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Machine Learning-Based Thermodynamic Modeling of Acid Gas Absorption in Aqueous Methyldiethanolamine and Aqueous Piperazine
No full textInternational audienc
Table ronde : Comment observer et évaluer les transitions à l'échelle du territoire ?
No full textInternational audienc
Onset of Dripping Instabilities in Thin Non-Newtonian Liquid Films Flowing Down Inclined Surfaces
No full textInternational audienc
Stockage souterrain d'hydrogène en milieu poreux : Simulations de scénarios de conversion et de création d'un nouveau site de stockage
No full textInternational audienceLes aquifères profonds offrent un environnement particulièrement adapté au stockage souterrain de gaz, compte tenu de leur capacité volumique et leur répartition géographique. En France, le stockage de gaz naturel en aquifères est une technologie maîtrisée depuis plus de 70 ans. Dans le contexte actuel de la transition énergétique et du développement de la filière hydrogène en Europe, les solutions de stockage souterrain d’hydrogène font l’objet d’une évaluation approfondie afin d’accompagner le déploiement de l’hydrogène renouvelable.Le stockage souterrain d’hydrogène présente toutefois des défis spécifiques. La présence de minéraux sulfurés pourrait induire la formation de sulfure d’hydrogène (H2S). Ce processus abiotique a été simulé dans les conditions d’un site du Bassin Parisien afin d’évaluer la quantité potentielle de H2S générée. Par ailleurs, des réactions microbiennes telles que la sulfato-réduction et la méthanogenèse pourraient altérer la qualité du gaz stocké, en consommant partiellement l’hydrogène et en générant des composés indésirables comme le H₂S et le CH₄. La cinétique de ces réactions dépend fortement des conditions géochimiques de l’aquifère : pH, température, disponibilité des réactifs et des nutriments.Cette présentation propose une analyse du stockage d’hydrogène dans un environnement réel, présentant des conditions naturellement favorables. Deux études menées à l’échelle du réservoir sont présentées : l’une portant sur le développement d’un nouveau site de stockage, l’autre sur la conversion d’un site existant de stockage de gaz naturel.La géométrie, le cyclage du gaz et les conditions initiales des deux modèles sont basées des données réelles issues du Bassin Parisien. Une attention particulière est portée à l’évolution temporelle de la composition du gaz soutiré et à la chimie de l’eau dans l’aquifère
How social pharmaceutical innovations are addressing problems of availability, accessibility and affordability of drugs for rare diseases
No full textInternational audienceBackgroundThe current organization of the pharmaceutical innovation system poses three major challenges for rare disease patients in terms of availability, accessibility and affordability of treatments. While some changes have emerged in the European Union to address some of these challenges, their impacts are not experienced uniformly across member states nor around the world. We have observed niche initiatives that are actively working to address those challenges within their local contexts. In a position paper in this journal, we characterized such initiatives as “social pharmaceutical innovation” (or SPIN): novel collaborations involving diverse sets of actors that break with conventional pharmaceutical innovation practices to develop interventions that address unmet societal needs of rare disease patients and that are not primarily market driven.ResultsHere we report on 15 cases of SPIN across Brazil, Canada, France and the Netherlands that we studied through semi-structured qualitative interviews (n = 151) with players involved in those cases. Our findings show how SPIN initiatives are reconfiguring pharmaceutical innovation networks to include a wider range of actors in redistributed and differentiated roles within innovation processes. Further, we find that SPINs are associated with changes in the ways data is gathered (often in clinical contexts rather than in conventional trials), and how evidence is assembled to improve access to the treatments. Finally, we demonstrate how SPINs are providing new routes for patients to access treatments for rare diseases, often at more affordable prices.ConclusionsWhile promising, SPINs are not perfect solutions for rare disease patients or the broader challenges to the pharmaceutical innovation system. SPINs are specific solutions adapted to the particulars of local framing, institutions, national policy and care contexts of rare diseases, and should be developed as such. Our findings support these recommendations for SPIN: use local knowledge and expertise in crafting SPINs; develop comprehensive strategies for data governance, access and ownership; and explore new economic models to recoup investments and/or sustain future initiatives. We invite collaboration on these topics and emerging SPIN initiatives so as to support efforts at addressing challenges of availability, accessibility and affordability of treatments for rare diseases patients
Quels chemins pour la décarbonation des pays en voie de développement ? Propos Introductifs : l’Afrique
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Effect of mechanical recycling and virgin PBR addition on the properties of ABS recovered from WEEE: A multi-technique characterization
No full textInternational audienceWith the growing use of electrical and electronic devices, the volume of waste electrical and electronic equipment (WEEE) continues to increase, posing a major environmental and recycling challenge. Acrylonitrile-butadiene-styrene (ABS) is one of the most common thermoplastics found in WEEE, its recovery is complicated by contamination, heterogeneity, and degradation. While mechanical recycling of ABS is widely practiced, the impact of specific processing steps on the chemical and physical properties of the recyclate remains insufficiently explored. This study investigates the effect of shredding and extrusion, as well as the integration of virgin polybutadiene rubber (PBR), on the morphology, chemical structure, and thermal stability of ABS-rich WEEE recyclates. A multi-analytical approach, combining Fourier transform infrared spectroscopy (FT-IR), transmission electron microscopy (TEM), scanning electron microscopy (SEM), energy dispersive X-ray analysis (EDX), inductively coupled plasma optical emission spectroscopy (ICP-OES) and thermal analysis − was employed to fully characterize the materials. Our findings show that extrusion improves sample homogeneity and removes some contaminants (e.g., Ba, Cl), leading to a significant increase in thermal stability (T10% +30 °C). The addition of virgin PBR contributes to enhanced internal cohesion and a fibrous morphology. This work provides a robust methodology for distinguishing processing-related changes from compositional variability in real-world recycled plastics. The approach can support the development of advanced processing strategies for polymer waste streams
Domain Generalization for Time Series: Enhancing Drilling Regression Models for Stick-Slip Index Prediction
No full textInternational audienceThis paper provides a comprehensive comparison of domain generalization techniques applied to time series data within a drilling context, focusing on the prediction of a continuous Stick-Slip Index (SSI), a critical metric for assessing torsional downhole vibrations at the drill bit. The study aims to develop a robust regression model that can generalize across domains by training on 60 second labeled sequences of 1 Hz surface drilling data to predict the SSI. The model is tested in wells that are different from those used during training. To fine-tune the model architecture, a grid search approach is employed to optimize key hyperparameters. A comparative analysis of the Adversarial Domain Generalization (ADG), Invariant Risk Minimization (IRM) and baseline models is presented, along with an evaluation of the effectiveness of transfer learning (TL) in improving model performance. The ADG and IRM models achieve performance improvements of 10% and 8%, respectively, over the baseline model. Most importantly, severe events are detected 60% of the time, against 20% for the baseline model. Overall, the results indicate that both ADG and IRM models surpass the baseline, with the ADG model exhibiting a slight advantage over the IRM model. Additionally, applying TL to a pre-trained model further improves performance. Our findings demonstrate the potential of domain generalization approaches in drilling applications, with ADG emerging as the most effective approach