A Film Boiling Study of Ethanol Pyrolysis

Avedisian, C. Thomas; Kuo, Wei-Chih; Tsang, Wing; Lowery, Adam

doi:10.1021/acs.iecr.8b00770

Cited by 6 publications

(1 citation statement)

References 35 publications

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“…The temperature at which this boiling transition occurs is referred to as the Leidenfrost temperature or the minimum film boiling (MFB) temperature, denoted T min . This threshold temperature holds immense significance in the industrial design of nuclear reactors, cryogenic milling, and grinding. , For example, for accurate estimation of the liquified natural gas (LNG) spill consequence, the transition of film boiling must be taken into consideration . By understanding and accurately determining the MFB temperature, engineers and researchers can optimize the performance and efficiency of various industrial processes and systems.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach for Predicting Minimum Film Boiling Temperature Considering the Surface Roughness Effect

Tang,

Yu,

Gong

et al. 2023

Ind. Eng. Chem. Res.

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The minimum film boiling (MFB) temperature, a crucial industrial design parameter for nuclear reactors, cryogenic milling, and grinding, refers to the lowest sustainable temperature at which stable film boiling occurs. Despite the development of many thermodynamic and hydrodynamic models, a universal model for predicting the MFB temperature is still required. In recent years, machine learning (ML) methods have been shown to outperform previous correlations in predicting the MFB temperature. However, these ML methods still have limitations in interpretability and the inclusion of key parameters, such as surface roughness, in the database. To address these issues, this study applies six ML models and a data interpretation method (SHapley Additive exPlanations, SHAP) to investigate MFB temperature. The extreme gradient boosting (XGBoost) model performs the best among these ML models, with an MAE of 2.6% for predicting MFB temperature without considering surface roughness, significantly outperforming the existing correlations. To account for surface roughness, a reduced database excluding data points without roughness values was used to train new XGBoost models. To fully use the removed data, a strategy is developed where normalized roughness and the MFB temperature predicted by the XGBoost model trained with the complete database excluding roughness are used as input variables. This approach improves the performance and generalization capabilities under insufficient surface roughness data sources by transferring the knowledge acquired from the complete database to the ML model developed by using the reduced database. The results show that the contributions of input variables and the physical variation trend of the XGBoost models are consistent with previous experimental results and theories.

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