Computational prediction of green fuels from crude palm oil in fluid catalytic cracking riser DOI Creative Commons
Agus Prasetyo Nuryadi, Widodo Wahyu Purwanto, Windi Susmayanti

и другие.

International Journal of Renewable Energy Development, Год журнала: 2023, Номер 12(5), С. 923 - 929

Опубликована: Авг. 16, 2023

Fluid catalytic cracking could convert crude palm oil into valuable green fuels to substitute fossil fuels. This study aimed predict the phenomenon and yield in industrial fluid riser using computational dynamics. A three-dimensional transient simulation Eulerian-Lagrangian with multiphase particle-in-cell is investigate reactive gas-particle hydrodynamics four-lump kinetic network model rare earth-Y catalyst for behaviors. The results show that velocity profile increase middle of reactor because reaction process produces OLP Gas products has a lighter molecular weight. endothermic causes temperature decrease heat comes from catalyst. analysis shows accurately predicts fuel oil. As result, conversion, organic liquid product yield, correspond 70 wt%, 28.8 27.5 respectively. Compared experimental study, prediction showed good agreement determined optimal dimension. methodology are guidelines optimizing FCC CPO.

Язык: Английский

A framework to model contractors’ hazard and risk exposure at process plants using unsupervised text mining DOI
Satyajeet Sahoo, J. Maiti, V.K. Tewari

и другие.

Process Safety and Environmental Protection, Год журнала: 2023, Номер 183, С. 24 - 45

Опубликована: Дек. 21, 2023

Язык: Английский

Процитировано

3

Fault Prognosis and Detection for Carbonate Chemical Process under Multimodal Conditions Based on Transformer and Self-Adaptive Deep Learning DOI

Xusong Pu,

Xiaolong Ge,

Botan Liu

и другие.

Industrial & Engineering Chemistry Research, Год журнала: 2024, Номер 63(24), С. 10677 - 10691

Опубликована: Июнь 11, 2024

Fault prognosis and detection are of vital importance for safe efficient operation complex chemical processes, which have become a research hotspot by employing mainstream deep learning methods. However, nonsignificant variability characteristic variables under some fault conditions multioperation mode makes the corresponding task difficult to achieve. In present work, transformer-based multivariate multistep prediction is utilized establish model, intends realize early prediction. addition, using samples multiple normal states, local adaptive standardization employed preprocess moving window data. Then, variational autoencoder combined with series bidirectional long short-term memory layers developed detection, make model operating modes. By practical process high-purity carbonate ester production as benchmark, efficiency validated, monitoring performance clearly clarified.

Язык: Английский

Процитировано

0

Process intensification of multiphase flow and reaction system: Perspectives DOI
Xingying Lan, Xiaogang Shi, Chengxiu Wang

и другие.

Chemical Engineering and Processing - Process Intensification, Год журнала: 2024, Номер 204, С. 109938 - 109938

Опубликована: Авг. 8, 2024

Язык: Английский

Процитировано

0

A Machine Learning Approach for Laboratory Safety Monitoring under Extreme Conditions DOI

Meiyong Xu,

Zhenni Han,

Anlu Wan

и другие.

Опубликована: Июль 26, 2024

Язык: Английский

Процитировано

0

Semi-supervised learning based on temporal-spatial adaptive algorithm and its recognition mechanism for carbonate ester production process monitoring DOI
Li Yao, Xiaolong Ge,

Botan Liu

и другие.

Process Safety and Environmental Protection, Год журнала: 2024, Номер unknown

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

0

Computational prediction of green fuels from crude palm oil in fluid catalytic cracking riser DOI Creative Commons
Agus Prasetyo Nuryadi, Widodo Wahyu Purwanto, Windi Susmayanti

и другие.

International Journal of Renewable Energy Development, Год журнала: 2023, Номер 12(5), С. 923 - 929

Опубликована: Авг. 16, 2023

Fluid catalytic cracking could convert crude palm oil into valuable green fuels to substitute fossil fuels. This study aimed predict the phenomenon and yield in industrial fluid riser using computational dynamics. A three-dimensional transient simulation Eulerian-Lagrangian with multiphase particle-in-cell is investigate reactive gas-particle hydrodynamics four-lump kinetic network model rare earth-Y catalyst for behaviors. The results show that velocity profile increase middle of reactor because reaction process produces OLP Gas products has a lighter molecular weight. endothermic causes temperature decrease heat comes from catalyst. analysis shows accurately predicts fuel oil. As result, conversion, organic liquid product yield, correspond 70 wt%, 28.8 27.5 respectively. Compared experimental study, prediction showed good agreement determined optimal dimension. methodology are guidelines optimizing FCC CPO.

Язык: Английский

Процитировано

0