Advanced methodology for screening of novel adsorption materials for cost-efficient CO2 capture

Riboldi, Luca; Charalambous, Charithea; Moubarak, Elias; Anantharaman, Rahul; Roussanaly, Simon; Fu, Chao; Smit, Berend; Young, John; Spek, Mijndert van der; Sánchez-Fernández, Eva; Ongari, Daniele; Majumdar, Sauradeep; García-Díez, Enrique; Kulakova, Viktoriia; García, Susana

doi:10.2139/ssrn.3815636

Cited by 5 publications

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“…This can be problematic, i.e., very computationally expensive, if one needs to optimize the process. Possibilities to address the CPU issue include fitting the IAST mixture predictions to B-splines as shown in the work of Santos et al, or precomputing the mixture isotherms on a grid and using interpolation techniques …”

Section: Discussionmentioning

confidence: 99%

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A Robust Framework for Generating Adsorption Isotherms to Screen Materials for Carbon Capture

Moubarak

Moosavi

Charalambous

et al. 2023

Ind. Eng. Chem. Res.

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To rank the performance of materials for a given carbon capture process, we rely on pure component isotherms from which we predict the mixture isotherms. For screening a large number of materials, we also increasingly rely on isotherms predicted from molecular simulations. In particular, for such screening studies, it is important that the procedures to generate the data are accurate, reliable, and robust. In this work, we develop an efficient and automated workflow for a meticulous sampling of pure component isotherms. The workflow was tested on a set of metal−organic frameworks (MOFs) and proved to be reliable given different guest molecules. We show that the coupling of our workflow with the Clausius− Clapeyron relation saves CPU time, yet enables us to accurately predict pure component isotherms at the temperatures of interest, starting from a reference isotherm at a given temperature. We also show that one can accurately predict the CO 2 and N 2 mixture isotherms using ideal adsorbed solution theory (IAST). In particular, we show that IAST is a more reliable numerical tool to predict binary adsorption uptakes for a range of pressures, temperatures, and compositions, as it does not rely on the fitting of experimental data, which typically needs to be done with analytical models such as dual-site Langmuir (DSL). This makes IAST a more suitable and general technique to bridge the gap between adsorption (raw) data and process modeling. To demonstrate this point, we show that the ranking of materials, for a standard three-step temperature swing adsorption (TSA) process, can be significantly different depending on the thermodynamic method used to predict binary adsorption data. We show that, for the design of processes that capture CO 2 from low concentration (0.4%) streams, the commonly used methodology to predict mixture isotherms incorrectly assigns up to 33% of the materials as top-performing.

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Section: Discussionmentioning

confidence: 99%

“…Possibilities to address the CPU issue include fitting the IAST mixture predictions to B-splines as shown in the work of Santos et al, 50 or precomputing the mixture isotherms on a grid and using interpolation techniques. 51 ■ ASSOCIATED CONTENT…”

Section: ■ Conclusionmentioning

confidence: 99%

A Robust Framework for Generating Adsorption Isotherms to Screen Materials for Carbon Capture

Moubarak

Moosavi

Charalambous

et al. 2023

Ind. Eng. Chem. Res.

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“…Possibilities to address the CPU issue include fitting the IAST mixture predictions to an analytical expression or pre-computing the mixture isotherm on a grid and using interpolation techniques. 49…”

Section: Discussionmentioning

confidence: 99%

A Robust Framework for Generating Adsorption Isotherms to Screen Materials for Carbon Capture

Moubarak

Moosavi

Charalambous³

et al. 2023

Preprint

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To rank the performance of materials for a given carbon capture process, we rely on pure component isotherms from which we predict the mixture isotherms. For screening a large number of materials we also increasingly rely on isotherms predicted from molecular simulations. In particular, for such screening studies, it is important that the procedures to generate the data are accurate, reliable, and robust. In this work, we develop an efficient and automated workflow for a meticulous sampling of pure component isotherms. The workflow was tested on a set of metal-organic frameworks (MOFs) and proved to be reliable given different guest molecules. We show that the coupling of our workflow with the Clausius-Clapeyron relation saves CPU time, yet enables us to accurately predict pure component isotherms at the temperatures of interest, starting from a reference isotherm at a given temperature. We also show that one can accurately predict the CO2 and N2 mixture isotherms using Ideal Adsorbed Solution Theory (IAST). In particular, we show that IAST is a more reliable numerical tool to predict binary adsorption uptakes for a range of pressures, temperatures, and compositions, as it does not rely on the fitting of experimental data, which typically needs to be done with analytical models such as dual-site Langmuir (DSL). This makes IAST a more suitable and general technique to bridge the gap between adsorption (raw) data and process modelling. To demonstrate this point, we show that the ranking of materials, for a standard 3-step Temperature Swing Adsorption (TSA) process, can be significantly different depending on the thermodynamic method used to predict binary adsorption data. We show that for the design of processes that capture CO2 from low concentration (0.4%) streams, the commonly used methodology to predict mixture isotherms incorrectly assigns up to 33% of the materials as top-performing.

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“…Hence it is essential to address the CPU issue. For example, by fitting the IAST mixture predictions to an analytical expression or pre-compute the mixture isotherm on a grid and using interpolation techniques [23]. An alternative approach is the use of data science methods to develop a surrogate model.…”

Section: The Power Of An Integrated Platform For Materials Discoverymentioning

confidence: 99%

How to Decarbonize Our Energy Systems: Process‐Informed Design of New Materials for Carbon Capture

García

Smit

2023

Chemie Ingenieur Technik

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Decarbonisation from a variety of industrial and power emission sectors highlights a marked need for capture technologies that can be optimized for different CO 2 sources and integrated into an equally diverse range of applications of captured CO 2 as a feedstock. Some capture technologies are already operated at an industrial scale but may not be optimal for all required applications. Advanced tailored sorbent-based technologies allow flexible operation and reduced costs as they offer higher capture capacities and significantly lower energy penalties than the state-of-the-art systems. To accelerate the discovery, development, and deployment of novel advanced materials, it is critically important that efforts between experimentalists, theoreticians, and process engineers are coordinated. The PrISMa project addresses this challenge by integrating materials design with process design and environmental considerations to allow for tailor-making carbon capture solutions optimally tuned for local sources and sinks. In this article, we highlight some of the recent results obtained with the PrISMa platform.

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Advanced methodology for screening of novel adsorption materials for cost-efficient CO2 capture

Cited by 5 publications

References 0 publications

A Robust Framework for Generating Adsorption Isotherms to Screen Materials for Carbon Capture

A Robust Framework for Generating Adsorption Isotherms to Screen Materials for Carbon Capture

A Robust Framework for Generating Adsorption Isotherms to Screen Materials for Carbon Capture

How to Decarbonize Our Energy Systems: Process‐Informed Design of New Materials for Carbon Capture

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