Flow chemistry is a 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 this paper, we propose a novel open-access OpenFlowChem platform based on LabVIEW for process automation, control and monitoring. The platform is optimized for 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 the 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 semihydrogenation 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%.

中文翻译：

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OpenFlowChem –快速，强大和灵活的自动化流程化学和自我优化的平台†

流化学在实验室中既节省时间，又在工业上节省成本，部分原因是自动化和自主操作。尽管如此，与建立流程相比，批处理通常更可取，因为建立自主操作可能会花费很多时间。在本文中，我们提出了一种基于LabVIEW的新型开放访问OpenFlowChem平台，用于过程自动化，控制和监视。该平台针对快速系统设置，重新配置和高度灵活性进行了优化。该平台通过三个示例进行演示：具有自动逐步程序的自主操作，比例积分微分（PID）控制和自我优化。在第一个示例中，系统自动执行电子表格文件中定义的反应程序，以研究Pd / SiO ₂的可逆性催化剂在炔烃半氢化反应中与喹啉中毒。喹啉的添加增加了烯烃的选择性并降低了催化剂的活性，但是去除催化剂毒物所需的时间变化了十倍，并且取决于毒物浓度。在第二个示例中，PID控制器调整了氢化反应中的硝基苯浓度，以补偿催化剂的失活和工艺参数的干扰。PID控制器使在线光学液体传感器确定的氢气消耗保持恒定。在第三个实例中，炔烃半氢化反应中的产物收率是自优化的，从而调整了涂覆有5 wt％Pd / SiO ₂的管式反应器中的底物，催化剂毒物（喹啉）和溶剂的流速。催化剂。结果，烯烃的收率达到96.5％。