From data to decisions: Bayesian modelling and global sensitivity analysis for flotation control
For mineral processing engineers, this provides an interpretable method to identify dominant variables for flotation control, though it is an incremental application of existing methods to a specific domain.
This work develops a Gaussian Process-based surrogate model combined with Sobol indices and SHAP to identify key variables influencing air recovery in flotation systems, enabling interpretable data-driven control. The framework quantifies variable contributions and interactions using laboratory-scale experimental data.
This work presents a data-driven framework for interpretable modelling and decision support in flotation systems, integrating Gaussian Process (GP) regression with Global Sensitivity Analysis (GSA) via Sobol indices and local interpretability using SHapley Additive exPlanations (SHAP). Based on laboratory-scale experimental data, a static GP surrogate model is developed to capture how superficial air velocity, overflowing froth velocity, froth height over the lip, pulp height, bubble size, and tailings flowrate influence the measured air recovery. The trained GP enables the computation of Sobol indices to quantify the contribution of each variable and their interactions to the overall variance in air recovery. The combination of Bayesian inference and Sobol-based sensitivity metrics provides a systematic approach to identify the dominant and interacting variables governing air recovery. This study links Bayesian learning, sensitivity quantification, and explainability to provide a foundation for data-driven control and optimisation of flotation processes.