Research

Application

Establish IL-based hybrid gas separation process for various kinds of gas mixtures (Natural gas or shale gas…)

Stage 1:ILs screening Problem definition Screen ILs based on solubility and selectivity. Gas mixture analysis Screen IILs based on physical properties

Stage 2:Process design

Generate all possible flowsheets. Preliminary evaluation. Select flowsheet.

Apply a screening method to determine an environmentally friendly and economical hybrid gas separation scheme with low energy consumption

Stage 3:Process simulation and evaluation

Rigorous IL-based hybrid gas separation process simulation. Energy consumption and economical cost evaluation of the process. Compare this IL-based gas separation with conventional process.

9

Three-stage methodology

Multi-scale

Macro scale

Model

Process Design

Model data (COSMO-RS)

Experimental data Gas solubility data in ILs Henry constant of gas in ILs Gas selectivity data in ILs Property data of pure ILs

Henry constant data prediction

IL property data prediction Density Viscosity Surface tension Vapor pressure

IL-based absorption

Distillation

Membrane separation

Absorption …

Combine to hybrid gas separation process

10

Multi-scale concept

Database

11

Database & Model library

CO2 CO CH4 H2 C2H6 C2H4

C3H8 C4H10

N2 O2

N2O C4H8 C3H6

C2H2 NH3

NO2 SO2

COS H2S

12

Database establishment Gas component Measured solubility database

Number of gas

Number of ILs

Number of data

16 260 13875

CO2 CO CH4 H2 C2H6 C2H4

C3H8 C4H10

N2 O2

N2O C4H8 C3H6

C2H2 NH3

NO2 SO2

COS H2S

13

Database establishment Gas component Measured solubility database

Number of gas

Number of ILs

Number of data

16 260 13875

Number of gas

Number of ILs

Number of data

11 110 516

Measured Henry constant database

CO2 CO CH4 H2 C2H6 C2H4

C3H8 C4H10

N2 O2

N2O C4H8 C3H6

C2H2 NH3

NO2 SO2

COS H2S

14

Database establishment Gas component Measured solubility database

Number of gas

Number of ILs

Number of data

16 260 13875

Number of gas

Number of ILs

Number of data

11 110 516

Measured Henry constant database

Number of gas

Number of ILs

16 13585

Predicted Henry constant database (COSMO-RS)

CO2 CO CH4 H2 C2H6 C2H4

C3H8 C4H10

N2 O2

N2O C4H8 C3H6

C2H2 NH3

NO2 SO2

COS H2S

15

Database establishment Gas component Measured solubility database

Number of gas

Number of ILs

Number of data

16 260 13875

Number of gas

Number of ILs

Number of data

11 110 516

Measured Henry constant database

Number of gas

Number of ILs

16 13585

Predicted Henry constant database (COSMO-RS)

Merge into

Database

CO2 CO CH4 H2 C2H6 C2H4

C3H8 C4H10

N2 O2

N2O C4H8 C3H6

C2H2 NH3

NO2 SO2

COS H2S

16

Database establishment Gas component Measured solubility database

Number of gas

Number of ILs

Number of data

16 260 13875

Number of gas

Number of ILs

Number of data

11 110 516

Measured Henry constant database

Number of gas

Number of ILs

16 13585

Predicted Henry constant database (COSMO-RS)

Merge into

Database

209 cations

65 anions

19 gases

17

Database establishment Sufficient data can be retrieved from COSMO-RS.

Need a correction for CH4 , the same situation occurs for C2H4, C3H8.

