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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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Yonge A, Gusmão GS, Fushimi R, Medford AJ. Model-Based Design of Experiments for Temporal Analysis of Products (TAP): A Simulated Case Study in Oxidative Propane Dehydrogenation. Ind Eng Chem Res 2024; 63:4756-4770. [PMID: 38525291 PMCID: PMC10958505 DOI: 10.1021/acs.iecr.3c03418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 02/15/2024] [Accepted: 02/18/2024] [Indexed: 03/26/2024]
Abstract
Temporal analysis of products (TAP) reactors enable experiments that probe numerous kinetic processes within a single set of experimental data through variations in pulse intensity, delay, or temperature. Selecting additional TAP experiments often involves an arbitrary selection of reaction conditions or the use of chemical intuition. To make experiment selection in TAP more robust, we explore the efficacy of model-based design of experiments (MBDoE) for precision in TAP reactor kinetic modeling. We successfully applied this approach to a case study of synthetic oxidative propane dehydrogenation (OPDH) that involves pulses of propane and oxygen. We found that experiments identified as optimal through the MBDoE for precision generally reduce parameter uncertainties to a higher degree than alternative experiments. The performance of MBDoE for model divergence was also explored for OPDH, with the relevant active sites (catalyst structure) being unknown. An experiment that maximized the divergence between the three proposed mechanisms was identified and provided evidence that improved the mechanism discrimination. However, reoptimization of kinetic parameters eliminated the ability to discriminate between models. The findings yield insight into the prospects and limitations of MBDoE for TAP and transient kinetic experiments.
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Affiliation(s)
- Adam Yonge
- School
of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Gabriel S. Gusmão
- School
of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Rebecca Fushimi
- Catalysis
and Transient Kinetics Group, Idaho National
Laboratory, Idaho
Falls, Idaho 83415, United States
| | - Andrew J. Medford
- School
of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
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3
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Casas-Orozco D, Laky D, Mackey J, Reklaitis G, Nagy Z. Reaction kinetics determination and uncertainty analysis for the synthesis of the cancer drug lomustine. Chem Eng Sci 2023; 275:118591. [PMID: 38179266 PMCID: PMC10765472 DOI: 10.1016/j.ces.2023.118591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
Fast and reliable model development frameworks are required to support current trends in modernization of pharmaceutical processing, promoting the use of digital platforms to assist process design and operation. In this work, we use a parameter estimation framework built into the PharmaPy library to determine rate parameters and uncertainty regions of different mechanistic and semi-empirical kinetic expressions for the synthesis of the drug lomustine. The parameter estimation procedure was complemented by identifiability analysis, resulting in simplified reaction mechanisms. Comparison of parameters and their uncertainty in process design was demonstrated through design space analysis, showing important differences in model prediction and the extent of their corresponding design spaces. The results of this work can serve to analyze lomustine manufacturing processes that include separation and isolation steps, where parametric sensitivity is expected to propagate along the manufacturing line and impact process feasible operation, and attainment of critical quality attributes of the product.
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4
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Duarte BPM, Atkinson AC, Granjo JFO, Oliveira NMC. Optimal Design of Experiments for Implicit Models. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2020.1862670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Belmiro P. M. Duarte
- Department of Chemical and Biological Engineering, Instituto Politécnico de Coimbra, Instituto Superior de Engenharia de Coimbra, Coimbra, Portugal
- CIEPQPF, Department of Chemical Engineering, University of Coimbra, Coimbra, Portugal
| | | | - José F. O. Granjo
- CIEPQPF, Department of Chemical Engineering, University of Coimbra, Coimbra, Portugal
| | - Nuno M. C. Oliveira
- CIEPQPF, Department of Chemical Engineering, University of Coimbra, Coimbra, Portugal
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5
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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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6
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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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7
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Model-Based Design of Experiments for High-Dimensional Inputs Supported by Machine-Learning Methods. Processes (Basel) 2021. [DOI: 10.3390/pr9030508] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Algorithms that compute locally optimal continuous designs often rely on a finite design space or on the repeated solution of difficult non-linear programs. Both approaches require extensive evaluations of the Jacobian Df of the underlying model. These evaluations are a heavy computational burden. Based on the Kiefer-Wolfowitz Equivalence Theorem, we present a novel design of experiments algorithm that computes optimal designs in a continuous design space. For this iterative algorithm, we combine an adaptive Bayes-like sampling scheme with Gaussian process regression to approximate the directional derivative of the design criterion. The approximation allows us to adaptively select new design points on which to evaluate the model. The adaptive selection of the algorithm requires significantly less evaluations of Df and reduces the runtime of the computations. We show the viability of the new algorithm on two examples from chemical engineering.
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Waldron C, Pankajakshan A, Quaglio M, Cao E, Galvanin F, Gavriilidis A. Model-based design of transient flow experiments for the identification of kinetic parameters. REACT CHEM ENG 2020. [DOI: 10.1039/c9re00342h] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Rapid and precise estimation of kinetic parameters is facilitated by transient flow experiments designed using model-based design of experiments.
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Affiliation(s)
- Conor Waldron
- Dept of Chemical Engineering
- University College London
- London
- UK
| | | | - Marco Quaglio
- Dept of Chemical Engineering
- University College London
- London
- UK
| | - Enhong Cao
- Dept of Chemical Engineering
- University College London
- London
- UK
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Waldron C, Pankajakshan A, Quaglio M, Cao E, Galvanin F, Gavriilidis A. Closed-Loop Model-Based Design of Experiments for Kinetic Model Discrimination and Parameter Estimation: Benzoic Acid Esterification on a Heterogeneous Catalyst. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b04089] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Conor Waldron
- Department of Chemical Engineering, University College London, London WC1E 7JE, U.K
| | - Arun Pankajakshan
- Department of Chemical Engineering, University College London, London WC1E 7JE, U.K
| | - Marco Quaglio
- Department of Chemical Engineering, University College London, London WC1E 7JE, U.K
| | - Enhong Cao
- Department of Chemical Engineering, University College London, London WC1E 7JE, U.K
| | - Federico Galvanin
- Department of Chemical Engineering, University College London, London WC1E 7JE, U.K
| | - Asterios Gavriilidis
- Department of Chemical Engineering, University College London, London WC1E 7JE, U.K
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10
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Shahmohammadi A, McAuley KB. Sequential model-based A- and V-optimal design of experiments for building fundamental models of pharmaceutical production processes. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2019.06.029] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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