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Bradley W, Kim J, Kilwein Z, Blakely L, Eydenberg M, Jalvin J, Laird C, Boukouvala F. Perspectives on the Integration between First-Principles and Data-Driven Modeling. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107898] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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2
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Sharma N, Liu YA. A Hybrid
Science‐Guided
Machine Learning Approach for Modeling Chemical Processes: A Review. AIChE J 2022. [DOI: 10.1002/aic.17609] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Niket Sharma
- AspenTech Center of Excellence in Process System Engineering, Department of Chemical Engineering Virginia Polytechnic Institute and State University Blacksburg Virginia USA
| | - Y. A. Liu
- AspenTech Center of Excellence in Process System Engineering, Department of Chemical Engineering Virginia Polytechnic Institute and State University Blacksburg Virginia USA
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Gärtler M, Khaydarov V, Klöpper B, Urbas L. The Machine Learning Life Cycle in Chemical Operations – Status and Open Challenges. CHEM-ING-TECH 2021. [DOI: 10.1002/cite.202100134] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Marco Gärtler
- ABB Corporate Research Center Wallstadter Straße 59 68526 Ladenburg Germany
| | - Valentin Khaydarov
- Technische Universität Dresden Professur für Prozessleittechnik 01062 Dresden Germany
| | - Benjamin Klöpper
- ABB Corporate Research Center Wallstadter Straße 59 68526 Ladenburg Germany
| | - Leon Urbas
- Technische Universität Dresden Professur für Prozessleittechnik 01062 Dresden Germany
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5
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Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers. Processes (Basel) 2021. [DOI: 10.3390/pr9081456] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Chemical Product Engineering (CPE) is marked by numerous challenges, such as the complexity of the properties–structure–ingredients–process relationship of the different products and the necessity to discover and develop constantly and quickly new molecules and materials with tailor-made properties. In recent years, artificial intelligence (AI) and machine learning (ML) methods have gained increasing attention due to their performance in tackling particularly complex problems in various areas, such as computer vision and natural language processing. As such, they present a specific interest in addressing the complex challenges of CPE. This article provides an updated review of the state of the art regarding the implementation of ML techniques in different types of CPE problems with a particular focus on four specific domains, namely the design and discovery of new molecules and materials, the modeling of processes, the prediction of chemical reactions/retrosynthesis and the support for sensorial analysis. This review is further completed by general guidelines for the selection of an appropriate ML technique given the characteristics of each problem and by a critical discussion of several key issues associated with the development of ML modeling approaches. Accordingly, this paper may serve both the experienced researcher in the field as well as the newcomer.
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Riese J, Grünewald M. Challenges and Opportunities to Enhance Flexibility in Design and Operation of Chemical Processes. CHEM-ING-TECH 2020. [DOI: 10.1002/cite.202000057] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Julia Riese
- Ruhr-University Bochum Faculty for Mechanical Engineering Laboratory of Fluid Separations Universitätsstraße 150 44801 Bochum Germany
| | - Marcus Grünewald
- Ruhr-University Bochum Faculty for Mechanical Engineering Laboratory of Fluid Separations Universitätsstraße 150 44801 Bochum Germany
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Franzoi RE, Menezes BC, Kelly JD, Gut JAW, Grossmann IE. Cutpoint Temperature Surrogate Modeling for Distillation Yields and Properties. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c02868] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Robert E. Franzoi
- Department of Chemical Engineering, University of São Paulo, São Paulo, Brazil
| | - Brenno C. Menezes
- Division of Engineering Management and Decision Sciences, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Jeffrey D. Kelly
- Industrial Algorithms Ltd., 15 St. Andrews Road, Toronto, Canada
| | - Jorge A. W. Gut
- Department of Chemical Engineering, University of São Paulo, São Paulo, Brazil
| | - Ignacio E. Grossmann
- Chemical Engineering Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213 United States
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8
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Heese R, Nies J, Bortz M. Some Aspects of Combining Data and Models in Process Engineering. CHEM-ING-TECH 2020. [DOI: 10.1002/cite.202000007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Raoul Heese
- Fraunhofer Center for Machine Learning and Fraunhofer Institute for Industrial Mathematics (ITWM) Fraunhofer-Platz 1 67663 Kaiserslautern Germany
| | - Julia Nies
- Fraunhofer Center for Machine Learning and Fraunhofer Institute for Industrial Mathematics (ITWM) Fraunhofer-Platz 1 67663 Kaiserslautern Germany
| | - Michael Bortz
- Fraunhofer Center for Machine Learning and Fraunhofer Institute for Industrial Mathematics (ITWM) Fraunhofer-Platz 1 67663 Kaiserslautern Germany
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