Modeling Selection of Post-Combustion Carbon Scavenging Adsorbents

Abstract

A method for proposing one or more promising adsorbents, the method being performed using an adsorbent modeling computing device including a processor coupled to a memory device. The method includes generating an initial set of primary features using the adsorbent modeling framework of the adsorbent modeling computing device, and determining one or more secondary features using the adsorbent modeling framework, wherein the one or more secondary features are combinations of the generated primary features, including interaction parameters. The method also includes subjecting the feature sets of primary and secondary features to a correlation review using the adsorbent modeling framework, and proposing a carbon capture performance framework using the adsorbent modeling framework. The method further includes proposing one or more promising adsorbents for use by a post-combustion carbon system using the adsorbent modeling computing device.

Claims

1. A method (200) for proposing one or more promising adsorbents, wherein the method (200) is performed using an adsorbent modeling computing device (140) including a processor coupled to a memory device, Using the adsorbent modeling framework of the adsorbent modeling computing device (140), an initial set of primary features is generated (202), Determining one or more secondary features using the adsorbent modeling framework of the adsorbent modeling computing device (140) (204), wherein the one or more secondary features are a combination of the generated primary features including interaction parameters (204), Using the adsorbent modeling framework of the adsorbent modeling computing device (140), the feature sets of primary and secondary features are subjected to correlation review (206), Using the adsorbent modeling framework of the adsorbent modeling computing device (140), a carbon scavenging performance framework is proposed (208), The adsorbent modeling computing device (140) proposes one or more potential adsorbents to be used by a post-combustion carbon system (210) Method (200), including.

2. The method according to claim 1 (200), wherein generating the initial set of primary features (202) comprises generating one or more adsorbent parameters for a known adsorbent.

3. The method according to claim 2 (200), wherein generating one or more adsorbent parameters for a known adsorbent includes generating one or more of isosteric heat of adsorption, Henry's law constant, pore size, pore volume, and surface area.

4. The method according to claim 1 (200), wherein proposing one or more promising adsorbents (210) includes proposing one or more metal-organic frameworks.

5. The method according to claim 1 (200), wherein determining the interaction parameter includes determining at least one of the correlation coefficient and statistical significance.

6. The method according to claim 1 (200), wherein providing the feature set to the correlation review (206) includes generating a pair plot or scatter plot.

7. The method according to claim 1 (200), wherein providing the feature set to the correlation review (206) includes generating a correlation matrix.

8. The method according to claim 1 (200), wherein proposing the carbon capture performance framework (208) includes performing a regression analysis and generating a transfer function generated from the regression analysis.

9. The method according to claim 8 (200), wherein the proposal of one or more promising adsorbents (210) is based on carbon scavenging performance values ​​determined by the carbon scavenging performance framework.

10. The method of claim 9 (200), wherein the proposal of one or more promising adsorbents (210) is further comprising determining the carbon capture performance value using the transfer function generated from the regression analysis.

11. Memory and A processor that is communicatively coupled to the memory, wherein the processor The process involves generating an initial set of primary features using an adsorbent modeling framework, Determining one or more secondary features in the adsorbent modeling framework, wherein the one or more secondary features are a combination of the generated primary features including interaction parameters, The adsorbent modeling framework is used to provide the feature sets of primary and secondary features for correlation review, To propose a carbon scavenging performance framework using the aforementioned adsorbent modeling framework, To propose one or more promising adsorbents for use in post-combustion carbon systems. The processor and An adsorbent modeling computing device (140) is provided.

12. The adsorbent modeling computing device (140) according to claim 11, wherein the initial set of primary features includes one or more adsorbent parameters for known adsorbents.

13. The adsorbent modeling computing device (140) according to claim 12, wherein one or more adsorbent parameters for a known adsorbent include one or more of isosteric heat of adsorption, Henry's law constant, pore size, pore volume, and surface area.

14. The adsorbent modeling computing device (140) according to claim 11, wherein the proposed one or more promising adsorbents comprises one or more metal-organic frameworks.

15. The adsorbent modeling computing device (140) according to claim 11, wherein the interaction parameter includes at least one of a correlation coefficient and statistical significance.

16. The adsorbent modeling computing device (140) according to claim 11, wherein the correlation review includes generating a pair plot or a scatter plot.

17. The adsorbent modeling computing device (140) according to claim 11, wherein the correlation review includes generating a correlation matrix.

18. The adsorbent modeling computing device (140) according to claim 11, wherein the carbon capture performance framework includes a regression analysis and a transfer function generated based on the regression analysis.

19. The adsorbent modeling computing device (140) according to claim 18, wherein the proposed one or more promising adsorbents are based on carbon capture performance values ​​determined by the carbon capture performance framework.

20. The adsorbent modeling computing device (140) according to claim 19, wherein the carbon capture performance value is determined using the transfer function generated based on the regression analysis.

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