Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

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

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 dataset of measurements made on a process using an old catalyst. This second dataset 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.
Document type :
Preprints, Working Papers, ...
Complete list of metadatas

Cited literature [29 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02935247
Contributor : Julien Jacques <>
Submitted on : Wednesday, September 16, 2020 - 2:50:52 AM
Last modification on : Thursday, September 17, 2020 - 3:28:11 AM

File

Iapteff2020.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02935247, version 1

Collections

Citation

Loïc Iapteff, Julien Jacques, Matthieu Rolland, Benoît Celse. Reducing the number of experiments required for modeling the hydrocracking process with kriging through Bayesian transfer learning. 2020. ⟨hal-02935247⟩

Share

Metrics

Record views

79

Files downloads

24