Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats
Chemical engineers rely on models for design, research, and daily decision-making, often with potentially large financial and safety implications. Previous efforts a few decades ago to combine artificial intelligence and chemical engineering for modeling were unable to fulfill the expectations. In t...
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| Auteurs principaux: | , , , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Elsevier
2021
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| Accès en ligne: | https://doaj.org/article/5297e32d3e9d477d95ca985730c8e9e8 |
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