A systematic modeling methodology of deep neural network‐based 
structure‐property relationship for rapid and reliable prediction on flashpoints

Wen, Huaqiang; Su, Yang; Wang, Zihao; Jin, Saimeng; Ren, Jingzheng; Shen, Weifeng; Eden, Mario

doi:10.1002/aic.17402

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A systematic modeling methodology of deep neural network‐based structure‐property relationship for rapid and reliable prediction on flashpoints

Wen, H., Su, Y., Wang, Z., Jin, S., Ren, J., Shen, W., et al. (2022). A systematic modeling methodology of deep neural network‐based structure‐property relationship for rapid and reliable prediction on flashpoints. AIChE Journal, 68(1): 17402. doi:10.1002/aic.17402.

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Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-0009-7DD5-B Versions-Permalink: https://hdl.handle.net/21.11116/0000-0009-B2E6-A

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Urheber:
Wen, Huaqiang¹, Autor
Su, Yang², Autor
Wang, Zihao³, Autor
Jin, Saimeng¹, Autor
Ren, Jingzheng⁴, Autor
Shen, Weifeng¹, Autor
Eden, Mario⁵, Autor

Affiliations:
1School of Chemistry and Chemical Engineering, Chongqing University, Chongqing, China, ou_persistent22
2School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, China, ou_persistent22
3Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738151
4Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, ou_persistent22
5Department of Chemical Engineering, Auburn University, Auburn, AL, United States, ou_persistent22