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Understanding mineral deposit formation in heat exchangers to reduce corrosion risks
Geothermal fluids can be highly damaging even for supposedly resistant metal alloys. Whether it involves uniform corrosion or stress corrosion cracking, the phenomenon can be even more severe in the event of mineral deposit formation, which has the added disadvantage of reducing the thermal efficiency of the equipment affected. In order to gain a better understanding of these risks, research teams from IFPEN, INSA-Lyon, Mines de Saint-Etienne and the French Corrosion Institute have joined forces to conduct the GeoSteelCor project. Methodologies have been developed to control mineral deposit formation on metallic surfaces, in laboratory conditions, as well as more realistically in a high-pressure and high-temperature corrosion test loop. These methodologies have also been used to study the impact of mineral deposits on stress corrosion cracking.
Machine learning accelerates access to high-precision data for chemistry via ab initio molecular dynamics
Ab initio calculations consist in solving the Schrödinger equation for a set of atoms representing a chemical system of interest. In chemical reactivity, ab initio molecular dynamics (AIMD) make it possible, for example, to predict rate constants with a high degree of accuracy, such as in the case of zeolites with protons as active sites. Nevertheless, the conditions for obtaining results with the required accuracy can involve excessive calculation times. A recently developed method (Machine Learning Perturbation Theory) makes it possible to overcome this obstacle. The chosen application is that of alkene isomerization and cracking in large-pore zeolites.