Support vector regression models for trickle bed reactors

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.

• Regression method based on Support Vector Machines.
• Data mined from over 22,000 experimental conditions from various authors.
• SVR method shows remarkably good predictability over this wide range of data.
• Improves on earlier neural network-based heuristic learning proposed by Larachi and co-workers [1].
• Scalable and extendable to other multiphase reactor systems.