Signal processing/Data science

Coastal systems such as deltas are vulnerable to sea-level rise and erosion. The stability of deltas depends on the sedimentary input produced in the watershed, which is strongly impacted by climatic and anthropogenic factors, whose variations and increasing pressure lead to problems of resource management (aquifers) and land-use planning (bank instability, flood management, etc.). In order to predict the impact of different environmental change scenarios and thus enable the implementation of appropriate local policies, it is essential to have modeling tools capable of integrating the various hydroclimatic and anthropogenic parameters and their temporal evolution...

IFPEN is a global leader in the development of fossil feedstocks hydrotreatinga for clean fuel production. Processes from the same family now apply to a broader diversity of feedstocks: plastic and tire pyrolysis oil, in the context of chemical recycling, vegetable oils for biofuel production, etc. For these processes themselves to be eco-efficientb, beyond the targeted environmental benefit, their operating conditions need to be optimized by the use of kinetic or hybrid modelsc, as a function of the feedstocks employed and the specifications sought for the target products...

Urban road traffic is a significant source of pollutant emissions that impacts air quality. Being able to predict the dispersion of these emissions is of major importance for evaluating real exposure and planning traffic flows. To this end, a PhD research project proposed a modeling chain making it possible to simulate highly turbulent flows on a local urban scale and obtain two-dimensional spatial maps of pollutant concentration...

Data from NIRS are processed mathematically, via chemometric approaches, generally using a Partial Least Squares (PLS)-type model. This linear methodology is aimed at establishing a statistical relationship, represented by the maximum covariance, between an explanatory variable X and a response variable y. It has been successfully used at IFPEN to predict the properties of oil products and, in recent years, it has mirrored the evolution of new energy technologies (NET)...

Reactive fluid transport simulation has multiple applications - flows in porous media, combustion, process engineering - and requires thermodynamic equilibrium calculations (also knows as “flash” calculations). However, these calculations can take a long time and, as they are involved in large numbers in the simulations carried out, in practice they limit the latter to systems containing few chemical species or to restricted time and space scales...

IFPEN is a global leader in the development of catalysts and processes for clean fuel production. For these processes themselves to be eco-efficient1, it is necessary to optimize the coupling of catalysts with the operating conditions, as a function of the feedstocks used and the target specifications for the refined products. It is therefore useful to be able to draw on predictive models for the performance achieved, and machine learning can help improving these models...

While macroscopic models combined with experimental analysis of porosity are well established for geometrically simple pores, hierarchized and disordered microstructures defy existing frameworks and call into question conventional interpretations. We proposed a digital framework to help overcome this challenge, taking into account morphology, connectivity and pore size distribution...

Inside porous materials, physico-chemical phenomena such as matter transport, catalytic reactions and capillary effects are strongly influenced by the geometry of the pore networks, i.e., the degree of porosity, the distribution of pore sizes and their connectivity. (....) IFPEN and Saint Gobain Research Provence decided to tackle the problem differently, by exploring a new numerical approach...