Updates
Here you can find the most recent research progress for the HIYIELD project.
Realized Outcomes
Completed Deliverables Report
|
Deliverable No. |
Deliverable Name |
Work Package No |
Lead Benificiary |
|---|---|---|---|
| D1.2 | Technology Watch Report M12 | WP 1 | KTH |
| D2.1 | Database of suitable scrap types as process feedstock | WP 2 | AEIF |
| D2.2 | Report on defined Scenarios and Improvements | WP 2 | KTH |
| D2.3 | Database on demands, limitations, online data availability on scrap usage in steelmaking | WP 2 | FENO |
| D3.1 | Report on the implementation of the infrastructure at demo plant | WP 3 | MIN |
| D3.2 | Report on augmented reality visualization for direct plant operator | WP 3 | KTH |
| D3.3 | Software and algorithms for scrap identification developed | WP 3 | SAG/SHS |
| D4.1 | Report on sorting, upgrading and use of postconsumer scraps | WP 4 | AEIF |
| D4.2 | Report on implementation of DL-scrap identification | WP 4 | FENO |
| D4.3 | Report on implementation high-speed liquid steel sampling&analysis | WP 4 | MIN |
| D7.1 | Plan for Communication, Dissemination and Exploitation activities - M6 | WP 7 | KTH |
| D7.2 | Plan for Communication, Dissemination and Exploitation activities - M18 | WP 7 | KTH |
| D7.5 | Data Management Plan - M6 | WP 7 | KTH |
Achieved Milestones
|
Milestone No |
Milestone Name |
Work Package No |
Lead Benificiary |
|---|---|---|---|
| 1 | Scenarios defined | WP 2 | FENO |
| 2 | Infrastructure prepared | WP 3 | MIN |
| 3 | Methods implemented | WP 4 | SAG/SHS |
Newsletters
Publications
- DOES - A multimodal dataset for supervised and unsupervised analysis of steel scrap
- CLRiuS: Contrastive Learning for intrinsically unordered Steel Scrap
- Machine learning approach for predicting tramp elements in the basic oxygen furnace based on the compiled steel scrap mix