Cyclone Performance Prediction Using Linear Regression Techniques

  1. Marina Corral Bobadilla 1
  2. Roberto Fernandez Martinez 2
  3. Rubén Lostado Lorza 1
  4. Fátima Somovilla Gomez 1
  5. Vergara Gonzalez, Eliseo P. 1
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

  2. 2 Universidad del País Vasco/Euskal Herriko Unibertsitatea
    info

    Universidad del País Vasco/Euskal Herriko Unibertsitatea

    Lejona, España

    ROR https://ror.org/000xsnr85

Liburua:
International Joint Conference SOCO’16-CISIS’16-ICEUTE’16: San Sebastián, Spain, October 19th-21st, 2016 Proceedings
  1. Manuel Graña (coord.)
  2. José Manuel López-Guede (coord.)
  3. Oier Etxaniz (coord.)
  4. Álvaro Herrero (coord.)
  5. Héctor Quintián (coord.)
  6. Emilio Corchado (coord.)

Argitaletxea: Springer Suiza

ISBN: 978-3-319-47364-2,  3-319-47364-6,  978-3-319-47363-5,  3-319-47363-8

Argitalpen urtea: 2017

Orrialdeak: 53-62

Biltzarra: International Conference on Computational Intelligence in Security for Information Systems (9. 2016. San Sebastián)

Mota: Biltzar ekarpena

DIALNET GOOGLE SCHOLAR

Laburpena

A wide range of industrial fields utilize cyclone separators and so, evaluating their performance according to different materials and varying operating conditions could contribute useful information and could also save these industries significant amounts of capital. This study models cyclone performance using linear regression techniques and low errors were obtained in comparison with the values obtained from real experiments. Linear regression and generalized linear regression techniques, simple and enhanced with Gradient Boosting techniques, were used to create linear models with low errors of approximately 0.83 % in cyclone performance.

Datuen iturria: Dialnet