International Journal of Hydrogen Energy, Год журнала: 2025, Номер 138, С. 1017 - 1033
Опубликована: Май 22, 2025
Язык: Английский
International Journal of Hydrogen Energy, Год журнала: 2025, Номер 138, С. 1017 - 1033
Опубликована: Май 22, 2025
Язык: Английский
Process Safety and Environmental Protection, Год журнала: 2024, Номер unknown
Опубликована: Дек. 1, 2024
Язык: Английский
Процитировано
3Energy, Год журнала: 2025, Номер unknown, С. 134560 - 134560
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0Renewable Energy, Год журнала: 2025, Номер unknown, С. 122474 - 122474
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0Energy, Год журнала: 2025, Номер unknown, С. 134720 - 134720
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0Ain Shams Engineering Journal, Год журнала: 2025, Номер 16(2), С. 103282 - 103282
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
0REVIEWS ON ADVANCED MATERIALS SCIENCE, Год журнала: 2025, Номер 64(1)
Опубликована: Янв. 1, 2025
Abstract This study addresses the research gap in understanding durability aspects and microstructure properties of steel slag (SS) concrete. A series different experimental results, including porosity, water absorption, pulse velocity, carbonation depth, chloride penetration, alkali–silica reaction, acid attacks, shrinkage, were summarized to assess SS Similarly, pozzolanic reaction SS, heat hydration, scanning electronic microscopy, thermogravimetry used SS-based Results indicate that improved However, inconsistencies quality, reactivity, expansion risks due free lime magnesium oxide present challenges. Finally, review also highlights proposed recommendation for future research.
Язык: Английский
Процитировано
0Energy Exploration & Exploitation, Год журнала: 2025, Номер unknown
Опубликована: Фев. 13, 2025
Precisely forecasting coke reactivity index (CRI) plays a critical role in the metallurgical industry, as it enables optimization of quality, leading to cost-effective production and efficient resource utilization. In this research, several machine learning predictive models based on extra trees, decision tree, support vector machine, random forest, multilayer perceptron artificial neural network, K-nearest neighbors, convolutional ensemble learning, adaptive boosting using dataset gathered from plant are developed predict CRI. To minimize overfitting each algorithm, K-fold cross-validation methodology is employed during training phase. The efficacy algorithm visually represented through graphical methods quantitatively evaluated performance metrics. findings indicate that maximum fluidity mean reflectance (MMR) exhibit direct correlation with CRI while being indirectly relevant moisture content, ash sulfur basicity index, plastic layer thickness, MMR. Among various evaluated, forest model emerged most accurate tool, according metrics R-squared, square error, average absolute relative error (%), numerical values 0.958, 3.718, 2.545%, respectively, for total datapoints. tool can be easily used accurately estimate without needing experimental or field data reliably.
Язык: Английский
Процитировано
0Energy, Год журнала: 2025, Номер unknown, С. 135379 - 135379
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0Korean Journal of Chemical Engineering, Год журнала: 2025, Номер unknown
Опубликована: Апрель 22, 2025
Язык: Английский
Процитировано
0Process Safety and Environmental Protection, Год журнала: 2025, Номер 198, С. 107232 - 107232
Опубликована: Апрель 29, 2025
Язык: Английский
Процитировано
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