Today, the generation of kinetic models is still seen as a resource intensive and specialised activity. We report an efficient method of generating reaction profiles from transient flows using a state-of-the-art continuous-flow platform. Experimental data for multistep aromatic nucleophilic substitution reactions are collected from an automated linear gradient flow ramp with online HPLC at the reactor outlet. Using this approach, we generated 16 profiles, at 3 different inlet concentrations and 4 temperatures, in less than 3 hours run time. The kinetic parameters, 4 rate constants and 4 activation energies were fitted with less than 4% uncertainty. We derived an expression for the error in the observed rate constants due to dispersion and showed that such error is 5% or lower. The large range of operational conditions prevented the need to isolate individual reaction steps. Our approach enables early identification of the sensitivity of product quality to parameter changes and early use of unit operation models to identify optimal process-equipment combinations in silico, greatly reducing scale up risks.

中文翻译：

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从瞬态流量数据快速生成多步动力学模型。

时至今日，动力学模型的生成仍被视为一种资源密集的专业活动。我们报告了一种使用最新的连续流平台从瞬时流生成反应曲线的有效方法。多步芳族亲核取代反应的实验数据是通过在反应器出口使用在线HPLC的自动线性梯度流量梯度收集的。使用这种方法，我们在不到3小时的运行时间内就在3种不同的入口浓度和4种温度下生成了16个分布图。动力学参数，4个速率常数和4个活化能的拟合不确定度小于4％。我们得出了由于色散引起的观察到的速率常数误差的表达式，并表明该误差为5％或更低。大范围的操作条件使得无需分离各个反应步骤。我们的方法可以及早识别产品质量对参数变化的敏感性，并及早使用单元操作模型来识别计算机上的最佳工艺-设备组合，从而大大降低了扩大生产规模的风险。