Discrepancy quantification between experimental and simulated data of CO2 adsorption isotherm using hierarchical Bayesian estimation

Kojima, Sotaro; Park, Jongwoo; Carter, Eli A.; Walton, Krista S.; Sholl, David S.; Yajima, Tomoyuki; Fujiki, Junpei; Kawajiri, Yoshiaki

doi:10.1016/j.seppur.2022.121371

Cited by 11 publications

(8 citation statements)

References 80 publications

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“…Following data cleaning, the predictions concurred with the experimental results. Data cleaning was also utilized by Kojima et al to determine deviations between experimental and simulated data in the modeling of adsorption isotherms in nanoporous adsorbents . Reproducibility of experimental data is known to be an issue in materials chemistry research .…”

Section: Resultsmentioning

confidence: 99%

“…Data cleaning was also utilized by Kojima et al to determine deviations between experimental and simulated data in the modeling of adsorption isotherms in nanoporous adsorbents. 149 Reproducibility of experimental data is known to be an issue in materials chemistry research. 150 While the number of replicate measurements was often lacking, metaanalyses have described the inconsistency with replicate measurements of approximately 20% of reported adsorption isotherms for porous materials.…”

Section: Silica Aerogel Graphmentioning

confidence: 99%

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Silica Aerogel Synthesis/Process–Property Predictions by Machine Learning

et al. 2023

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Silica aerogels are mesoporous high surface area materials with extensive synthetic and processing conditions. To effectively synthesize aerogels, the impact of synthetic pathways on the resulting aerogel properties must be understood prior to experimental investigation. We develop an information architecture, the silica aerogel graph database (10 3 ), and a supervised machine learning neural network regression model to examine these relationships. The property graph database enables rapid queries and visualization of the impact of the synthesis and processing conditions on the final aerogel properties. The model maps from silica aerogel synthetic and processing conditions to predict the aerogel BET surface area with an average error of 109 ± 84 m 2 /g. Following a validation experiment, the model was shown to predict the aerogel surface area from new synthetic and processing conditions with an error of less than 5%. The experiment demonstrates the usefulness of the model in surface area prediction through the compatibility between computational and experimental results. Both in its current form and with further expansion, the developed graph database could reduce experimental dimensionality, time, and resources, enabling the successful synthesis of high surface area silica aerogels, which are advantageous for applications including thermal insulation, sorption media, and catalysis.

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Section: Resultsmentioning

confidence: 99%

Section: Silica Aerogel Graphmentioning

confidence: 99%

Silica Aerogel Synthesis/Process–Property Predictions by Machine Learning

et al. 2023

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“…As shown in Figure S7 and Tables S4 and S5, the Langmuir–Freundlich model had a higher R 2 value ( R 2 > 0.99) compared to the Langmuir model ( R 2 < 0.98), indicating the formation of a monolayer. This was based on chemisorption on a heterogeneous adsorbent surface, rather than on a homogeneous surface 31–33 . The amine efficiency (CO 2 /N molar ratio) was calculated (Figure 2B) to evaluate the efficiency of the amine groups for capturing CO 2 at 0.0004 bar.…”

Section: Resultsmentioning

confidence: 99%

“…This was based on chemisorption on a heterogeneous adsorbent surface, rather than on a homogeneous surface. [31][32][33] The amine efficiency (CO 2 /N molar ratio) was calculated (Figure 2B) to evaluate the efficiency of the amine groups for capturing CO 2 at 0.0004 bar. The results showed that the mixed-amine (PEI and DEA) strategy used for the synthesis of PEI/DEA@FS led to significant improvements in CO 2 adsorption capacity (0.75 mmol/g) and amine efficiency (0.14) compared to PEI@FS.…”

Section: Single-component Sorption Isothermsmentioning

confidence: 99%

Effective adsorptive removal of trace CO₂ from olefin using alkanolamine‐polyethylenimine‐functionalized fumed silica

