基于模型的甲烷氧化偶联催化剂筛选和优化实验设计

IF 3 Q2 ENGINEERING, CHEMICAL
Anjana Puliyanda
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引用次数: 0

摘要

甲烷(OCM)氧化偶联生成作为平台化学品的乙烷和乙烯(C2 化合物)涉及复杂的化学反应,既有气相反应,也有催化剂表面反应,结果是以牺牲 C2 选择性为代价的产物分布。这项研究利用各种混合金属氧化物在不同反应条件(温度、接触时间和反应物流速)下的实验数据来训练随机森林回归器,从而预测甲烷转化率和 C2 选择性(关键性能指标 (KPI))。经动力学验证的随机森林模型可用于确定每种催化剂的最佳条件,从而最大限度地提高 C2 产率。通过特征重要性对回归器可解释性的研究发现,除了反应条件外,金属和支撑物的选择对 C2 选择性预测也至关重要,而甲烷转化率的预测则主要受反应条件的影响。机器学习(ML)回归器被用作动力学替代物,通过多目标优化程序为每种催化剂找到选择性和转化率均最大化的最佳反应条件位置。预计催化剂的最大 C2 产率平均可提高 15%。利用最优位置分析催化剂与常用的 OCM 催化剂 Mn-Na2WO4/SiO2 的关系,可以消除工艺条件中的变化,从而揭示基于金属和载体固有特性的独特模式。此外,在静态多目标优化例程和自适应贝叶斯例程中,使用 ML 代理对催化剂描述符和反应条件的决策空间进行了优化,以获得高 C2 收率。提出了各种支撑物上的过渡金属氧化物,但没有提出对应的镧系氧化物。该框架有望应用于材料加速平台,在该平台中,考虑影响下游关键绩效指标的多尺度现象至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Model-based catalyst screening and optimal experimental design for the oxidative coupling of methane

The oxidative coupling of methane (OCM) to produce ethane and ethylene (C2 compounds) as platform chemicals involves complex chemistry with reactions both in the gas phase and on the catalyst surface, resulting in a distribution of products at the expense of C2 selectivity. This work uses experimental data from a variety of mixed metal oxides on supports at different reaction conditions (temperature, contact time, and reactant flow rates) to train a random forest regressor that predicts methane conversion and C2 selectivity (key performance indicators (KPIs)). The kinetically validated random forest models are deployed to locate optimal conditions that maximize C2 yield for each of the catalysts. Investigating the regressor interpretability via feature importance reveals that the choice of metals and support are crucial to C2 selectivity predictions in addition to the reaction conditions, while the predictions of methane conversion are largely governed by the reaction conditions. The machine learning (ML) regressor is used as a kinetic surrogate to find a locus of optimal reaction conditions that maximize both selectivity-conversion for each of the catalysts via a multi-objective optimization routine. The maximum C2 yields for catalysts are projected to be improved by 15% on average. Analyzing the catalysts with respect to a popular OCM catalyst, Mn-Na2WO4/SiO2, using the optimal locus eliminates variability in the process conditions to reveal distinct patterns based on intrinsic properties of metals and supports. Further, the decision space with catalyst descriptors and reaction conditions is optimized for high C2 yields using the ML surrogate, in a static multi-objective optimization routine, and an adaptive Bayesian routine, where the latter was found to have a wider field focus in proposing catalyst formulations and reaction conditions. Transition metal oxides on a variety of supports were proposed but not their lanthanide oxide counterparts. The framework has the potential to lend itself to materials acceleration platforms where it is crucial to consider multi-scale phenomena that impact downstream KPIs.

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