Analysis and characterization

Today, air quality is an issue of major concern when it comes to public health. To protect it, it is necessary to reduce emissions but also monitor global chemical changes taking place in the atmosphere. From the economic and safety points of view, it is also important to monitor industrial gas emissions. It was to tackle these different aspects that IFPEN’s researchers began developing a range of technological solutions, within the context of the Flair Suite™ project.

One way to decrease the amount of atmospheric CO2 is to reduce greenhouse gas emissions of fossil origin, but it is also possible to increase the quantities of carbon stored in the soil. (...) In the current context of climate change, understanding the evolution of carbon in the ground is critically important. It is for this reason that IFPEN wanted to study the potential offered by Rock-Eval®, a flagship of oil research...

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.

Today, characterization of geological reservoirs, a long-standing theme in petroleum exploration, becomes a base of interest for a variety of applications, such as CO2 and hydrogen storage as well as geothermal energy. In recent years, the combined use of 3D microtomography (or micro-CT ) imaging and advanced simulation techniques has allowed the emergence of a digital approach to computing the petrophysical properties of reservoir rocks (Digital Rock Physics). This represents a real complement - and in some cases an alternative - to traditional laboratory measurements.

The design of high-quality porous materials is a major challenge for the energy efficiency of industrial processes in the fields of catalysis and biocatalysis and separation and purification operations. For such applications, these materials derive their properties of interest from their specific microstructure, incorporating a large quantity of empty spaces that are organized and connected on a nanometric scale. IFPEN and Saint Gobain Research Provence (SGRP) joined forces to acquire a tool that will ultimately facilitate the development of porous materials optimized for given usages.

Platinum nanoparticles supported on chlorinated γ-alumina are used in bifunctional heterogeneous catalysts, which are central to numerous industrial processes. An atomic-scale study...

The development of production processes for fuel and platform molecules from lignocellulosic biomass requires knowledge of the chemical composition, on a molecular scale, of the various liquid products generated...