Artificial Intelligence in Process Engineering
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....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-06-01
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| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.202000261 |
| _version_ | 1818742241635598336 |
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| author | Christoph Thon Benedikt Finke Arno Kwade Carsten Schilde |
| author_facet | Christoph Thon Benedikt Finke Arno Kwade Carsten Schilde |
| author_sort | Christoph Thon |
| collection | DOAJ |
| description | 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 postprocessing 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. |
| first_indexed | 2024-12-18T02:09:23Z |
| format | Article |
| id | doaj.art-96bc1e14d4234e0e8d3f740e53415196 |
| institution | Directory Open Access Journal |
| issn | 2640-4567 |
| language | English |
| last_indexed | 2024-12-18T02:09:23Z |
| publishDate | 2021-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj.art-96bc1e14d4234e0e8d3f740e534151962022-12-21T21:24:31ZengWileyAdvanced Intelligent Systems2640-45672021-06-0136n/an/a10.1002/aisy.202000261Artificial Intelligence in Process EngineeringChristoph Thon0Benedikt Finke1Arno Kwade2Carsten Schilde3Institute for Particle Technology (iPAT) Technische Universität Braunschweig Volkmaroder Str. 5 Braunschweig D-38104 GermanyInstitute for Particle Technology (iPAT) Technische Universität Braunschweig Volkmaroder Str. 5 Braunschweig D-38104 GermanyInstitute for Particle Technology (iPAT) Technische Universität Braunschweig Volkmaroder Str. 5 Braunschweig D-38104 GermanyInstitute for Particle Technology (iPAT) Technische Universität Braunschweig Volkmaroder Str. 5 Braunschweig D-38104 GermanyIn 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 postprocessing 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.https://doi.org/10.1002/aisy.202000261artificial intelligencehybrid modelingmechanistic modelingpredictive modelingprocess engineering |
| spellingShingle | Christoph Thon Benedikt Finke Arno Kwade Carsten Schilde Artificial Intelligence in Process Engineering Advanced Intelligent Systems artificial intelligence hybrid modeling mechanistic modeling predictive modeling process engineering |
| title | Artificial Intelligence in Process Engineering |
| title_full | Artificial Intelligence in Process Engineering |
| title_fullStr | Artificial Intelligence in Process Engineering |
| title_full_unstemmed | Artificial Intelligence in Process Engineering |
| title_short | Artificial Intelligence in Process Engineering |
| title_sort | artificial intelligence in process engineering |
| topic | artificial intelligence hybrid modeling mechanistic modeling predictive modeling process engineering |
| url | https://doi.org/10.1002/aisy.202000261 |
| work_keys_str_mv | AT christophthon artificialintelligenceinprocessengineering AT benediktfinke artificialintelligenceinprocessengineering AT arnokwade artificialintelligenceinprocessengineering AT carstenschilde artificialintelligenceinprocessengineering |