Grey-box Recursive Parameter Identification of a Nonlinear Dynamic Model for Mineral Flotation

TL;DR

This paper introduces a grey-box recursive parameter identification method for nonlinear mineral flotation models, enabling real-time parameter updates and significantly improving concentrate grade predictions under varying operational conditions.

Contribution

It develops a novel recursive identification technique for nonlinear dynamic models, enhancing real-time parameter estimation and process control in mineral flotation.

Findings

Achieved high fit metrics for model parameters n and C.

Significantly improved concentrate grade estimation accuracy.

Demonstrated robustness of the method across different scenarios.

Abstract

This study presents a grey-box recursive identification technique to estimate key parameters in a mineral flotation process across two scenarios. The method is applied to a nonlinear physics-based dynamic model validated at a laboratory scale, allowing real-time updates of two model parameters, n and C, in response to changing conditions. The proposed approach effectively adapts to process variability and allows for continuous adjustments based on operational fluctuations, resulting in a significantly improved estimation of concentrate grade - one key performance indicator. In Scenario 1, parameters n and C achieved fit metrics of 97.99 and 96.86, respectively, with concentrate grade estimations improving from 75.1 to 98.69 using recursive identification. In Scenario 2, the fit metrics for n and C were 96.27 and 95.48, respectively, with the concentrate grade estimations increasing from 96.27 to 99.45 with recursive identification. The results demonstrate the effectiveness of the proposed grey-box recursive identification method in accurately estimating parameters and predicting concentrate grade in a mineral flotation process.