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OpenFlowChem – a platform for quick, robust and flexible automation and self-optimisation of flow chemistry


Flow chemistry is time-saver in the laboratory and a cost-saver in industry partly because of automation and autonomous operation. Nevertheless, a batch process is often preferred over a flow counterpart because setting up the autonomous operation may take a lot of time. In the paper, we propose a novel open-access OpenFlowChem platform based on Labview for process automation. The platform is optimized for a quick system setup, reconfiguration and high flexibility. The platform is demonstrated in three examples: autonomous operation with an automatic stepwise program, proportional–integral–derivative (PID) control and self-optimization. In the first example, the system automatically executed a reaction program defined in a spreadsheet file to study reversibility of a Pd/SiO2 catalyst poisoning with quinoline in the reaction of alkyne semihydrogenation. The addition of quinoline increased alkene selectivity and reduced the catalyst activity, but the time required to remove the catalyst poison varied by a factor of 10 and depended on the poison concentration. In the second example, a PID controller adjusted the nitrobenzene concentration in a hydrogenation reaction to compensate for catalyst deactivation and a disturbance in process parameters. The PID controller kept constant the hydrogen consumption determined by an inline optical liquid sensor. In the third example, the product yield in alkyne semihydrogention was self-optimized adjusting the flow rates of the substrate, the catalyst poison (quinoline) and the solvent in a tube reactor coated with a 5 wt% Pd/SiO2 catalyst. As a result, the alkene yield reached 96.5%.

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Publication details

The article was received on 20 Mar 2018, accepted on 07 Aug 2018 and first published on 09 Aug 2018

Article type: Paper
DOI: 10.1039/C8RE00046H
Citation: React. Chem. Eng., 2018, Accepted Manuscript
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    OpenFlowChem – a platform for quick, robust and flexible automation and self-optimisation of flow chemistry

    N. Cherkasov, Y. Bai, A. Exposito and E. V. Rebrov, React. Chem. Eng., 2018, Accepted Manuscript , DOI: 10.1039/C8RE00046H

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