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Reducing the number of experiments required for modelling the hydrocracking process with kriging through Bayesian transfer learning

Author

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  • Loïc Iapteff
  • Julien Jacques
  • Matthieu Rolland
  • Benoit Celse

Abstract

The objective is to improve the learning of a regression model of the hydrocracking process using a reduced number of observations. When a new catalyst is used for the hydrocracking process, a new model must be fitted. Generating new data is expensive and therefore it is advantageous to limit the amount of new data generation. Our idea is to use a second data set of measurements made on a process using an old catalyst. This second data set is large enough to fit performing models for the old catalyst. In this work, we use the knowledge from this old catalyst to learn a model on the new catalyst. This task is a transfer learning task. We show that the results are greatly improved with a Bayesian approach to transfer linear model and kriging model.

Suggested Citation

  • Loïc Iapteff & Julien Jacques & Matthieu Rolland & Benoit Celse, 2021. "Reducing the number of experiments required for modelling the hydrocracking process with kriging through Bayesian transfer learning," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1344-1364, November.
    • Handle: RePEc:bla:jorssc:v:70:y:2021:i:5:p:1344-1364
    DOI: 10.1111/rssc.12516
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    2. Roustant, Olivier & Ginsbourger, David & Deville, Yves, 2012. "DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i01).
    3. David Ginsbourger & Delphine Dupuy & Anca Badea & Laurent Carraro & Olivier Roustant, 2009. "A note on the choice and the estimation of Kriging models for the analysis of deterministic computer experiments," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(2), pages 115-131, March.
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