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Issue 50 of Science@ifpen
News in brief

The in silico creation of molecular structures

What chemical engineer has never dreamed of having access to a tool that can directly identify a fluid (pure substance or mixture) on the basis of characteristics necessary to a given application context? This Holy Grail could become a reality thanks to the field of Chemoinformatics and its methods...
Individual page

Mathieu FERAILLE

Research engineer / project leader
Holder of an Engineering Graduate Degree in General Engineering from the "Ecole Polytechnique" (Palaiseau – France) and a Specialized Engineering Graduate Degree in Petroleum Engineering and Project
Science@ifpen - Issue 49
News in brief

Microfluidics and Chemoinformatics: a highly complementary approach to studying material/fluid compatibility

Pour de nombreuses applications industrielles, comme le recyclage chimique des plastiques, ou encore pour assurer la compatibilité entre polymères et nouveaux carburants, il est essentiel d’anticiper les interactions entre matériaux et fluides...
Impact of hydroclimatic and anthropological parameters on the dynamics of the Rhône delta
News in brief

Impact of hydroclimatic and anthropological parameters on the dynamics of the Rhône delta

Coastal sedimentary basins evolve under the effect of interactions between, on the one hand, hydroclimatic processes taking place in catchment areas, and on the other hand coastal marine processes that remodel the coastline. The evolution of these environments is naturally controlled by the climate, over different time periods (ranging from tens of years to thousands of years), through variations in sedimentary flows and erosion...
Cheminformatics and its descriptors: application to polymer/fluid compatibility
News in brief

Cheminformatics and its descriptors: application to polymer/fluid compatibility

Ensuring compatibility between polymers and fluids is essential in numerous industrial sectors: in the automotive sector, for example, the resistance of materials used in the fuel supply system is a vital consideration.
Individual page

Julien COATLÉVEN

Research engineer in scientific computing
Julien Coatléven graduated from ENSTA (Paris) and completed his doctoral thesis in Applied Mathematics at Ecole Polytechnique (Paris) and INRIA Rocquencourt. After completing post-doctoral research at
Issue 46 of Science@ifpen - Earth Sciences and Environmental Technologies
News in brief

Geoheritage and geodiversity accessible to all thanks to digital technology

Emerging in the 1990s, the notions of geoheritage and geodiversity have been receiving growing attention from academic communities, international organizations and public authorities. (...) It was in this context that, in 2020, IFPEN signed a partnership agreement with UNESCO, one of the objectives of which is to share digital tools facilitating the promotion of geoheritage and geodiversity to the general public...
Individual page

Delphine SINOQUET

Research engineer / project leader in optimization
PhD in applied mathematics
Master degree in numerical analysis (Paris 6 university) PhD in applied mathematics (Paris 13 university) : inversion problem of seismic tomography 2003-now : research engineer in applied mathematics
Issue 45 of Science@ifpen
News in brief

Faster “flash” calculations thanks to deep learning

A large number of simulators, whether they relate to the design of reaction processes, the evolution of oil reservoirs or combustion systems, require access to thermodynamic properties. In order to provide these properties, IFPEN has been developing a library of calculation modules, called “Carnot”, named after the famous French thermodynamics expert. These calculations, in particular those concerning phase equilibrium (also known as flash calculations), generally require the use of substantial calculation resources due to the complexity of the systems considered, and represent in many cases the most time-consuming step in the simulation process.
Issue 45 of Science@ifpen
News in brief

Semantic segmentation through deep learning in materials sciences

Semantic segmentation conducted on microscopy images is a processing operation carried out to quantify a material’s porosity and its heterogeneity. It is aimed at classifying every pixel within the image (on the basis of degree of heterogeneity and porosity). However, for some materials (such as aluminas employed for catalysis), it is very difficult or even impossible using a traditional image processing approach, since porosity differences are characterized by small contrasts and complex textural variations. One way of overcoming this obstacle is to tackle semantic segmentation via deep learning, using a convolutional neural network.