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Gaussian process regression for modeling computational and experimental mineral processing data

delete2025-12-09
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PRE
AI
A
Amir Eskanlou *
Z
Zhen Yin
J
Jef Caers
DOI：10.1016/j.mineng.2025.110000delete
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Abstract

Abstract

En 中文
• GPR effectively models sparse, high-dimensional mineral processing datasets. • Variogram-based GPR enhances robustness by deriving covariance from data. • GPR quantifies uncertainty associated with process predictions. • Polynomial models fail under scarce or skewed mineral processing datasets. • This approach can be extended to optimize a wide range of mineral processing systems.

Journal

Minerals Engineering cover
Minerals Engineering
IF:
5
Papers:
8.1K
Citations:
2.6W

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No organization information available