(Take the common gases such as CO2 and light hydrocarbon gases as example)

the comparison between experiment and COSMO-RS

CO2 CH4

18

Model library establishment (Quantitative model)

Engineering model for Henry’s constant of CO2

The temperature dependence of the Henry’s constants was described by the following equation:

H exp[ / ]a b T= +

Based on sufficient experimental data, a model for Henry’s constant of CO2 in imidazolium-based ILs with three kinds of anions is established. The value of a and b can be fitted with carbon atom number on the alkyl chain: (a and b are calculated from group contribution method)

Eq.(1)

Model

[Cnmim][Tf2N] Eq.(2)

[Cnmim][BF4]

Eq.(3)

[Cnmim][PF6]

Eq.(4)

1373.335 149.075exp[8.835 0.273 ]CnH CnT

− −= + +

3082.425+180.795exp[14.977 0.733 ]CnH CnT

−= − +

2064.244+85.694exp[11.108 0.352 ]CnH CnT

−= − +

Comparison result for Henry’s constant of CO2 (Quantitative)

The AARD for predicted Henry’s constant of CO2 in imidazolium-based ILs with [Tf2N], [BF4] and [PF6] is 7.12%. Therefore, the predicted data is in good agreement with experiments as shown in Figures.

19

Model library establishment (Quantitative model)

Case study

20

Stage 2 Process Design

21

Framework: Workflow & Dataflow

Problem: gas mixtures need to be separated Available: solubility, selectivity, viscosity …. Find: potential IL solvent

Stage 1 ILs screening is highlighted together with the collected data and developed models in this presentation.

Step 1: Problem definition (gas mixture, product)

START Workflow

22

Stage 1: Screening

Gas A H2

B CH4

C C2H4

D C2H6

E CO2

Shale gas model 5 gas product separate

Step 1: Problem definition (gas mixture:A,B,C,D; product)

START Workflow

Step 2: ILs screening based on solubility

Tool Dataflow

2.1 Retrieve Henry constant data of each gas in ILs

2.2 List suitable ILs for each gas (Hn< Ho)

A(H2) B(CH4) C(C2H4) D(C2H6) E(CO2) IL1 ✘ ✔ ✘ ✔ ✔ IL2 ✘ ✘ ✔ ✘ ✔ … … … … … … ILn ✘ ✘ ✔ ✔ ✘

Database Literature

Henry constant

Objective: Find feasible ILs for each gas

23

Stage 1: Screening

START Workflow Tool Dataflow

Step 3: ILs screening based on selectivity

Objective: Narrow down the ILs through further considering the selectivity

…

Feasible IL: 𝑆𝑆 =

𝐻𝐻𝑚𝑚𝑚𝑚𝑚𝑚𝐻𝐻𝑚𝑚𝑚𝑚𝑚𝑚

> 𝑆𝑆𝑜𝑜 𝐸𝐸𝐸𝐸. (5)

Database Literature Henry constant

For five gases (ABCDE) separation problem, list 30 possible cases as below, for each case, the corresponding feasible ILs (from step 2) can be chosen :

24

Stage 1: Screening

Where 𝐻𝐻𝑚𝑚𝑚𝑚𝑚𝑚 is the minimum H of the unabsorbed gas; 𝐻𝐻𝑚𝑚𝑚𝑚𝑚𝑚 is the maxmum H of the absorbed gas.

ILa one gas absorbed ILs:

two gas absorbed ILs:

three gas absorbed ILs:

four gas absorbed ILs:

ILb ILc ILd ILe

ILab ILac ILad ILae ILbc ILbd ILbe ILcd ILce ILde

ILabc ILabd ILade ILbcd ILbce ILcde ILabe ILacd ILbcd ILbde

ILabcd ILabce ILabde ILacde ILbcde

START Workflow

Step 4: Gas mixture analysis

…

25

Stage 1: Screening

Objective: Narrow down the ILs through considering the economical aspect

4.1 Composition analysis

80%

3% 7% 3% 7%

Shale gas

CH4H2CO2C2H4C2H6

Shale Gas Percent

A H2 3%

B CH4 80%

C C2H4 3%

D C2H6 7%

E CO2 7%

Database Literature

Tool

Shale Gas Percent B.P/oC

A H2 3% -252.8

B CH4 80% -161.4

C C2H4 3% -103.7

D C2H6 7% -88.6

E CO2 7% -78.5

START Workflow

Step 4: Gas mixture analysis

…

26

Stage 1: Screening

4.1 Composition analysis

4.2 Remove the undesired ILs

Objective: Narrow down the ILs through considering the economical aspect

Remove the solvents (ILs) for the compound in the largest amount (B) together with the gas which has a

lower B.P. than B.

