This study investigates VO_x/MgO-γAl₂O₃ in the oxidative cracking ofn-hexane to produce light olefins in the absence of gas-phase oxygen. The catalysts were prepared with varying mass ratios of MgO/γAl₂O₃(1:2, 1:1, and 2:1), while the VO_x loading was maintained at 10 wt %. Among the synthesized catalysts, VO_x/MgO-γAl₂O₃ 1:1 showed superior catalytic activity, with 89.1% n-hexane conversion and 92.6% light olefin selectivity. Introducing an appropriate amount of MgO enhanced the dispersion of VO_x active species, balanced the acidity, and suppressed the oxidation of hydrocarbons. Additionally, a machine learning model was developed to predict oxidative cracking products’ yields. The model, based on 44 data points from this study and literature, predicted n-hexane conversion, olefin yield, carbon oxide yield, methane yield, and paraffin yield using catalyst formulations, temperature, and time as inputs. The model showed a high correlation (R²) of 0.99 and RMSE values between 1.6 and 8.5, highlighting its strong predictive capability.

中文翻译：

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使用晶格氧在 VOx/MgO-γAl2O3 上氧化裂解正己烷制轻质烯烃：性能评估和机器学习建模


本研究研究了 VO_x/MgO-γAl₂O₃ 在不存在气相氧的情况下，正己烷氧化裂解生成轻质烯烃。用不同质量比的 MgO/γAl₂O₃（1：2、1：1 和 2：1）制备催化剂，而 VO_x 负载量保持在 10 wt %。在合成的催化剂中，VO_x/MgO-γAl₂O₃ 1：1 表现出优异的催化活性，具有 89.1% 的正己烷转化率和 92.6% 的轻烯烃选择性。引入适量的 MgO 增强了 VO_x 活性物质的分散性，平衡了酸度，并抑制了碳氢化合物的氧化。此外，还开发了一种机器学习模型来预测氧化裂化产物的产量。该模型基于本研究和文献的 44 个数据点，使用催化剂配方、温度和时间作为输入来预测正己烷转化率、烯烃产率、氧化碳产率、甲烷产率和石蜡产率。该模型显示出 0.99 的高相关性 （R²），RMSE 值在 1.6 和 8.5 之间，突出了其强大的预测能力。