Integrated metal–organic framework and pressure/vacuum swing adsorption process design: Descriptor optimization

Zhang, Xiang; Zhou, Teng; Sundmacher, Kai

doi:10.1002/aic.17524

Cited by 16 publications

(19 citation statements)

References 52 publications

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“…For concision, the detailed model equations and descriptions are not repeated here. They can be found in our first article 11

\min E ((),,,,,, P_{italicAD}, P_{italicDE}, t_{italicPR}, t_{italicAD}, t_{italicRI}, t_{italicDE})

normals . normalt . \frac{\partial y_{i}}{\partial italict} = f_{1} ((), y_{i}, italicu, italicP, q_{E}, q_{A})

gas—phase component mass balances

\frac{italic∂ italicP}{\partial italict} = f_{2} ((), italicP, italicu, q_{E}, q_{A})

total mass balance

\frac{italic∂ q_{i}}{\partial italict} = f_{3} ((),, {italicq}_{italici}^{italiceq}, q_{italici})

solid—phase balances and mass transfer kinetics

f_{4} ((),,, {italicq}_{italici}^{italiceq}, P, {italicy}_{italici}) = 0

DSL isotherm models

f_{5} ((), P, italicu, y_{A}, y_{E}) = 0

pressure drop

f_{6} ((), y_{i}, q_{i}, italicP, italicu) = 0

cyclic steady state and boundary conditions

…”

Section: Property‐performance Relationships For Efficient Mof Screeningmentioning

confidence: 99%

“…The molfiles were directly analyzed using the cheminformatics package RDKit to get the detailed information of atoms, bonds, and groups. Accordingly, the 10 chemical descriptors can be calculated based on their definitions given in Step 1 11 . Additionally, different from Step 1 where Zeo++ was used, the XYZfiles and the PoreBlazer 4 program were applied for computing geometric descriptors.…”

Section: Computational Mof Synthesis and Screeningmentioning

confidence: 99%

“…

w_{j}

S V_{j}

ρ

, and

β

are the SVM model parameters. All these parameters have been given in the first article 11 . After applying the above validity domain constraints, 4269 MOF candidates were remained.…”

Section: Computational Mof Synthesis and Screeningmentioning

confidence: 99%

“…Despite many progress in these two areas, it is worth noting that to select adsorbents and optimize P/VSA processes separately is insufficient to obtain a truly optimal P/VSA process. It has been widely recognized that P/VSA process design is a multi‐scale design problem that incorporates inter‐linked adsorbent material, gas–solid phase, and P/VSA process levels 9–11 . Variations of adsorbent materials and process operating conditions can simultaneously affect the adsorption behavior in the gas–solid phase and hence jointly determine the final process performance.…”

Section: Introductionmentioning

confidence: 99%

“…Following the above strategy, the authors' group recently proposed a two‐step integrated MOF and P/VSA process design methodology (see Figure 1) to solve the multi‐scale design problem. In the first article of this series, Step 1 (i.e., descriptor optimization) has been elaborated and published 11 . Only optimal MOF descriptors have been obtained, instead of any hypothesized MOFs with known topology and chemistry.…”

Section: Introductionmentioning

confidence: 99%

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Integrated metal‐organic framework (MOF) and pressure/vacuum swing adsorption process design:MOFmatching

2022

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A two-step integrated metal-organic framework (MOF) and pressure/vacuum swing adsorption (P/VSA) process design has been recently established for gas separation.In the first step, selected MOF descriptors and process operating conditions are simultaneously optimized to maximize the process performance. Based on the obtained results, the second step (i.e., MOF matching) is addressed and exemplified by propene/propane separation in this work. Computational MOF synthesis and screening are explicitly carried out to find new advanced material candidates for enhancing the separation process efficiency. First, model-based propertyperformance relationships are developed for fast MOF screening. Then, MOF building blocks are extracted from 471 MOFs contained in the Computation-Ready Experimental (CoRE) MOF database. With these building blocks, 45,472 hypothetical MOFs are created. After model-based and molecular simulation-based screening, six candidates are left and sent to P/VSA process optimization. Finally, three candidates are found to meet the predefined separation specifications and one candidate shows a better process performance than the best out of the 471 CoRE MOFs.

