Compatibility and Interaction Mechanism between the C4F7N/CO2/O2 Gas Mixture and FKM and NBR

Zhuo, Ran; Chen, Junyi; Xiao, Song; Fu, Mingli; Wang, Dibo; Tan, Xiangyu; Li, Yi; Wu, Peng; Tang, Ju; Zhang, Xiaoxing

doi:10.1021/acsomega.3c00195

Cited by 3 publications

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Comparison of machine learning algorithms based on machine learning for the prediction of thermal plasma physical parameters of C4F7N and CO2 environmentally friendly gas mixtures

Ding,

Tian,

2024

AIP Advances

View full text Add to dashboard Cite

With the goal of “carbon peak and carbon neutrality,” the need for environmentally friendly gases to replace SF6, a high greenhouse effect gas, is urgent. C4F7N, as an environmentally friendly gas with the greatest potential to replace SF6 as an arc extinguishing medium in circuit breakers, can be mixed with CO2 to greatly improve the shortcomings of its high liquefaction temperature, and the calculation of the physical parameters of the mixed gas plasma is a prerequisite for the computational simulation of the arc process in the opening of circuit breakers. Because solving the physical parameters is expensive, based on the system of differential equations, this paper adopts several machine learning algorithms by mining the relationship between the data using the known physical parameter data to predict the results of the physical parameters to be solved under certain conditions, which greatly reduces the cost of computation. The machine learning algorithms used in this paper are K-nearest-neighbor regression, decision tree, random forest, support vector machine, and gradient boosting regression, of which for the support vector machine, hyperparameters find it difficult to determine the problem of optimization using the gray wolf algorithm. The prediction results of several algorithms show that they are more accurate and that the problem can be solved better by using the method of machine learning. Finally, the comparison results show that the support vector machine exhibits better performance in most cases and that the gray wolf algorithm can make the results of the support vector machine more accurate.

show abstract

Comparison of machine learning algorithms based on machine learning for the prediction of thermal plasma physical parameters of C4F7N and CO2 environmentally friendly gas mixtures

Ding,

Tian,

2024

AIP Advances

View full text Add to dashboard Cite

show abstract

Partial Discharge and Decomposition Characteristics of C₄F₇N/CO₂/O₂ Under Metallic Protrusion Defects

Liu,

Yao,

Xia

et al. 2023

2023 2nd Asia Power and Electrical Technology Conference (APET)

View full text Add to dashboard Cite

Mechanism of O₂ Influence on the Decomposition Process of the Eco-Friendly Gas Insulating Medium C₄F₇N/CO₂

Ye,

Chu,

Brault

et al. 2024

IEEE Trans. Dielect. Electr. Insul.

View full text Add to dashboard Cite

The C4F7N/CO2/O2 gas mixture is the most promising eco-friendly gas insulation medium available. However, there are few studies on the mechanism of the influence of the buffer gas O2 ratio and its role in the decomposition characteristics of C4F7N/CO2. In this paper, based on the ReaxFF reaction molecular dynamics method and density functional theory, a simulation of the thermal decomposition process of the C4F7N/CO2 mixture under different O2 ratios was carried out at temperatures in the range 2000 -3000 K. A constructed model of the C4F7N/CO2/O2 mixture reaction system was used that included the possible reaction paths, product distribution characteristics and their generation rates. The calculation results show that the thermal decomposition of C4F7N/CO2/O2 mainly generates species such as CF3, CF2,

show abstract

Compatibility and Interaction Mechanism between the C₄F₇N/CO₂/O₂ Gas Mixture and FKM and NBR

Cited by 3 publications

References 36 publications

Comparison of machine learning algorithms based on machine learning for the prediction of thermal plasma physical parameters of C4F7N and CO2 environmentally friendly gas mixtures

Comparison of machine learning algorithms based on machine learning for the prediction of thermal plasma physical parameters of C4F7N and CO2 environmentally friendly gas mixtures

Partial Discharge and Decomposition Characteristics of C₄F₇N/CO₂/O₂ Under Metallic Protrusion Defects

Mechanism of O₂ Influence on the Decomposition Process of the Eco-Friendly Gas Insulating Medium C₄F₇N/CO₂

Contact Info

Product

Resources

About

Compatibility and Interaction Mechanism between the C4F7N/CO2/O2 Gas Mixture and FKM and NBR

Cited by 3 publications

References 36 publications

Comparison of machine learning algorithms based on machine learning for the prediction of thermal plasma physical parameters of C4F7N and CO2 environmentally friendly gas mixtures

Comparison of machine learning algorithms based on machine learning for the prediction of thermal plasma physical parameters of C4F7N and CO2 environmentally friendly gas mixtures

Partial Discharge and Decomposition Characteristics of C4F7N/CO2/O2 Under Metallic Protrusion Defects

Mechanism of O2 Influence on the Decomposition Process of the Eco-Friendly Gas Insulating Medium C4F7N/CO2

Contact Info

Product

Resources

About

Compatibility and Interaction Mechanism between the C₄F₇N/CO₂/O₂ Gas Mixture and FKM and NBR

Partial Discharge and Decomposition Characteristics of C₄F₇N/CO₂/O₂ Under Metallic Protrusion Defects

Mechanism of O₂ Influence on the Decomposition Process of the Eco-Friendly Gas Insulating Medium C₄F₇N/CO₂