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Yao X, Wang Z, Qian M, Deng Q, Sun P. Kinetic Aspects of Esterification and Transesterification in Microstructured Reactors. Molecules 2024; 29:3651. [PMID: 39125055 PMCID: PMC11314161 DOI: 10.3390/molecules29153651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2024] [Revised: 07/24/2024] [Accepted: 07/29/2024] [Indexed: 08/12/2024] Open
Abstract
Microstructured reactors offer fast chemical engineering transfer and precise microfluidic control, enabling the determination of reactions' kinetic parameters. This review examines recent advancements in measuring microreaction kinetics. It explores kinetic modeling, reaction mechanisms, and intrinsic kinetic equations pertaining to two types of microreaction: esterification and transesterification reactions involving acids, bases, or biocatalysts. The utilization of a micro packed-bed reactor successfully achieves a harmonious combination of the micro-dispersion state and the reaction kinetic characteristics. Additionally, this review presents micro-process simulation software and explores the advanced integration of microreactors with spectroscopic analyses for reaction monitoring and data acquisition. Furthermore, it elaborates on the control principles of the micro platform. The superiority of online measurement, automation, and the digitalization of the microreaction process for kinetic measurements is highlighted, showcasing the vast prospects of artificial intelligence applications.
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Affiliation(s)
- Xingjun Yao
- Shandong Provincial Key Laboratory of Chemical Energy Storage and Novel Cell Technology, School of Chemistry and Chemical Engineering, Liaocheng University, Liaocheng 252059, China
| | - Zhenxue Wang
- Shandong Provincial Key Laboratory of Chemical Energy Storage and Novel Cell Technology, School of Chemistry and Chemical Engineering, Liaocheng University, Liaocheng 252059, China
| | - Ming Qian
- Shandong Provincial Key Laboratory of Chemical Energy Storage and Novel Cell Technology, School of Chemistry and Chemical Engineering, Liaocheng University, Liaocheng 252059, China
| | - Qiulin Deng
- School of Materials Science and Engineering, Southwest University of Science and Technology, Mianyang 621010, China;
| | - Peiyong Sun
- Beijing Institute of Petrochemical Technology, Daxing District, Beijing 102617, China;
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2
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Williamson E, Brutchey RL. Using Data-Driven Learning to Predict and Control the Outcomes of Inorganic Materials Synthesis. Inorg Chem 2023; 62:16251-16262. [PMID: 37767941 PMCID: PMC10565808 DOI: 10.1021/acs.inorgchem.3c02697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Indexed: 09/29/2023]
Abstract
The design of inorganic materials for various applications critically depends on our ability to manipulate their synthesis in a rational, robust, and controllable fashion. Different from the conventional trial-and-error approach, data-driven techniques such as the design of experiments (DoE) and machine learning are an effective and more efficient way to predictably control materials synthesis. Here, we present a Viewpoint on recent progress in leveraging such techniques for predicting and controlling the outcomes of inorganic materials synthesis. We first compare how the design choice (statistical DoE vs machine learning) affects the type of control it can offer over the resulting product attributes, information elucidated, and experimental cost. These attributes are supported by discussing select case studies from the recent literature that highlight the power of these techniques for materials synthesis. The influence of experimental bias is next discussed, followed finally by our perspectives on the major challenges in the widespread implementation of predictable and controllable materials synthesis using data-driven techniques.
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Affiliation(s)
- Emily
M. Williamson
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States
| | - Richard L. Brutchey
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States
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3
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Kinetic study on the reaction routes in the oxidation of K/A oil by nitric acid with microreactors. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2022.118273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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4
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Wang J, Dowling AW. Pyomo.