Screening rule:

Optimal solvent: the ILs which don’t like CH4 and H2.

Organize the gas w.r.t boiling point.

Increased

Database Literature

Tool

Shale Gas Percent B.P/oC

A H2 3% -252.8

B CH4 80% -161.4

C C2H4 3% -103.7

D C2H6 7% -88.6

E CO2 7% -78.5

START Workflow

Step 4: Gas mixture analysis

…

27

Stage 1: Screening

4.1 Composition analysis

4.2 Remove the undesired ILs

Objective: Narrow down the ILs through considering the economical aspect

Database tool: experimental data

Henry’s constant of 5 gases in [emim][Tf2N]-left and [bmim][BF4]-right

Tool

Database Literature

Shale Gas Percent B.P/oC

A H2 3% -252.8

B CH4 80% -161.4

C C2H4 3% -103.7

D C2H6 7% -88.6

E CO2 7% -78.5

START Workflow

Step 4: Gas mixture analysis

…

28

Stage 1: Screening

4.1 Composition analysis

4.2 Remove the undesired ILs

Objective: Narrow down the ILs through considering the economical aspect

Database tool: experimental data

Henry’s constant of 5 gases in [emim][Tf2N]-left and [bmim][BF4]-right

Tool

Database Literature

CH4 and H2 have lower solubility in ILs

Shale Gas Percent B.P/oC

A H2 3% -252.8

B CH4 80% -161.4

C C2H4 3% -103.7

D C2H6 7% -88.6

E CO2 7% -78.5

START Workflow

Step 4: Gas mixture analysis

…

29

Stage 1: Screening

4.1 Composition analysis

4.2 Remove the undesired ILs

Objective: Narrow down the ILs through considering the economical aspect

Database tool: experimental data

Henry’s constant of 5 gases in [emim][Tf2N]-left and [bmim][BF4]-right

Tool

Database Literature

It is possible to find solvent which don’t like both CH4 and H2 in database.

START Workflow

Step 4: Gas mixture analysis

…

30

Stage 1: Screening Tool Dataflow

Step 5: ILs screening based on physical properties

Objective: Find the promising IL w.r.t. absorber parameters

5.1 Retrieve the physical property data of the selected IL Viscosity, density….

Database for IL pure property

5.2 Ranking the candidate based on optimal properties of selected IL

[emim][Tf2N] In this case study:

Stage 2 Process Design

31

Stage 2: Process Design

Design: hybrid gas separation process scheme. Select: the process for high energy efficiency.

32

Stage 2: Process Design STAR

T Workflow Tools & methods

…

Step 7: Preliminarily evaluate the flowsheets Flowsheet

energy method

Step 8: Select the most efficient process design scheme for hybrid gas

separation processes

Step 6: Generate all possible flowsheets Knowledge

based method for Separation

techniques

33

Step 6: Generate all possible flowsheets

ABCDE

Knowledge based method for Separation

techniques

*Where d means distillation, m means membrane separation

Tools & methods

The promising IL which don’t like both A and B.

ILcde-AB/CDE

d or m or ILb-A/B

d or ILc or ILde-C/DE

ILd or ILce-D/CE

d or ILd or ILe-D/E

AB part

CDE part

Ex.

The first operation unit: absorber

34

Step 8-Select flowsheet (case study)

17% of total gas

Evaporation of the gases: 13.5 KJ/mol gas

Preliminary energy balance

100 kmol/h

170 kmol/h

No energy required compared to distillation

Stage 2 Process Design

Simulate: IL-based absorption hybrid process Analyze: energy consumption and economy. Compare: traditional process and IL-based process.

