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The electrochemical storage of electrical energy has become a global issue and a major challenge. Li-ion batteries are considered as the preferred solution to store energy. Safe batteries systems keep today the weakest link in the development of many energies or power demanding.
The increasing demand for plastics and the need for efficient recycling are critical issues in waste management due to the complexity of separating mixed polymers and impurities. Despite advancements, current recycling rates remain low, largely because of the difficulty in accurately sorting plastic waste.
L’équipe R&I corrosion du département d’Electrochimie et Matériaux au sein d’IFP énergies nouvelles (Solaize), en lien avec le LaSIE (La Rochelle), propose un poste de thèse captivant sur la compréhension de la dégradation des métaux dans les pr
The global production of plastics is growing exponentially and consequently is the plastic pollution in the environment. At IFP Energies Nouvelles, we develop models that simulate the transport, deposition, and erosion of plastics in the environment.
Context and objectives
During laundry washing, fibers are inevitably shed from textiles and are carried into the wastewater. These fibers, typically around 10 μm in diameter, vary in composition
- mainly polyester and cotton
The energy transition towards renewable sources is a major societal challenge. Optimizing wind farm systems is crucial to maximize efficiency while reducing component wear and tear. However, control algorithms for these systems are often computationally intensive and hard to execute in real-time.
Textile recycling is an essential initiative to reduce waste and mitigate the environmental impact of the fashion industry. The process involves the collection of used garments, which are then sorted, cleaned and converted into new materials or repurposed products.
The thesis aims to study the energy transition’s impact on water resources in the light of the projected increase in global metal demand.
IFP Energies nouvelles (IFPEN) offers a PhD opportunity in the field of Non-Intrusive Load Monitoring (NILM) applied to industrial environments. The project aims to develop an innovative method using Convolutional Neural Networks (CNN) to enhance the accuracy of device energy signature detection.
Considering the sources of uncertainty in system design is essential in IFPEN applications for sustainable energy mix: e.g. to achieve the expected performance levels for an electric motor, for example, or for the reliability of a wind turbine.
Catalytic processes play a key role in many industries, and the optimization of these systems remains a major challenge. Combining the exceptional performance of homogeneous catalysis with the ease of implementation of heterogeneous catalysis is a major challenge.
Deciphering runaway mechanisms in Lithium-ion batteries by in mortem spectroscopic approaches