Artificial Intelligence in Process Engineering

被引:21
|
作者
Thon, Christoph [1 ]
Finke, Benedikt [1 ]
Kwade, Arno [1 ]
Schilde, Carsten [1 ]
机构
[1] Tech Univ Braunschweig, Inst Particle Technol iPAT, Volkmaroder Str 5, D-38104 Braunschweig, Germany
关键词
artificial intelligence; hybrid modeling; mechanistic modeling; predictive modeling; process engineering; NEURAL-NETWORKS; REPRESENTATION; MODELS; STATE;
D O I
10.1002/aisy.202000261
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In recent years, the field of Artificial Intelligence (AI) is experiencing a boom, caused by recent breakthroughs in computing power, AI techniques, and software architectures. Among the many fields being impacted by this paradigm shift, process engineering has experienced the benefits caused by AI. However, the published methods and applications in process engineering are diverse, and there is still much unexploited potential. Herein, the goal of providing a systematic overview of the current state of AI and its applications in process engineering is discussed. Current applications are described and classified according to a broader systematic. Current techniques, types of AI as well as pre-and post-processing will be examined similarly and assigned to the previously discussed applications. Given the importance of mechanistic models in process engineering as opposed to the pure black box nature of most of AI, reverse engineering strategies as well as hybrid modeling will be highlighted. Furthermore, a holistic strategy will be formulated for the application of the current state of AI in process engineering.
引用
收藏
页数:29
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