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“…For concision, the detailed model equations and descriptions are not repeated here. They can be found in our first article 11

\min E ((),,,,,, P_{italicAD}, P_{italicDE}, t_{italicPR}, t_{italicAD}, t_{italicRI}, t_{italicDE})

normals . normalt . \frac{\partial y_{i}}{\partial italict} = f_{1} ((), y_{i}, italicu, italicP, q_{E}, q_{A})

gas—phase component mass balances

\frac{italic∂ italicP}{\partial italict} = f_{2} ((), italicP, italicu, q_{E}, q_{A})

total mass balance

\frac{italic∂ q_{i}}{\partial italict} = f_{3} ((),, {italicq}_{italici}^{italiceq}, q_{italici})

solid—phase balances and mass transfer kinetics

f_{4} ((),,, {italicq}_{italici}^{italiceq}, P, {italicy}_{italici}) = 0

DSL isotherm models

f_{5} ((), P, italicu, y_{A}, y_{E}) = 0

pressure drop

f_{6} ((), y_{i}, q_{i}, italicP, italicu) = 0

cyclic steady state and boundary conditions

…”

Section: Property‐performance Relationships For Efficient Mof Screeningmentioning

confidence: 99%

Section: Computational Mof Synthesis and Screeningmentioning

confidence: 99%

“…

w_{j}

S V_{j}

ρ

, and

β

are the SVM model parameters. All these parameters have been given in the first article 11 . After applying the above validity domain constraints, 4269 MOF candidates were remained.…”

Section: Computational Mof Synthesis and Screeningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrated metal‐organic framework (MOF) and pressure/vacuum swing adsorption process design:MOFmatching

2022

Self Cite

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Functional Material Systems Enabled by Automated Data Extraction and Machine Learning

Kalhor

Jung

Bräse

et al. 2023

Adv Funct Materials

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The development of new functional materials is crucial for addressing global challenges such as clean energy or the discovery of new drugs and antibiotics. Functional material systems are typically composed of functional molecular building blocks, organized across multiple length scales in a hierarchical order. The large design space allows for precise tuning of properties to specific applications, but also makes it time‐consuming and expensive to screen for optimal structures using traditional experimental methods. Machine learning (ML) models can potentially revolutionize the field of materials science by predicting chemical syntheses and materials properties with high accuracy. However, ML models require data to be trained and validated. Methods to automatically extract data from scientific literature make it possible to build large and diverse datasets for ML models. In this article, opportunities and challenges of data extraction and machine learning methods are discussed to accelerate the discovery of high‐performing functional material systems, while ensuring that the predicted materials are stable, synthesizable, scalable, and sustainable. The potential impact of large language models (LLMs) on the data extraction process are discussed. Additionally, the importance of research data management tools is discussed to overcome the intrinsic limitations of data extraction approaches.

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Multiscale screening of metal‐organic frameworks for one‐step ethylene purification in pressure‐swing adsorption processes

Zhou,

Cao,

Shang

et al. 2024

AIChE Journal

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Purification of polymer‐grade (99.9%) C2H4 from C2 hydrocarbon mixture is industrially important but challenging. Cryogenic distillation is energy intensive. Recently, pressure/vacuum swing adsorption (P/VSA) processes using metal‐organic frameworks (MOFs) as adsorbents have been attracting increasing attention as an alternative technology. A multiscale hierarchical framework is proposed to select promising MOFs and design suitable P/VSA processes by combining property‐based material screening and model‐based process optimization. Using this framework, we successfully identified seven promising one‐step C2H4 purification MOFs that outperformed the benchmark TJT‐100 in both C2H4 purity and recovery for the 5/90/5 C2H2/C2H4/C2H6 feed mixture. Among them, OFUCAV exhibited the highest C2H4 productivity of 0.158 mol/m3/s while JAVTAC demonstrated the lowest energy consumption of 42.76 kWh/tonne. The latter can potentially save 80% of the energy consumption compared to cryogenic distillation. Moreover, these two P/VSA processes show very high robustness in response to composition fluctuations in the feed stream.

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Integrated metal–organic framework and pressure/vacuum swing adsorption process design: Descriptor optimization

Cited by 16 publications

References 52 publications

Integrated metal‐organic framework (MOF) and pressure/vacuum swing adsorption process design:MOFmatching

Integrated metal‐organic framework (MOF) and pressure/vacuum swing adsorption process design:MOFmatching

Functional Material Systems Enabled by Automated Data Extraction and Machine Learning

Multiscale screening of metal‐organic frameworks for one‐step ethylene purification in pressure‐swing adsorption processes

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