Reducing the Number of Experiments Required for Modelling the Hydrocracking Process with Kriging Through Bayesian Transfer Learning

Iapteff, Loïc; Jacques, Julien; Rolland, Matthieu; Celse, Benoit

doi:10.1111/rssc.12516

Cited by 4 publications

(3 citation statements)

References 37 publications

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“…It should be noticed that bold-italicθ̂ t → g → 0 bold-italicθ̂ s and bold-italicθ̂ t → g → + ∞ bold-italicθ̂ t , M L , where bold-italicθ̂ t , M L is the maximum likelihood estimator on the target sample only, without transfer. The heuristic method to chose the g value proposed for Bayesian kriging transfer is not adapted for this case. Indeed, the model is more complex, and we cannot know the range in which parameters will lead to a high value of the likelihood.…”

Section: Methodsmentioning

confidence: 99%

“…The work presented in this article is an extension of the method proposed in a previous work for the modeling of the density of the hydrocracking diesel cut using a kriging model (Gaussian process). The aim of the current article is to use the information from the previous generation catalyst ( n ) to reduce the required time to fit the current catalyst generation ( n + 1) model.…”

Section: Introductionmentioning

confidence: 99%

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Reducing the Number of Experimental Points to Fit Kinetic Models: A Bayesian Approach

Iapteff

Jacques

Celse

et al. 2023

Ind. Eng. Chem. Res.

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Hydrocracking is a crucial refinery process that transforms heavy molecules (i.e., vacuum gas oil (VGO)) into lighter and highly valued products such as naphtha, kerosene, and diesel. It is a two-step process. The hydrotreatment (HDT) reactor uses a more robust catalyst, which essentially serves to remove heteroatoms from the VGO feed in order to satisfy product quality constraints and avoid poisoning the more delicate zeolite-based HCK catalysts. The second hydrocracking (HCK) reactor uses a commercial zeolite catalyst with a carefully selected balance of acid and metallic sites. For hydrotreatment simulation, the kinetic model is decomposed in several ODEs (ordinary differential equations). Catalyst vendors develop more and more catalysts. For each new catalyst (new generation), the kinetic parameters must be refitted. This task is costly and time-consuming. In this article, in order to reduce the required number of experimental points, a Bayesian transfer approach is proposed to fit the parameters of catalyst (n + 1), using the past knowledge of catalyst (n) to add more information. A method for the choice of the prior is proposed and can be used for any type of parametric model. This approach is applied and shows an improvement in the prediction performance and robustness compared to a classical fitting method. In our case, only 10 pilot plant points on catalyst (n + 1) are requested to refit an HDN kinetic model.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reducing the Number of Experimental Points to Fit Kinetic Models: A Bayesian Approach

Iapteff

Jacques

Celse

et al. 2023

Ind. Eng. Chem. Res.

Self Cite

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show abstract

“…Kriging is a representative interpolation model and has excellent predictive performance in a data group with many design parameters and strong nonlinearity [35]. It also provides statistical estimates and does not depend on the user's experience because it optimizes parameters through the maximum likelihood estimation method (MLE) [36,37]. EDT refers to a method of generating multiple decision trees and predicting them as the average of each decision tree result [38,39].…”

Section: Formulation Of Optimizationmentioning

confidence: 99%

Optimization of the Uniformity Index Performance in the Selective Catalytic Reduction System Using a Metamodel

Kim,

Park,

Yoo

et al. 2023

Sustainability

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The significance of the selective catalytic reduction system in vehicles increases in line with the high standards of emission control and enhanced selective catalytic reduction efficiency. This study aims to improve the performance of the selective catalytic reduction system through an optimization method using a metamodel. The objective function is defined as the ammonia uniformity index, and the design parameters are defined in relation to the pipe length and mixer related to the chemical reaction of the urea solution. The range of design parameters has been designated by a trial-and-error method in order to maintain the overall design drawings of the selective catalytic reduction system and prevent modeling errors. Three algorithms, namely, ensemble decision tree, Kriging, and radial basis function, are employed to develop the metamodel. The accuracy of the metamodel is verified based on three indicators: the normalized root mean square error, root mean square error, and maximum absolute error. The metamodel is generated using the Kriging model, which has the highest accuracy among the algorithms, and optimization is also performed. The predicted optimization results are confirmed by computational fluid dynamics numerical analysis with a 99.83% match. The ammonia uniformity index is improved by 1.38% compared to the base model, and it can be said that the NOx purification efficiency is improved by 30.95%. Consequently, optimizing the uniformity index performance through structural optimization is of utmost importance. Furthermore, this study reveals that the design variables related to the mixer play a crucial role in the performance. Therefore, using the metamodel to optimize the selectively catalytic reduction system’s structure should be considered significant. Finally, in the future, the analysis model can be validated using test equipment based on the findings of this study.

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Transfer Learning of Hydroprocessing Model from Fossil Feedstocks to Waste Plastic Pyrolysis Oil

Pejpichestakul,

Becker,

Celse

2024

Computer Aided Chemical Engineering

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Reducing the Number of Experiments Required for Modelling the Hydrocracking Process with Kriging Through Bayesian Transfer Learning

Cited by 4 publications

References 37 publications

Reducing the Number of Experimental Points to Fit Kinetic Models: A Bayesian Approach

Reducing the Number of Experimental Points to Fit Kinetic Models: A Bayesian Approach

Optimization of the Uniformity Index Performance in the Selective Catalytic Reduction System Using a Metamodel

Transfer Learning of Hydroprocessing Model from Fossil Feedstocks to Waste Plastic Pyrolysis Oil

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