Partenaires
Nos tutelles
Nos partenaires
Rechercher
Les événements de juin 2026
Séminaire
Séminaires Ampère
Mercredi 3 juin de 12h30 à 14h00 - Francisca Viejo Reales - University of Huelva, Spain Séminaire "Parameter Identification of a Dynamic Nitrification Model Using Experimental Soil Solution Data" Résumé : Human activities, especially the widespread use of synthetic fertilizers, have profoundly disrupted the natural nitrogen cycle. This disruption has led to severe environmental issues, including eutrophication, biodiversity loss, and the formation of marine dead zones. According to the Planetary Boundaries Framework, the safe threshold for nitrogen input into the environment has already been largely exceeded. Addressing this imbalance is, therefore, a necessity. In this context, optimizing fertilization is a key step toward restoring balance and moving towards more sustainable agricultural systems.
To achieve this goal, it is essential to gain a deeper understanding of the behavior of nitrogen species in soil. One of the key processes in this cycle is nitrification, a biological process in which ammonium is converted into nitrite and then into nitrate through the action of microorganisms (bacteria). In this regard, the development of models of nitrification represents a valuable tool to describe and predict the dynamics of the process. Nevertheless, the parameters of such models are highly sensitive to multiple environmental and biological factors, reflecting the inherent complexity of living systems. This preliminary work aims at identifying a set of parameters of a dynamical model of the nitrification process using a experimental dataset consisting in concentration data of nitrogen species obtained under controlled laboratory conditions.Lieu : Salle Chaussey au H9 (ECL) - Lien Zoom transmis par email.
Jeudi 4 juin de 12h30 à 14h00 - Caiyi Xiong - LIAS, Poitiers Séminaire "Real time data driven model learning of complex dynamical systems application to high-speed rotating machines operated with active magnetic bearing" Résumé : Active Magnetic Bearings (AMB) are widely used in high-speed rotating machines due to their ability to levitate rotors without mechanical contact. The considered AMB-supported rotor system is a multivariable system that is open-loop unstable, which makes system identification particularly challenging and requires identification to be performed in closed loop. This article aims at developing data-driven identification methods to track the evolving dynamics of AMB systems in real time, with the objective of detecting performance degradation and anticipating potential failures.
At the current stage of the research, the system is assumed to be time-invariant and the objective is to obtain an initial characterization of its dynamic behavior, in particular the resonance and anti-resonance information. For this purpose, a nonparametric identification is carried out in the frequency domain. Multisine excitation signals are applied to the system and only steady-state responses are retained. Several estimators were evaluated, and the Errors-In-Variables (EIV) estimator was selected to estimate the frequency response Gk. This step provides an accurate nonparametric representation of the system dynamics. Based on this result, future work can be extended to the identification of parametric models.Lieu : Salle Chaussey au H9 (ECL) - Lien Zoom transmis par email.
AIS
Mercredi 3 juin de 12h30 à 14h00 - Francisca Viejo Reales - University of Huelva, Spain Séminaire "Parameter Identification of a Dynamic Nitrification Model Using Experimental Soil Solution Data" Résumé : Human activities, especially the widespread use of synthetic fertilizers, have profoundly disrupted the natural nitrogen cycle. This disruption has led to severe environmental issues, including eutrophication, biodiversity loss, and the formation of marine dead zones. According to the Planetary Boundaries Framework, the safe threshold for nitrogen input into the environment has already been largely exceeded. Addressing this imbalance is, therefore, a necessity. In this context, optimizing fertilization is a key step toward restoring balance and moving towards more sustainable agricultural systems.
To achieve this goal, it is essential to gain a deeper understanding of the behavior of nitrogen species in soil. One of the key processes in this cycle is nitrification, a biological process in which ammonium is converted into nitrite and then into nitrate through the action of microorganisms (bacteria). In this regard, the development of models of nitrification represents a valuable tool to describe and predict the dynamics of the process. Nevertheless, the parameters of such models are highly sensitive to multiple environmental and biological factors, reflecting the inherent complexity of living systems. This preliminary work aims at identifying a set of parameters of a dynamical model of the nitrification process using a experimental dataset consisting in concentration data of nitrogen species obtained under controlled laboratory conditions.Lieu : Salle Chaussey au H9 (ECL) - Lien Zoom transmis par email.
Jeudi 4 juin de 12h30 à 14h00 - Caiyi Xiong - LIAS, Poitiers Séminaire "Real time data driven model learning of complex dynamical systems application to high-speed rotating machines operated with active magnetic bearing" Résumé : Active Magnetic Bearings (AMB) are widely used in high-speed rotating machines due to their ability to levitate rotors without mechanical contact. The considered AMB-supported rotor system is a multivariable system that is open-loop unstable, which makes system identification particularly challenging and requires identification to be performed in closed loop. This article aims at developing data-driven identification methods to track the evolving dynamics of AMB systems in real time, with the objective of detecting performance degradation and anticipating potential failures.
At the current stage of the research, the system is assumed to be time-invariant and the objective is to obtain an initial characterization of its dynamic behavior, in particular the resonance and anti-resonance information. For this purpose, a nonparametric identification is carried out in the frequency domain. Multisine excitation signals are applied to the system and only steady-state responses are retained. Several estimators were evaluated, and the Errors-In-Variables (EIV) estimator was selected to estimate the frequency response Gk. This step provides an accurate nonparametric representation of the system dynamics. Based on this result, future work can be extended to the identification of parametric models.Lieu : Salle Chaussey au H9 (ECL) - Lien Zoom transmis par email.