Data-driven machine learning for scale-up of bubbling and turbulent fluidized beds: flow hydrodynamics and reactor performance

Fluidized beds provide excellent solids mixing and high heat and mass transfer rates, making them widely used across various industries. However, conventional experimental and computational fluid dynamics (CFD) approaches are often time-consuming and computationally expensive. To address these chall...

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Xehetasun bibliografikoak

Egile Nagusiak:	Xi Chen, Yipeng Xu, Yue Yuan, Qiao Lin, Xiaoyang Wei, Yuanyuan Shao, Jesse Zhu, Dongbing Li
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	Taylor & Francis Group 2026-12-01
Saila:	Engineering Applications of Computational Fluid Mechanics
Gaiak:	Fluidized beds flow hydrodynamics reactor performance machine learning SHAP
Sarrera elektronikoa:	https://www.tandfonline.com/doi/10.1080/19942060.2026.2670348
Etiketak:	Etiketa erantsi Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!