Oxygen availability was identified to play a key role in determining the product selectivity of tetralin oxidation conducted at a constant temperature and pressure in a microfluidic reactor. The current study is concerned with applying chemometrics involving regression techniques to identify a single most important parameter that directly affects oxygen availability and has a high influence on tetralin conversion (CR) and product selectivity (S). Five parameters (predictors) identified previously were the gas–liquid interfacial area (a), the length of the oxygen gas bubble (L_G), the length of the liquid slug (L_S), the two-phase superficial velocity (U_TP) and the liquid flow rate to the reactor (Q), where ‘a’ was suspected to be directly related to oxygen availability. CR and S were regressed on all the predictors by fitting separate simple linear regression (SLR) models. The decreasing order of explained variance in the outputs based on the calibration model was as follows: in CR: a² > L_G > U³_TP > L_S > Q; in S: a > L_G > U²_TP > Q. The powers of the variables indicate the respective best fits determined through evaluation of statistical performance measures. Multicollinearity issues among predictors were detected through Pearson's correlation coefficients and diagnostics like variance inflation factors (VIF) and eigenvalues of the correlation matrix. This issue was addressed through multiple linear regression (MLR) by considering a second input in addition to the best predictor from the SLR (a). Drastic changes in regression coefficient estimates and inflated standard errors rendered the coefficients of all other variables (except ‘a’) insignificant in the MLR models. The incremental contribution of ‘a’ towards improving the output variance was also confirmed through F-tests and partial correlations with the outputs, controlling for other variables as well. Thus, it could be stated with certainty that the gas–liquid interfacial area affected the outcomes the most. The findings from this study could be applied in industrial reactor design (for example – loop reactors), where the product selectivity can be controlled effectively through a higher interfacial area. In addition, through chemometrics, the reaction progress could be monitored by predicting reactant conversion and product selectivity, thereby eliminating the need for offline gas chromatographic (GC) measurements.

中文翻译：

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一种统计方法，用于处理微流体反应器操作中的预测变量之间的多重共线性，以控制液相氧化选择性†

在微流体反应器中，在恒定的温度和压力下，确定氧气的可用性在确定四氢萘氧化产物的选择性方面起着关键作用。当前的研究涉及应用化学计量学和回归技术来确定一个最重要的参数，该参数直接影响氧气的利用率，并对四氢萘转化率（CR）和产物选择性（S）产生很大影响。先前确定的五个参数（预测变量）是气液界面面积（a），氧气气泡的长度（L _G），液团的长度（L _S），两相表面速度（U _TP））和到达反应器的液体流速（Q），其中“ a ”被怀疑与氧气的供应量直接相关。通过拟合单独的简单线性回归（SLR）模型，在所有预测变量上对CR和S进行了回归。在校准模型的基础上，输出中解释方差的递减顺序如下：在CR中：a ² > L _G > U ³ _TP > L _S > Q；在S中：a > L _G > U ² _TP > Q。变量的幂表示通过评估统计绩效指标而确定的各个最佳拟合。通过Pearson的相关系数和诊断方法（例如方差膨胀因子（VIF）和相关矩阵的特征值）来检测预测变量之间的共共线性问题。通过使用多元线性回归（MLR）解决了这个问题，除了考虑了SLR的最佳预测值外，还考虑了第二项输入（a）。回归系数估计的急剧变化和夸大的标准误使所有其他变量（“ a ”除外）的系数在MLR模型中均不显着。F也证实了“ a ”对改善输出方差的增量贡献。-测试和与输出的部分相关性，还控制其他变量。因此，可以肯定地说，气液界面面积对结果的影响最大。这项研究的结果可应用于工业反应器设计（例如，环流反应器），在该设计中，可以通过更大的界面面积有效地控制产品的选择性。此外，通过化学计量学，可以通过预测反应物转化率和产物选择性来监测反应进程，从而无需离线气相色谱（GC）测量。