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Subraveti SG, Li Z, Prasad V, Rajendran A. Can a Computer “Learn” Nonlinear Chromatography?: Experimental Validation of Physics-Based Deep Neural Networks for the Simulation of Chromatographic Processes. Ind Eng Chem Res 2023. [DOI: 10.1021/acs.iecr.2c04355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
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Need for a Next Generation of Chromatography Models—Academic Demands for Thermodynamic Consistency and Industrial Requirements in Everyday Project Work. Processes (Basel) 2022. [DOI: 10.3390/pr10040715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Process chromatography modelling for process development, design, and optimization as well as process control has been under development for decades. Still, the discussion of scientific potential and industrial applications needs is open to innovation. The discussion of next-generation modelling approaches starting from Langmuirian to steric mass action and multilayer or thermodynamic consistent real and ideal adsorption theory or colloidal particle adsorption approaches is continued.
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Farmahini AH, Krishnamurthy S, Friedrich D, Brandani S, Sarkisov L. Performance-Based Screening of Porous Materials for Carbon Capture. Chem Rev 2021; 121:10666-10741. [PMID: 34374527 PMCID: PMC8431366 DOI: 10.1021/acs.chemrev.0c01266] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Indexed: 02/07/2023]
Abstract
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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Affiliation(s)
- Amir H. Farmahini
- Department
of Chemical Engineering and Analytical Science, School of Engineering, The University of Manchester, Manchester M13 9PL, United Kingdom
| | | | - Daniel Friedrich
- School
of Engineering, Institute for Energy Systems, The University of Edinburgh, Edinburgh EH9 3FB, United Kingdom
| | - Stefano Brandani
- School
of Engineering, Institute of Materials and Processes, The University of Edinburgh, Sanderson Building, Edinburgh EH9 3FB, United Kingdom
| | - Lev Sarkisov
- Department
of Chemical Engineering and Analytical Science, School of Engineering, The University of Manchester, Manchester M13 9PL, United Kingdom
- School
of Engineering, Institute of Materials and Processes, The University of Edinburgh, Sanderson Building, Edinburgh EH9 3FB, United Kingdom
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Sun Y, DeJaco RF, Li Z, Tang D, Glante S, Sholl DS, Colina CM, Snurr RQ, Thommes M, Hartmann M, Siepmann JI. Fingerprinting diverse nanoporous materials for optimal hydrogen storage conditions using meta-learning. SCIENCE ADVANCES 2021; 7:7/30/eabg3983. [PMID: 34290094 PMCID: PMC8294760 DOI: 10.1126/sciadv.abg3983] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 06/04/2021] [Indexed: 06/03/2023]
Abstract
Adsorptive hydrogen storage is a desirable technology for fuel cell vehicles, and efficiently identifying the optimal storage temperature requires modeling hydrogen loading as a continuous function of pressure and temperature. Using data obtained from high-throughput Monte Carlo simulations for zeolites, metal-organic frameworks, and hyper-cross-linked polymers, we develop a meta-learning model that jointly predicts the adsorption loading for multiple materials over wide ranges of pressure and temperature. Meta-learning gives higher accuracy and improved generalization compared to fitting a model separately to each material and allows us to identify the optimal hydrogen storage temperature with the highest working capacity for a given pressure difference. Materials with high optimal temperatures are found in close proximity in the fingerprint space and exhibit high isosteric heats of adsorption. Our method and results provide new guidelines toward the design of hydrogen storage materials and a new route to incorporate machine learning into high-throughput materials discovery.
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Affiliation(s)
- Yangzesheng Sun
- Department of Chemistry and Chemical Theory Center, University of Minnesota, 207 Pleasant Street SE, Minneapolis, MN 55455-0431, USA
| | - Robert F DeJaco
- Department of Chemical Engineering and Materials Science, University of Minnesota, 412 Washington Avenue SE, Minneapolis, MN 55455-0132, USA
| | - Zhao Li
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
| | - Dai Tang
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0100, USA
| | - Stephan Glante
- Erlangen Center for Interface Research and Catalysis (ECRC), Friedrich-Alexander-University Erlangen-Nürnberg, Egerlandtstr. 3, 91058 Erlangen, Germany
| | - David S Sholl
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0100, USA
| | - Coray M Colina
- Department of Chemistry, University of Florida, PO Box 117200, Gainesville, FL 32611, USA
| | - Randall Q Snurr
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
| | - Matthias Thommes
- Department of Chemical and Bioengineering, Institute of Separation Science and Technology, Friedrich-Alexander-University Erlangen-Nürnberg, 91058 Erlangen, Germany
| | - Martin Hartmann
- Erlangen Center for Interface Research and Catalysis (ECRC), Friedrich-Alexander-University Erlangen-Nürnberg, Egerlandtstr. 3, 91058 Erlangen, Germany
| | - J Ilja Siepmann
- Department of Chemistry and Chemical Theory Center, University of Minnesota, 207 Pleasant Street SE, Minneapolis, MN 55455-0431, USA.
- Department of Chemical Engineering and Materials Science, University of Minnesota, 412 Washington Avenue SE, Minneapolis, MN 55455-0132, USA
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