Machine learning and process systems engineering for sustainable chemical processes-A short review

被引:1
|
作者
Torres, Ana Ines [1 ]
Ferreira, Jimena [2 ]
Pedemonte, Martin [2 ,3 ]
机构
[1] Carnegie Mellon Univ, Dept Chem Engn, 5000 Forbes Ave, Pittsburgh, PA 15213 USA
[2] Univ Republica, Fac Ingn, Julio Herrera y Reissig 565, Montevideo 11300, Uruguay
[3] PEDECIBA Programa Desarrollo Ciencias Basicas, Montevideo, Uruguay
基金
美国安德鲁·梅隆基金会;
关键词
Machine learning; Sustainability; Chemical process systems; ENVIRONMENTAL-IMPACT; PRACTICAL TUTORIAL; ALGORITHMS; MODELS;
D O I
10.1016/j.cogsc.2024.100982
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
This work provides an overview of recent applications of machine learning (ML) to process systems engineering problems related to sustainability. The review is organized by the type of ML problem being solved: regression, classification, and clustering. For each type of problem, we provide references that cover pertinent applications. The review targets a reader interested in learning where to educate themselves on the main algorithms for each type of ML problem, and where to get relevant examples. The article ends with a brief discussion of the current limitations of ML tools and good practice suggestions. optimization systems However, advancement not cation showing nized being sustainability methods/algorithms
引用
收藏
页数:6
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