Name
Title | ||
|---|---|---|
Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems. |
Abstract | ||
|---|---|---|
•Data-driven models (DDMs) will become widespread across manufacturing.•Paramount to DDMs is the collection of an accurate set of model development data.•Process manufacturers face unique considerations and challenges in collecting data.•These points are presented in the context of the CRISP-DM framework.•This supports the development of DDMs to meet manufacturers’ requirements. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.compchemeng.2020.106881 | Computers & Chemical Engineering |
Keywords | DocType | Volume |
Data-driven models,Process resilience,Waste valorisation,Mathematical modelling,Machine learning,Industry 4.0 | Journal | 140 |
ISSN | Citations | PageRank |
0098-1354 | 1 | 0.37 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oliver J. Fisher | 1 | 1 | 0.37 |
| Nicholas James Watson | 2 | 1 | 0.37 |
| Josep Escrig | 3 | 1 | 0.37 |
| Rob Witt | 4 | 1 | 0.37 |
| Laura Porcu | 5 | 1 | 0.37 |
| Darren Bacon | 6 | 1 | 0.37 |
| Martin Rigley | 7 | 1 | 0.37 |
| Rachel L. Gomes | 8 | 1 | 0.70 |