• La Universidad
    • Historia
    • Rectoría
    • Autoridades
    • Secretaría General
    • Pastoral UC
    • Organización
    • Hechos y cifras
    • Noticias UC
  • 2011-03-15-13-28-09
  • Facultades
    • Agronomía e Ingeniería Forestal
    • Arquitectura, Diseño y Estudios Urbanos
    • Artes
    • Ciencias Biológicas
    • Ciencias Económicas y Administrativas
    • Ciencias Sociales
    • College
    • Comunicaciones
    • Derecho
    • Educación
    • Filosofía
    • Física
    • Historia, Geografía y Ciencia Política
    • Ingeniería
    • Letras
    • Matemáticas
    • Medicina
    • Química
    • Teología
    • Sede regional Villarrica
  • 2011-03-15-13-28-09
  • Organizaciones vinculadas
  • 2011-03-15-13-28-09
  • Bibliotecas
  • 2011-03-15-13-28-09
  • Mi Portal UC
  • 2011-03-15-13-28-09
  • Correo UC
- Repository logo
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Suomi
  • Svenska
  • Türkçe
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Yкраї́нська
  • Log in
    Log in
    Have you forgotten your password?
Repository logo
  • Communities & Collections
  • All of DSpace
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Suomi
  • Svenska
  • Türkçe
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Yкраї́нська
  • Log in
    Log in
    Have you forgotten your password?
  1. Home
  2. Browse by Author

Browsing by Author "Cabrera, Miguel"

Now showing 1 - 1 of 1
Results Per Page
Sort Options
  • Loading...
    Thumbnail Image
    Item
    Soft-sensor for on-line estimation of ethanol concentrations in wine stills
    (ELSEVIER SCI LTD, 2008) Osorio, Daniel; Perez Correa, J. Ricardo; Agosin, Eduardo; Cabrera, Miguel
    Batch distillation is a traditional and widely-used technique to produce Pisco brandy, a young spirit made from Muscat wine. It is necessary to track a given ethanol composition in the distillate in order to obtain a reproducible spirit with a desired aromatic profile. The use of multiple ethanol sensors represents a considerable cost, which prevents many distilleries from adopting this technology. Aiming to provide practical and affordable industrial-scale distillation control technology, we developed a soft-sensor to estimate distillate ethanol concentration on-line based on four temperature measurements in the still. The soft-sensor, calibrated with laboratory and industrial experimental data, consisted of an Artificial Neural Network and involved simple data pre-processing procedures. Simplicity and good performance were the metrics adopted for testing different algorithms and network structures. Returning mean prediction errors of +/- 0.6% v/v with laboratory scale distillations and +/- 1.6% v/v in industrial trials, the resulting accuracy of the soft-sensor is sufficient to improve standard practice and reproducibility. (c) 2008 Elsevier Ltd. All rights reserved.

Bibliotecas - Pontificia Universidad Católica de Chile- Dirección oficinas centrales: Av. Vicuña Mackenna 4860. Santiago de Chile.

  • Cookie settings
  • Privacy policy
  • End User Agreement
  • Send Feedback