Predicting the performance of an industrial furnace using Gaussian process and linear regression: A comparison

Galeazzi, Andrea; de Fusco, Francesco; Prifti, Kristiano; Gallo, Francesco; Biegler, Lorenz; Manenti, Flavio

doi:10.1016/j.compchemeng.2023.108513

Cited by 6 publications

(1 citation statement)

References 37 publications

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“…An example is shown in Figure 3B, Gaussian process method simultaneously well completed extrapolation and interpolation. Performances of the extrapolation by the Gaussian process regression were tested by Galeazzi et al [53].…”

Section: Extrapolation and Vibrational Wave Functionmentioning

confidence: 99%

Ab initio investigation of potential energy curves of He₂, He₂⁺, and extrapolation by the machine learning method

Xu,

Lu,

Zhong

et al. 2024

Int J of Quantum Chemistry

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Diatomic molecules , are common compositions in plasmas using the helium as the working gas, and play key roles in quenching O2 and N2. However, the potential energy curve (PEC) of the first state obtained at the CASSCF level of theory is not accuracy and data of higher excited states of are lacking. Here, we calculated PECs of four excited states of and nine lowest electronic states of by employing CASSCF+MRCI/Aug‐cc‐pV5Z and CCSD(T)/Aug‐cc‐pV5Z level of theory with sample internuclear distance interval of 0.005 Å. PECs were extrapolated to the complete basis sets. There are double local maxima in PECs of three excited states , , . The machine learning method based on the Gaussian process was used to interpolate and extrapolate the PECs, and then vibrational wave functions are solved numerically. This ab initio investigation provided rotational‐vibrational constants and new data for experimental research in the future.

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Section: Extrapolation and Vibrational Wave Functionmentioning

confidence: 99%

Ab initio investigation of potential energy curves of He₂, He₂⁺, and extrapolation by the machine learning method

Xu,

Lu,

Zhong

et al. 2024

Int J of Quantum Chemistry

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show abstract

Targeting carbon-neutral waste reduction: Novel process design, modelling and optimization for converting medical waste into hydrogen

Zhou,

Liu,

Ren

et al. 2024

Energy

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Machine learning approach with a posteriori-based feature to predict service life of a thermal cracking furnace with coking deposition

Panjapornpon,

Rochpuang,

Bardeeniz

et al. 2024

Results in Engineering

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Predicting the performance of an industrial furnace using Gaussian process and linear regression: A comparison

Cited by 6 publications

References 37 publications

Ab initio investigation of potential energy curves of He₂, He₂⁺, and extrapolation by the machine learning method

Ab initio investigation of potential energy curves of He₂, He₂⁺, and extrapolation by the machine learning method

Targeting carbon-neutral waste reduction: Novel process design, modelling and optimization for converting medical waste into hydrogen

Machine learning approach with a posteriori-based feature to predict service life of a thermal cracking furnace with coking deposition

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Predicting the performance of an industrial furnace using Gaussian process and linear regression: A comparison

Cited by 6 publications

References 37 publications

Ab initio investigation of potential energy curves of He2, He2+, and extrapolation by the machine learning method

Ab initio investigation of potential energy curves of He2, He2+, and extrapolation by the machine learning method

Targeting carbon-neutral waste reduction: Novel process design, modelling and optimization for converting medical waste into hydrogen

Machine learning approach with a posteriori-based feature to predict service life of a thermal cracking furnace with coking deposition

Contact Info

Product

Resources

About

Ab initio investigation of potential energy curves of He₂, He₂⁺, and extrapolation by the machine learning method

Ab initio investigation of potential energy curves of He₂, He₂⁺, and extrapolation by the machine learning method