Unveiling the relation between multiple chemical products and process conditions for trichloroethylene and perchloroethylene production via catalysis network analysis†
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引用次数: 0
Abstract
Trichloroethylene (TRI) and perchloroethylene (PER) are widely-produced in the chemical industry and used as solvents, varnishes, degreasers, and dry cleaning chemicals that involve complex process conditions. Data science and network analysis are used in order to unveil relationships between reactants, process conditions, and selectivities of select products with the aim to improve production efficiency. Data visualization and machine learning reveal the sets of conditions that have positive and inverse relations with TRI and PER selectivities, while transforming the data into networks reveals which sets of experimental conditions correlate with desired outcomes. Thus, it becomes possible to tailor experimental conditions in order to increase desired selectivities while avoiding production of undesirable selectivities.
三氯乙烯（TRI）和全氯乙烯（PER）在化学工业中广泛生产，用作溶剂、清漆、脱脂剂和干洗化学品，涉及复杂的工艺条件。数据科学和网络分析被用来揭示反应物、工艺条件和特定产品选择性之间的关系，从而提高生产效率。数据可视化和机器学习揭示了与 TRI 和 PER 选择性呈正反关系的条件集，而将数据转化为网络则揭示了哪些实验条件集与预期结果相关。因此，有可能对实验条件进行调整，以提高理想的选择性，同时避免产生不理想的选择性。
期刊介绍：
A multidisciplinary journal focusing on cutting edge research across all fundamental science and technological aspects of catalysis.
Editor-in-chief: Bert Weckhuysen
Impact factor: 5.0
Time to first decision (peer reviewed only): 31 days
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