Hone Christopher A, Holmes Nicholas, Akien Geoffrey R, Bourne Richard A, Muller Frans L
Institute of Process Research and Development , School of Chemistry and School of Chemical and Process Engineering , University of Leeds , LS2 9JT , UK . Email:
Department of Chemistry , Lancaster University , Lancaster , LA1 4YB , UK.
React Chem Eng. 2017 Apr 1;2(2):103-108. doi: 10.1039/c6re00109b. Epub 2016 Oct 3.
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 , greatly reducing scale up risks.
如今,动力学模型的生成仍被视为一项资源密集型的专业活动。我们报告了一种利用先进的连续流动平台从瞬态流中生成反应曲线的有效方法。多步芳香亲核取代反应的实验数据是通过反应器出口处的在线高效液相色谱法从自动线性梯度流动斜坡中收集的。使用这种方法,我们在不到3小时的运行时间内,在3种不同的入口浓度和4个温度下生成了16条曲线。动力学参数,即4个速率常数和4个活化能,拟合的不确定度小于4%。我们推导了由于分散导致的观测速率常数误差的表达式,并表明这种误差为5%或更低。大范围的操作条件使得无需分离各个反应步骤。我们的方法能够早期识别产品质量对参数变化的敏感性,并早期使用单元操作模型来识别最佳的工艺-设备组合,从而大大降低放大风险。