35

Stage 3: Process Simulation & Evaluation

• The objective of Stage 3 is to simulate and evaluate the IL-based hybrid gas separation process by comparing the energy consumption and economical cost with conventional process.

36

Stage 3: Process Simulation & Evaluation START Workflow

Step 9: Rigorous IL-based hybrid gas separation process simulation

…

Step 10: Energy consumption and economical cost evaluation of the process

Step 11:Compare the IL-based gas separation with conventional process

Final process design

I. Databases and model library – Databases store many experimental data of gas solubility

and Henry’s constant. – Models used for property prediction play an important role in

gas separation problems.

II. IL-based gas separation process – A framework for the ILs screening, gas separation process

design and evaluation is proposed. – An IL-based hybrid separation scheme for shale gas model

has been introduced and the potential energy-saving highlighted through a conceptual example.

37

Conclusion

– Further work is necessary to fine-tune the IL screening tool (database, model, search engine).

– More detailed analysis is necessary to account for all possible performance criteria to establish the hybrid gas separation scheme.

– Different gas separation case studies will be developed.

38

Future work

39

Thank you very much

40

Case study- Feed gas analysis

CO2

C2H4

CH4

C2H6

H2

Shale gas

Traditional technology Boiling point (℃)

-252.8

-161.4

-103.7

-88.6

-78.5

Large energy consumption Find alternative hybrid separation process

Database & Model library

Database

Model library

Solubility, Henry constant

Pure IL property

Henry constant predicted model

Pure IL property model (Quantitative model)

Qualitative model

Quantitative model

Measured data Predicted data

41

Database and model library

Measured solubility database Gas ILs number Data dots

CO2 137 8827

CH4 11 510

H2S 15 722

NH3 7 225

SO2 16 303

CO 5 198

N2 10 208

O2 12 728

H2 12 552

N2O 12 577

C2H6 14 614

C3H8 3 95

C4H10 1 16

C2H4 3 268

C3H6 1 16

C4H8 1 16

42

Database establishment

Measured solubility database Gas ILs number Data dots

CO2 137 8827

CH4 11 510

H2S 15 722

NH3 7 225

SO2 16 303

CO 5 198

N2 10 208

O2 12 728

H2 12 552

N2O 12 577

C2H6 14 614

C3H8 3 95

C4H10 1 16

C2H4 3 268

C3H6 1 16

C4H8 1 16

43

Database establishment

Gas number :16

ILs number: 260

Data dots: 13875

Measured Henry constant database

44

Database establishment

Gas ILs number Data dots

CO2 34 165

CH4 22 130

C2H4 14 38

C2H6 10 41

C3H8 6 12

C4H10 1 7

N2O 6 21

N2 9 46

H2 4 28

O2 3 19

CO 1 9

Measured Henry constant database

45

Database establishment

Gas ILs number Data dots

CO2 34 165

CH4 22 130

C2H4 14 38

C2H6 10 41

C3H8 6 12

C4H10 1 7

N2O 6 21

N2 9 46

H2 4 28

O2 3 19

CO 1 9

Gas number :11

ILs number: 110

Data dots: 516

All of these measured data can be used for quantitative model to be applied to correction for predicted data and rigorous simulation.

Predicted Henry constant database (COSMO-RS)

46

Database establishment

Predicted Henry constant database (COSMO-RS)

47

Database establishment

CO2 CH4 C2H2 C2H4

C2H6 CO N2 NH3

O2 SO2 COS H2S H2

13 Gases

Predicted Henry constant database (COSMO-RS)

48

Database establishment

209 kinds of cations

Imidazole Pyridine Pyrrole Phosphonium Ammonium ……

Predicted Henry constant database (COSMO-RS)

49

Database establishment

209 kinds of cations 65 kinds of anions

Imidazole Pyridine Pyrrole Phosphonium Ammonium ……

Predicted Henry constant database (COSMO-RS)

50

Database establishment

209 kinds of cations 65 kinds of anions

Imidazole Pyridine Pyrrole Phosphonium Ammonium ……

ILs number: 13585