Huang,

Yu,

Fan

et al. 2023

AIChE Journal

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The purification of gaseous olefins, such as C2H4 and C3H6, requires the efficient removal of trace CO2 impurities. However, the development of ideal adsorbents with high CO2/olefin selectivity has remained a challenge. In this study, we synthesized a novel adsorbent, denoted as PEI/DEA@FS, by modifying fumed silica (FS) with a combination of polyethylenimine (PEI) and diethanolamine (DEA). At 298 K and 0.0004 bar, PEI/DEA@FS presented the highest CO2 adsorption capacity among previously studied adsorbents, with a value of 2.12 mmol/g. Notably, PEI/DEA@FS demonstrated very high ideal adsorption solution theory selectivity for CO2/C2H4 (>1010) and CO2/C3H6 (>107) at 298 K and 0.01 bar. Breakthrough experiments revealed its excellent separation performance, producing olefins (C2H4 and C3H6) with ultra‐high purity (>99.9%) from a CO2/olefin (1/99) mixture. Moreover, PEI/DEA@FS exhibited outstanding stability over 20 adsorption and desorption cycles, and its CO2 adsorption performance remained almost unchanged under 75% relative humidity in the feed stream.

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“…[23,24] Bayesian approaches have been reported in many fields [23] including medicine and biology, as well as chemical engineering. [25][26][27] Recently, applications to a CO 2 adsorption process [22] and a liquid chromatographic process [21] have been reported.…”

Section: Introductionmentioning

confidence: 99%

Modeling, parameter estimation, and uncertainty quantification for CO₂ adsorption process using flexible metal–organic frameworks by Bayesian Monte Carlo methods

Sugimoto

Takakura

Fujiki

et al. 2023

J Adv Manuf & Process

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Flexible metal–organic frameworks (flexible MOFs) are considered promising adsorbents for CO2 capture, some of which have sigmoidal isotherm shapes that allow adsorption and desorption operations within a narrow partial pressure range. Nevertheless, modeling of adsorption processes employing flexible MOFs remains a challenge due to the unique isotherm shapes and kinetics. In this work, a Bayesian estimation framework is applied sequentially to handle two experimental data sets: isotherm and breakthrough measurements. The computational challenge for estimating the isotherm and kinetic parameters from the isotherm measurements and breakthrough experiments is resolved by Markov chain and sequential Monte Carlo methods. The uncertainties of the model parameters are obtained as probability distributions.

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Discrepancy quantification between experimental and simulated data of CO2 adsorption isotherm using hierarchical Bayesian estimation

Cited by 11 publications

References 80 publications

Silica Aerogel Synthesis/Process–Property Predictions by Machine Learning

Silica Aerogel Synthesis/Process–Property Predictions by Machine Learning

Effective adsorptive removal of trace CO₂ from olefin using alkanolamine‐polyethylenimine‐functionalized fumed silica

Modeling, parameter estimation, and uncertainty quantification for CO₂ adsorption process using flexible metal–organic frameworks by Bayesian Monte Carlo methods

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Discrepancy quantification between experimental and simulated data of CO2 adsorption isotherm using hierarchical Bayesian estimation

Cited by 11 publications

References 80 publications

Silica Aerogel Synthesis/Process–Property Predictions by Machine Learning

Silica Aerogel Synthesis/Process–Property Predictions by Machine Learning

Effective adsorptive removal of trace CO2 from olefin using alkanolamine‐polyethylenimine‐functionalized fumed silica

Modeling, parameter estimation, and uncertainty quantification for CO2 adsorption process using flexible metal–organic frameworks by Bayesian Monte Carlo methods

Contact Info

Product

Resources

About

Effective adsorptive removal of trace CO₂ from olefin using alkanolamine‐polyethylenimine‐functionalized fumed silica

Modeling, parameter estimation, and uncertainty quantification for CO₂ adsorption process using flexible metal–organic frameworks by Bayesian Monte Carlo methods