DOE
: An
Open‐Source
Package for
Model‐Based
Design of Experiments in Python. AIChE J 2022. [DOI: 10.1002/aic.17813] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Jialu Wang
- Department of Chemical and Biomolecular Engineering University of Notre Dame Notre Dame IN
| | - Alexander W. Dowling
- Department of Chemical and Biomolecular Engineering University of Notre Dame Notre Dame IN
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5
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Yonge A, Kunz MR, Gusmão GS, Fang Z, Batchu R, Fushimi R, Medford AJ. Quantifying the impact of temporal analysis of products reactor initial state uncertainties on kinetic parameters. AIChE J 2022. [DOI: 10.1002/aic.17776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Adam Yonge
- College of Engineering Georgia Institute of Technology Atlanta GA
| | - M. Ross Kunz
- Department of Biological and Chemical Processing Idaho National Laboratory Idaho Falls ID
| | | | - Zongtang Fang
- Department of Biological and Chemical Processing Idaho National Laboratory Idaho Falls ID
| | - Rakesh Batchu
- Department of Biological and Chemical Processing Idaho National Laboratory Idaho Falls ID
| | - Rebecca Fushimi
- Department of Biological and Chemical Processing Idaho National Laboratory Idaho Falls ID
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6
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7
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Villaverde AF, Pathirana D, Fröhlich F, Hasenauer J, Banga JR. A protocol for dynamic model calibration. Brief Bioinform 2022; 23:bbab387. [PMID: 34619769 PMCID: PMC8769694 DOI: 10.1093/bib/bbab387] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 08/06/2021] [Accepted: 08/29/2021] [Indexed: 12/23/2022] Open
Abstract
Ordinary differential equation models are nowadays widely used for the mechanistic description of biological processes and their temporal evolution. These models typically have many unknown and nonmeasurable parameters, which have to be determined by fitting the model to experimental data. In order to perform this task, known as parameter estimation or model calibration, the modeller faces challenges such as poor parameter identifiability, lack of sufficiently informative experimental data and the existence of local minima in the objective function landscape. These issues tend to worsen with larger model sizes, increasing the computational complexity and the number of unknown parameters. An incorrectly calibrated model is problematic because it may result in inaccurate predictions and misleading conclusions. For nonexpert users, there are a large number of potential pitfalls. Here, we provide a protocol that guides the user through all the steps involved in the calibration of dynamic models. We illustrate the methodology with two models and provide all the code required to reproduce the results and perform the same analysis on new models. Our protocol provides practitioners and researchers in biological modelling with a one-stop guide that is at the same time compact and sufficiently comprehensive to cover all aspects of the problem.
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Affiliation(s)
- Alejandro F Villaverde
- Universidade de Vigo, Department of Systems Engineering & Control, Vigo 36310, Galicia, Spain
| | - Dilan Pathirana
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn 53115, Germany
| | - Fabian Fröhlich
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany
| | - Jan Hasenauer
- Center for Mathematics, Technische Universität München, Garching 85748, Germany
- Harvard Medical School, Cambridge, MA 02115, USA
| | - Julio R Banga
- Bioprocess Engineering Group, IIM-CSIC, Vigo 36208, Galicia, Spain
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8
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Ouimet JA, Liu X, Brown DJ, Eugene EA, Popps T, Muetzel ZW, Dowling AW, Phillip WA. DATA: Diafiltration Apparatus for high-Throughput Analysis. J Memb Sci 2022. [DOI: 10.1016/j.memsci.2021.119743] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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9
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Taylor CJ, Manson JA, Clemens G, Taylor BA, Chamberlain TW, Bourne RA. Modern advancements in continuous-flow aided kinetic analysis. REACT CHEM ENG 2022. [DOI: 10.1039/d1re00467k] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Although kinetic analysis has traditionally been conducted in a batch vessel, continuous-flow aided kinetic analysis continues to swell in popularity.
