| Title |
AI in Process Industries - Current Status and Future Prospects |
| ID_Doc |
8032 |
| Authors |
Bortz, M; Dadhe, K; Engell, S; Gepert, V; Kockmann, N; Müller-Pfefferkorn, R; Schindler, T; Urbas, L |
| Title |
AI in Process Industries - Current Status and Future Prospects |
| Year |
2023 |
| Published |
Chemie Ingenieur Technik, 95, 7 |
| DOI |
10.1002/cite.202200247 |
| Abstract |
The chemical industry is one of the key industrial sectors in Germany and at the same time one of the largest consumers of energy and raw materials. A successful energy transition and the development of a circular economy can only succeed if they are actively supported and shaped by the chemical industry - through the redesign of existing production processes and the exploration and implementation of new process routes. The challenge is to realize this transformation within a very short time and for many production processes, whereby a much larger number of process routes must be explored. Digital technologies are key to master this transformation towards more sustainability, climate, and environmental protection. The KEEN project aims to explore and leverage artificial intelligence (AI) opportunities in process industry. The newly developed AI methods are tested wherever possible in real working environments and production plants to prove the economic benefit, applicability, and reliability of the methods and technologies. |
| Author Keywords |
Data handling; Engineering of plants and processes; Optimization of operations; Self-optimizing plants; Surrogate models |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000970499500001 |
| WoS Category |
Engineering, Chemical |
| Research Area |
Engineering |
| PDF |
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cite.202200247
|