From stirring to mixing: artificial intelligence in the process industry
Research output: Contribution to book/conference proceedings/anthology/report › Conference contribution › Contributed › peer-review
Contributors
Abstract
The introduction of AI methods in production or production-related environments meets with resistance from operators due to their lack of relevant experience and their responsibility for plant safety. To overcome these inhibitions one requires prototypical implementations, which offer considerable benefits, meet the highest requirements for reliability, are accepted by the operating personnel, and get support from those in charge. As a result, AI technologies must be embedded into the complex IT/OT infrastructure of the companies. Traceability, maintainability and longevity must also be guaranteed. As a first step towards this, we present a concept of the demonstrator and its first results, which should make AI comprehensible by visualizing challenges and exploring possibilities in the process industry.
Details
Original language | English |
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Title of host publication | 2020 25TH IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA) |
Publisher | Wiley-IEEE Press |
Pages | 967-974 |
Number of pages | 8 |
Publication status | Published - 2020 |
Peer-reviewed | Yes |
Conference
Title | 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation |
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Abbreviated title | ETFA 2020 |
Conference number | 25 |
Duration | 8 - 11 September 2020 |
Website | |
Degree of recognition | International event |
Location | TU Wien & online |
City | Vienna |
Country | Austria |
External IDs
Scopus | 85093364861 |
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ORCID | /0000-0001-5165-4459/work/142248235 |
Keywords
Keywords
- Modular plant, Bioreactor, AI-based surrogate models, CFD-simulation, object recognition, AR-application, BUBBLE COALESCENCE, Modular plant, Bioreactor, Al-based surrogate models, CFD-simulation, object recognition, AR-application