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Affiliation(s)
- Connor J. Taylor
- Institute of Process Research and Development, School of Chemistry and School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - Jamie A. Manson
- Institute of Process Research and Development, School of Chemistry and School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - Graeme Clemens
- Chemical Development, Pharmaceutical Technology & Development, Operations, AstraZeneca, Macclesfield, UK
| | - Brian A. Taylor
- Chemical Development, Pharmaceutical Technology & Development, Operations, AstraZeneca, Macclesfield, UK
| | - Thomas W. Chamberlain
- Institute of Process Research and Development, School of Chemistry and School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - Richard A. Bourne
- Institute of Process Research and Development, School of Chemistry and School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
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Knoll S, Jusner CE, Sagmeister P, Williams JD, Hone CA, Horn M, Kappe CO. Autonomous model-based experimental design for rapid reaction development. REACT CHEM ENG 2022. [DOI: 10.1039/d2re00208f] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
To automate and democratize model-based experimental design for flow chemistry applications, we report the development of open-source software, Optipus. Reaction models are built in an iterative and automated fashion, for rapid reaction development.
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Affiliation(s)
- Sebastian Knoll
- Institute of Automation and Control, Graz University of Technology, Inffeldgasse 21b, 8010 Graz, Austria
| | - Clemens E. Jusner
- Center for Continuous Synthesis and Processing (CCFLOW), Research Center Pharmaceutical Engineering GmbH (RCPE), Inffeldgasse 13, 8010 Graz, Austria
- Institute of Chemistry, University of Graz, NAWI Graz, Heinrichstrasse 28, 8010 Graz, Austria
| | - Peter Sagmeister
- Center for Continuous Synthesis and Processing (CCFLOW), Research Center Pharmaceutical Engineering GmbH (RCPE), Inffeldgasse 13, 8010 Graz, Austria
- Institute of Chemistry, University of Graz, NAWI Graz, Heinrichstrasse 28, 8010 Graz, Austria
| | - Jason D. Williams
- Center for Continuous Synthesis and Processing (CCFLOW), Research Center Pharmaceutical Engineering GmbH (RCPE), Inffeldgasse 13, 8010 Graz, Austria
- Institute of Chemistry, University of Graz, NAWI Graz, Heinrichstrasse 28, 8010 Graz, Austria
| | - Christopher A. Hone
- Center for Continuous Synthesis and Processing (CCFLOW), Research Center Pharmaceutical Engineering GmbH (RCPE), Inffeldgasse 13, 8010 Graz, Austria
- Institute of Chemistry, University of Graz, NAWI Graz, Heinrichstrasse 28, 8010 Graz, Austria
| | - Martin Horn
- Institute of Automation and Control, Graz University of Technology, Inffeldgasse 21b, 8010 Graz, Austria
| | - C. Oliver Kappe
- Center for Continuous Synthesis and Processing (CCFLOW), Research Center Pharmaceutical Engineering GmbH (RCPE), Inffeldgasse 13, 8010 Graz, Austria
- Institute of Chemistry, University of Graz, NAWI Graz, Heinrichstrasse 28, 8010 Graz, Austria
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11
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Thakkar A, Johansson S, Jorner K, Buttar D, Reymond JL, Engkvist O. Artificial intelligence and automation in computer aided synthesis planning. REACT CHEM ENG 2021. [DOI: 10.1039/d0re00340a] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
In this perspective we deal with questions pertaining to the development of synthesis planning technologies over the course of recent years.
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Affiliation(s)
- Amol Thakkar
- Hit Discovery
- Discovery Sciences
- R&D
- AstraZeneca
- Gothenburg
| | | | - Kjell Jorner
- Early Chemical Development
- Pharmaceutical Sciences
- R&D
- AstraZeneca
- Macclesfield
| | - David Buttar
- Early Chemical Development
- Pharmaceutical Sciences
- R&D
- AstraZeneca
- Macclesfield
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry
- University of Bern
- 3012 Bern
- Switzerland
| | - Ola Engkvist
- Hit Discovery
- Discovery Sciences
- R&D
- AstraZeneca
- Gothenburg
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