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FlowMat：一个用于使用基于物理和机器学习方法对流动反应器进行建模的工具箱，用于模块化模拟、参数识别和反应器优化。

FlowMat: a toolbox for modeling flow reactors using physics-based and machine learning approaches for modular simulation, parameter identification, and reactor optimization.

作者信息

Knoll Sebastian, Silber Klara, Williams Jason D, Sagmeister Peter, Hone Christopher A, Kappe C Oliver, Steinberger Martin, Horn Martin

机构信息

Institute of Automation and Control, Graz University of Technology Inffeldgasse 21b 8010 Graz Austria

Center for Continuous Synthesis and Processing (CCFLOW), Research Center Pharmaceutical Engineering GmbH (RCPE) Inffeldgasse 13 8010 Graz Austria

出版信息

RSC Adv. 2025 Sep 12;15(40):33278-33296. doi: 10.1039/d5ra06173c. eCollection 2025 Sep 11.

DOI:10.1039/d5ra06173c
PMID:40949870
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12428333/
Abstract

This paper introduces a versatile, open-source MATLAB/Simulink toolbox for modeling and optimizing flow reactors. The toolbox features a modular architecture and an intuitive drag-and-drop interface, supporting a range of different modeling approaches, including physics-based, data-driven, and hybrid models such as physics-informed neural networks. We detail the toolbox's implementation and demonstrate its capabilities through real-world applications, including the simulation of flow reactors, identification of reaction parameters using experimental data (, transient data), and optimization of reactor operating points and configurations. Experimental validations illustrate the practical applicability and effectiveness of the toolbox, making it a valuable resource for researchers and engineers in the field with the potential of reducing the cost and time required for parameter determination and reactor optimization.

摘要

本文介绍了一个用于流动反应器建模和优化的通用开源MATLAB/Simulink工具箱。该工具箱具有模块化架构和直观的拖放界面，支持一系列不同的建模方法，包括基于物理的、数据驱动的以及混合模型，如物理信息神经网络。我们详细介绍了该工具箱的实现，并通过实际应用展示了其功能，包括流动反应器的模拟、使用实验数据（瞬态数据）识别反应参数以及优化反应器操作点和配置。实验验证说明了该工具箱的实际适用性和有效性，使其成为该领域研究人员和工程师的宝贵资源，有可能降低参数确定和反应器优化所需的成本和时间。

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本文引用的文献

1
A field guide to flow chemistry for synthetic organic chemists.合成有机化学家的流动化学实地指南。
Chem Sci. 2023 Mar 15;14(16):4230-4247. doi: 10.1039/d3sc00992k. eCollection 2023 Apr 26.
2
Digitising chemical synthesis in automated and robotic flow.在自动化和机器人流动中实现化学合成的数字化。
Chem Sci. 2020 Oct 7;11(44):11973-11988. doi: 10.1039/d0sc04250a.
3
When robotics met fluidics.当机器人技术遇上流体技术。
Lab Chip. 2020 Feb 21;20(4):709-716. doi: 10.1039/c9lc01042d. Epub 2020 Jan 2.
4
A robotic platform for flow synthesis of organic compounds informed by AI planning.基于人工智能规划的有机化合物流动合成机器人平台。
Science. 2019 Aug 9;365(6453). doi: 10.1126/science.aax1566.
5
Continuous flow chemistry: where are we now? Recent applications, challenges and limitations.连续流动化学：我们现在在哪里？最新应用、挑战和局限性。
Chem Commun (Camb). 2018 Dec 11;54(99):13894-13928. doi: 10.1039/c8cc07427e.
6
Automating multistep flow synthesis: approach and challenges in integrating chemistry, machines and logic.多步流动合成的自动化：整合化学、机器和逻辑方面的方法与挑战。
Beilstein J Org Chem. 2017 May 19;13:960-987. doi: 10.3762/bjoc.13.97. eCollection 2017.
7
The Hitchhiker's Guide to Flow Chemistry ∥.《流动化学漫游指南》。
Chem Rev. 2017 Sep 27;117(18):11796-11893. doi: 10.1021/acs.chemrev.7b00183. Epub 2017 Jun 1.
8
"Batch" kinetics in flow: online IR analysis and continuous control.流中“批量”动力学：在线红外分析和连续控制。
Angew Chem Int Ed Engl. 2014 Jan 7;53(2):470-3. doi: 10.1002/anie.201306468. Epub 2013 Nov 29.