  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.
