MASS-CONSERVING PHYSICS-INFORMED AUGMENTATION AND FOURIER FEATURE NETWORKS FOR SMALL-DATA PREDICTION OF MOLYBDENITE (MO₂S) LEACHING KINETICS

Molybdenum remains a strategic metal for advanced steels and catalysis, while environmental and energy pressures are accelerating interest in hydrometallurgical leaching routes for molybdenite (MoS₂). Predicting leaching kinetics is difficult because the process is highly nonlinear and strongly inf...

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Hlavní autoři:	Nurdaulet Izmailov, Meirambek Shaimerden, Azamat Toishybek, Kaisar Kassymzhanov, Araylim Mukangalieva, Nurzhan Ultarakov, Alma Turganbayeva
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Al-Farabi Kazakh National University 2025-12-01
Edice:	Journal of Problems in Computer Science and Information Technologies
Témata:	molybdenite leaching hydrometallurgy small data physics-informed augmentation mass conservation Fourier feature networks
On-line přístup:	https://jpcsit.kaznu.kz/index.php/kaznu/article/view/291
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