Published in Minerals Engineering, 2025
Recommended citation: Quintanilla, P., Fernández, F., Mancilla, C., Rojas, M., & Navia, D. (2025). Digital twin with automatic disturbance detection for an expert-controlled SAG mill. Minerals Engineering, 220, 109076. https://doi.org/10.1016/j.mineng.2024.109076
Published in Computer Aided Chemical Engineering, 2024
Recommended citation: Quintanilla, P., Navia, D., Neethling, S., & Brito-Parada, P. (2024). Experimental Implementation of an Economic Model Predictive Control for Froth Flotation. Computer Aided Chemical Engineering, 53, 1759-1764. https://doi.org/10.1016/B978-0-443-28824-1.50294-5
Published in IEEE, 2024
Recommended citation: González, R. A., & Quintanilla, P. (2024). Grey-box Recursive Parameter Identification of a Nonlinear Dynamic Model for Mineral Flotation. IEEE, 10th International Conference on Control, Decision and Information Technologies (CoDIT), pp. 2967-2972, doi: 10.1109/CoDIT62066.2024.10708161
Published in IFAC-PapersOnLine, 2023
Recommended citation: Quintanilla, P., Navia, D., Neethling, S. J., & Brito-Parada, P. R. (2023). Evaluation of Changes in Feed Particle Size within an Economic Model Predictive Control Strategy for Froth Flotation. IFAC-PapersOnLine, 56(2), 2317-2322. https://doi.org/10.1016/j.ifacol.2023.10.1200
Published in Minerals Engineering, 2023
Recommended citation: Quintanilla, P., Navia, D., Neethling, S. J., & Brito-Parada, P. R. (2023). Economic model predictive control for a rougher froth flotation cell using physics-based models. Minerals Engineering, 196, 108050. https://doi.org/10.1016/j.mineng.2023.108050
Published in MethodsX, 2023
Recommended citation: Quintanilla, P., Navia, D., Moreno, F., Neethling, S. J., & Brito-Parada, P. R. (2023). A methodology to implement a closed-loop feedback-feedforward level control in a laboratory-scale flotation bank using peristaltic pumps. MethodsX, 10, 102081. https://doi.org/10.1016/j.mex.2023.102081
Published in Minerals Engineering, 2022
Recommended citation: Mesa, D., Quintanilla, P., & Reyes, F. (2022). Bubble Analyser — An open-source software for bubble size measurement using image analysis. Minerals Engineering, 180, 107497. https://doi.org/10.1016/j.mineng.2022.107497
Published in Minerals Engineering, 2021
Recommended citation: Quintanilla, P., Neethling, S. J., Mesa, D., Navia, D., & Brito-Parada, P. R. (2021). A dynamic flotation model for predictive control incorporating froth physics. Part II: Model calibration and validation. Minerals Engineering, 173, 107190. https://doi.org/10.1016/j.mineng.2021.107190
Published in Minerals Engineering, 2021
Recommended citation: Quintanilla, P., Neethling, S. J., Navia, D., & Brito-Parada, P. R. (2021). A dynamic flotation model for predictive control incorporating froth physics. Part I: Model development. Minerals Engineering, 173, 107192. https://doi.org/10.1016/j.mineng.2021.107192
Published in Materials Proceeding, 2021
Recommended citation: Quintanilla, P.; Neethling, S.J.; Brito-Parada, P.R. Development and Validation of a Dynamic Model for Flotation Predictive Control Incorporating Froth Physics. Mater. Proc. 2021, 5, 13. https://doi.org/10.3390/materproc2021005013
Published in Minerals Engineering, 2021
Recommended citation: Quintanilla, P., Neethling, S. J., & Brito-Parada, P. R. (2021). Modelling for froth flotation control: A review. Minerals Engineering, 162, 106718. https://doi.org/10.1016/j.mineng.2020.106718
Published in Computers & Chemical Engineering, 2019
Recommended citation: Navia, D., Puen, A., Quintanilla, P., Briceño, L., & Bergh, L. (2019). On dealing with measured disturbances in the modifier adaptation method for real-time optimization. Computers & Chemical Engineering, 128, 141-163. https://doi.org/10.1016/j.compchemeng.2019.06.004
Published in Computer Aided Chemical Engineering, 2018
Recommended citation: Navia, D., Puen, A., Quintanilla, P., Bergh, L., Briceño, L., & de Prada, C. (2018). A Proposal to Include the Information of Disturbances in Modifier Adaptation Methodology for Real Time Optimization. Computer Aided Chemical Engineering, 43, 1081-1086. https://doi.org/10.1016/B978-0-444-64235-6.50189-3
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