Catalyst design is key to the improvement of chemical process efficiency. The required work for the development of new catalysts can be supported through the proper application of artificial intelligence to identify optimal compositions. A generic methodology for the application of machine learning to catalysis research is therefore outlined in this work. The catalytic oxidation of SO₂ was used to exemplify the first iteration of this methodology. 1784 data points from 31 published papers were compiled into a databank. The inlet SO₂ concentration ranged from 0 to 66 mol%. An artificial neural network (ANN) was trained on the databank in order to predict SO₂ conversion based on the catalyst composition and the reactor operating conditions (temperature, pressure, catalyst mass: volumetric flowrate ratio (w/v), and feed composition). The model achieved a root‐mean‐square error of 6.6%. A preliminary screening step identified 3:1 V‐Mg/SiO₂ catalysts as exhibiting high conversion at 648 K. A multi‐objective optimization was then performed on a single catalyst to identify solutions exhibiting high conversion and high productivity at 648 K while minimizing the catalyst cost. The optimal solution was predicted to be a 2.9 wt% V/0.2 wt% Mg/SiO₂ catalyst operating at a w/v of 7.49 kg‐cat · s/m³ STP, achieving 100% SO₂ conversion with a material cost among the bottom third of cost values. Artificial intelligence can then be employed to extract useful knowledge from published catalytic data and orient future search for novel catalyst development.

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使用人工智能的催化剂设计：SO2到SO3案例研究

催化剂设计是提高化学过程效率的关键。开发新催化剂所需的工作可以通过人工智能的正确应用来确定最佳组成，从而得到支持。因此，这项工作概述了将机器学习应用于催化研究的通用方法。SO ₂的催化氧化被用来举例说明该方法的第一次迭代。来自31个已发表论文的1784个数据点被汇编到一个数据库中。入口SO ₂浓度为0至66mol％。在数据库上训练了人工神经网络（ANN），以便预测SO ₂转化率取决于催化剂的组成和反应器的操作条件（温度，压力，催化剂质量：体积流量比（w / v）和进料组成）。该模型的均方根误差为6.6％。初步筛选步骤确定了3：1 V-Mg / SiO ₂催化剂在648 K时具有高转化率。然后，对单个催化剂进行了多目标优化，以识别在648 K时具有高转化率和高生产率的溶液，同时最大程度地降低了转化率。催化剂成本。最佳的溶液预计为2.9 wt％V / 0.2 wt％Mg / SiO ₂催化剂，w / v为7.49 kg-cat·s / m ³ STP，实现100％SO ₂在成本值的后三分之一中进行材料成本转换。然后，可以使用人工智能从已发布的催化数据中提取有用的知识，并为未来的新型催化剂开发寻找方向。