Name
Title | ||
|---|---|---|
Neural prediction of product quality based on pilot paper machine process measurements |
Abstract | ||
|---|---|---|
We describe a multilayer perceptron model to predict the laboratory measurements of paper quality using the instantaneous state of the papermaking production process. Actual industrial data from a pilot paper machine was used. The final model met its goal accuracy 95.7% of the time at best (tensile index quality) and 66.7% at worst (beta formation). We anticipate usage possibilities in lowering machine prototyping expenses, and possibly in quality control at production sites. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-20282-7_25 | ICANNGA (1) |
Keywords | Field | DocType |
final model,multilayer perceptron model,papermaking production process,tensile index quality,product quality,pilot paper machine,quality control,neural prediction,production site,pilot paper machine process,actual industrial data,beta formation,paper quality,indexation,production process,prediction,multilayer perceptron | Papermaking,Simulation,Computer science,Control engineering,Scheduling (production processes),Paper machine,Multilayer perceptron,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
6593 | 0302-9743 | 2 |
PageRank | References | Authors |
0.40 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paavo Nieminen | 1 | 12 | 3.40 |
| Tommi Kärkkäinen | 2 | 197 | 29.59 |
| Kari Luostarinen | 3 | 29 | 3.22 |
| Jukka Muhonen | 4 | 2 | 0.40 |