arXiv subjects

Daniel Navia

Publications and source records attributed to Daniel Navia.

4 recordsLinked to original sources

arXiv2026

From data to decisions: Bayesian modelling and global sensitivity analysis for flotation control

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.

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arXiv2025

Digital twin with automatic disturbance detection for an expert-controlled SAG mill

This study presents the development and validation of a digital twin for a semi-autogenous grinding (SAG) mill controlled by an expert system. The digital twin integrates three key components of the closed-loop operation: (1) fuzzy logic for expert control, (2) a state-space model for regulatory control, and (3) a recurrent neural network to simulate the SAG mill process. The digital twin is combined with a statistical framework for automatically detecting process disturbances (or critical operations), which triggers model retraining only when deviations from expected behaviour are identified, ensuring continuous updates with new data to enhance the SAG supervision. The model was trained with 68 hours of operational industrial data and validated with an additional 8 hours, allowing it to predict mill behaviour within a 2.5-minute horizon at 30-second intervals with errors smaller than 5%.

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arXiv2024

Experimental implementation of an economic model predictive control for froth flotation

We present the implementation of a novel economic model predictive control (E-MPC) strategy for froth flotation, the largest tonnage mineral separation process. A previously calibrated and validated dynamic model incorporating froth physics was used, which overcomes the limitations of previous simplified models reported in the literature. The E-MPC's optimal control problem was solved using full discretization with orthogonal collocation over finite elements, employing automatic differentiation via CasADi. This approach was applied in a 30-litre laboratory-scale flotation cell, significantly improving mineral recovery from 9% to 29% under feed flowrate disturbances while maintaining a minimum concentrate grade of 20%.

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arXiv2024

Digital twin with automatic disturbance detection for real-time optimization of a semi-autogenous grinding (SAG) mill

This work describes the development and validation of a digital twin for a semi-autogenous grinding (SAG) mill controlled by an expert system. The digital twin consists of three modules emulating a closed-loop system: fuzzy logic for the expert control, a state-space model for regulatory control, and a recurrent neural network for the SAG mill process. The model was trained with 68 hours of data and validated with 8 hours of test data. It predicts the mill's behavior within a 2.5-minute horizon with a 30-second sampling time. The disturbance detection evaluates the need for retraining, and the digital twin shows promise for supervising the SAG mill with the expert control system. Future work will focus on integrating this digital twin into real-time optimization strategies with industrial validation.

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