.png){kind=icon}
Return
Support vector regression models for trickle bed reactors
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cej.2012.07.081.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Transport phenomena in multiphase reactors are poorly understood and first-principles modeling approaches have hitherto met with limited success. Industry continues thus far to depend heavily on engineering correlations for variables like pressure drop, transport coefficients and wetting efficiencies. While immensely useful, engineering correlations typically have wide variations in their predictive capability when venturing outside their instructed domain, and hence universally applicable correlations are rare. In this contribution, we present a machine learning approach for modeling such multiphase systems, specifically using the Support Vector Regression (SVR) algorithm. An application of trickle bed reactors is considered wherein key design variables for which numerous correlations exist in the literature (with a large variation in their predictions), are all correlated using the SVR approach with remarkable accuracy of prediction for all the different literature data sets with wide-ranging databanks. (C) 2012 Elsevier B.V. All rights reserved.
Keywords:
Support Vector Machines (SVMs)
Support Vector Regression (SVR)
Correlations
Trickle bed reactors
Machine learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
13.2
Papers:
7.5W
Citations:
48.5W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSave
ShareSaveShareSave
View more