Machine-learning based optimisation of corrosion inhibitor formulations for CO2-containing aqueous environments
About the Project
The EPSRC Centre for Doctoral Training in Future Fluid Dynamics is now recruiting to this fantastic PhD opportunity in partnership with SLB.
As a student on the CDT you will participate in a four-year programme that combines an integrated MSc (completed over the first two years) paired with a three-year PhD-level research programme.
The PhD project: Machine-learning based optimisation of corrosion inhibitor formulations for CO2-containing aqueous environments
Internal corrosion of carbon steel is a major issue for energy infrastructure, leading to economic and safety concerns. One method of mitigating corrosion is through the injection of chemicals (corrosion inhibitors) which function through surface adsorption.
Researchers are seeking next-generation inhibitors that are environmentally friendly, sustainable, and effective at higher temperatures. However, the immense diversity of corrosion inhibitor formulations and the time-consuming nature of conventional corrosion test methods limits the speed and capability at which such chemistries can be developed, as well as resulting in significant material usage. To this end, numerous methods to rapidly evaluate the performance of corrosion inhibitor formulations have been proposed. However, methodologies developed thus far suffer from a number of limitations, such as time-consuming manual configuration and lack of systematic optimisation of formulations.
These key limitations result in sub-optimal inhibitor recipes being adopted commercially. This project will address these challenges by exploiting University of Leeds recent advances in corrosion testing, machine learning and AI-enabled corrosion analysis and optimisation methods, to create a novel fluidic high-throughput chemical screening system, coupled with data-driven analysis to accelerate the optimisation of corrosion inhibitors for corrosion of carbon steel in energy systems.
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