New performance indicators for adsorbent evaluation derived from a reduced order model of an idealized PSA process for CO 2 capture

Ga, Seongbin; Jang, Hong; Lee, Jay H.

doi:10.1016/j.compchemeng.2016.11.021

Cited by 40 publications

(31 citation statements)

References 22 publications

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“…Later, Park et al 94 assessed separation performance of selected MOFs for subambient temperature postcombustion carbon capture based on (i) a selection of simple adsorbent metrics (e.g., CO 2 swing capacity, selectivity, and regenerability), (ii) performance in an idealized 2-step PSA model (adopted from Ga et al 123 ) consisting of adsorption and desorption steps, and (iii) performance in a rigorous model of 4-step Skarstrom cycle with light product pressurization. The results from this study showed that the order of high performing materials is different for the idealized 2-step model and the 4-step Skarstrom cycle.…”

Section: Computational Screening Of Porous Materials: a Historical Perspectivementioning

confidence: 99%

Performance-Based Screening of Porous Materials for Carbon Capture

et al. 2021

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Computational screening methods have changed the way new materials and processes are discovered and designed. For adsorption-based gas separations and carbon capture, recent efforts have been directed toward the development of multiscale and performance-based screening workflows where we can go from the atomistic structure of an adsorbent to its equilibrium and transport properties at different scales, and eventually to its separation performance at the process level. The objective of this work is to review the current status of this new approach, discuss its potential and impact on the field of materials screening, and highlight the challenges that limit its application. We compile and introduce all the elements required for the development, implementation, and operation of multiscale workflows, hence providing a useful practical guide and a comprehensive source of reference to the scientific communities who work in this area. Our review includes information about available materials databases, state-of-the-art molecular simulation and process modeling tools, and a complete catalogue of data and parameters that are required at each stage of the multiscale screening. We thoroughly discuss the challenges associated with data availability, consistency of the models, and reproducibility of the data and, finally, propose new directions for the future of the field.

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Section: Computational Screening Of Porous Materials: a Historical Perspectivementioning

confidence: 99%

Performance-Based Screening of Porous Materials for Carbon Capture

et al. 2021

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“…Then, various performance metrics (e.g., working capacity and selectivity) are calculated to identify top‐ranked candidates for further process evaluation when applicable. Although this approach works for a large adsorbent database, those widely used metrics can neither tell whether separation requirements are satisfied nor rank the adsorbents correctly in terms of real process performance 13–15 . Alternatively, hypothetical adsorption isotherms can be sampled and used for process optimization.…”

Section: Introductionmentioning

confidence: 99%

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

2021

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An integrated metal–organic framework (MOF) and pressure/vacuum swing adsorption (P/VSA) process design framework is presented for gas separation. It consists of two steps: adsorbent descriptor optimization, and MOF matching. In the first step, MOFs are represented as a large set of chemical and geometric descriptors from which the most influential ones are selected via a multistep screening method and treated as design variables. The valid design space of the selected descriptors is confined using a tailored classifier model and logic constraints. Based on collected adsorption isotherms of 471 different MOFs, data‐driven isotherm models are developed. Combining the design space, isotherms, and four‐step P/VSA process models, an integrated MOF and P/VSA process design problem is formulated. MOF descriptors and process operating conditions are optimized to maximize the process performance. The obtained optimal descriptors and isotherms can be used to guide the discovery of high‐performance MOFs in a subsequent MOF matching step. This article addresses the first descriptor optimization step exemplified by propene/propane separation.

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“…The major research issue for these two technologies at this point is to screen and develop high-performance materials for membranes 5 or adsorbents. 6 In addition to specific CCUS technologies, systems-level methodologies for strategic planning and policy making have been developed. CO 2 capture and utilization offer a large number of options in terms of technologies and final products.…”

Section: ■ Introductionmentioning

confidence: 99%

Input–Output Surrogate Models for Efficient Economic Evaluation of Amine Scrubbing CO₂ Capture Processes

Chung

Lee

2020

Ind. Eng. Chem. Res.

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Carbon capture followed by utilization or storage enables carbon-intensive sectors to abate their CO2 emissions. However, complexity and nonlinearity of the capture processes hinder the incorporation of their first-principles models into analyses and optimizations of overall carbon management strategies. Accordingly, it is desirable to have a systematic method to develop a computationally less demanding surrogate model, which can replace the rigorous CO2 capture process model, for use in a high-level decision-making environment. For such purposes, the surrogate model should be able to provide multiple information needed to connect to subsequent processing steps under varying source stream conditions. This research addresses the development of surrogate models for CO2 capture processes that enjoy significantly lowered complexity while preserving the key information. A surrogate model can be constructed by fitting the input–output data generated by process simulation and optimization with the rigorous model. Following the proposed method, surrogate models for the amine-based CO2 capture processes with two representative types of amines, monoethanolamine (MEA) and piperazine (PZ), are constructed and validated. The constructed surrogate models predict the specific steam consumption rate and total equipment purchase cost based on the input information of desired capture rate and CO2 source stream condition. The predicted information is shown to agree well with the simulation result of the rigorous first-principles model. This surrogate modeling approach can be applied to compare different capture technologies in the context of analyzing and synthesizing a larger CCUS processing network.

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New performance indicators for adsorbent evaluation derived from a reduced order model of an idealized PSA process for CO 2 capture

Cited by 40 publications

References 22 publications

Performance-Based Screening of Porous Materials for Carbon Capture

Performance-Based Screening of Porous Materials for Carbon Capture

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

Input–Output Surrogate Models for Efficient Economic Evaluation of Amine Scrubbing CO₂ Capture Processes

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New performance indicators for adsorbent evaluation derived from a reduced order model of an idealized PSA process for CO 2 capture

Cited by 40 publications

References 22 publications

Performance-Based Screening of Porous Materials for Carbon Capture

Performance-Based Screening of Porous Materials for Carbon Capture

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

Input–Output Surrogate Models for Efficient Economic Evaluation of Amine Scrubbing CO2 Capture Processes

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Product

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Input–Output Surrogate Models for Efficient Economic Evaluation of Amine Scrubbing CO₂ Capture Processes