3 years ago

Fast property prediction in an industrial rubber mixing process with local ELM model

Fast property prediction in an industrial rubber mixing process with local ELM model
Yi Liu, Zengliang Gao, Weiya Jin
Online property prediction in industrial rubber mixing processes is not an easy task. An efficient data-driven prediction model is developed in this work. The regularized extreme learning machine (RELM) is utilized as the fundamental soft sensor model. To better capture distinguished characteristics in multiple recipes and operating modes, a just-in-time RELM modeling method is developed. The number of hidden neurons and the value of regularization parameter of the just-in-time RELM model can be efficiently selected using a fast leave-one-out strategy. Consequently, without the time-consuming laboratory analysis process, the Mooney viscosity can be online predicted once a mixing batch has been discharged. The industrial Mooney viscosity prediction results show its better prediction performance in comparison with traditional approaches. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 45391.

Publisher URL: http://onlinelibrary.wiley.com/resolve/doi

DOI: 10.1002/app.45391

You might also like
Never Miss Important Research

Researcher is an app designed by academics, for academics. Create a personalised feed in two minutes.
Choose from over 15,000 academics journals covering ten research areas then let Researcher deliver you papers tailored to your interests each day.

  • Download from Google Play
  • Download from App Store
  • Download from AppInChina

Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.