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Abstract

Disclosed is a method for optimizing a mineral recovery process from ore material using a flotation chamber. The method comprises implementing a machine learning model to determine operational parameters of the flotation chamber, for the mineral recovery process based on a geometry of the flotation chamber and properties of the ore material. The method further comprises simulating the mineral recovery process using ranges of the determined operational parameters to determine a factor representative of a relationship between a gas hold-up value and a bubble diameter value. The method further comprises calculating the gas hold-up value and the bubble diameter value based on determined the operational parameters and the determined factor. The method further comprises utilizing the determined gas hold-up value and bubble diameter value to determine optimized values of the operational parameters for the mineral recovery process by implementing a virtual sensor, to achieve higher throughput of recovered minerals from the ore material.

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First Claim