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Implementation and utilization of data science/machine learning methods to unravel the rheological properties of paraffinic diesel fuels

About This PhD Project

Project Description

Besides being a fuel, diesel has also other important roles, such as: lubrication, cooling and cleaning of the motor’s systems. The presence of organic (asphaltenes, waxes) or inorganic (water, nanoparticles) contaminants can hinder these properties by changing its rheology. For instance, wax crystallization and deposit is a pathological phenomenon taking place in paraffin-rich diesels under low temperature and/or high pressures. These crystals can block filter’s pores, reducing the injection rate and durability of the combustion system.

Even more importantly, novel environmental constraints aiming the reduction of SOx emissions impose diesel fuels to have very low sulfur concentrations, mainly those destined to maritime utilization. In order to reduce the sulfur amount, hydrodesulfurization (HDS) processes at the refinery sites must be employed. The consequence of this process is the increase of the concentration of aliphatic chains (paraffins) in diesel, what decreases its rheological properties by the wax-deposition mechanism. In brief, reducing sulfur concentration in diesel is intimately linked to some loss of its rheological properties, namely, low viscosity.

Due to technological and methodological limitations, details of the kinetics of crystallization at the molecular scale are not yet well-understood. Moreover, how one can suppress this phenomenon by a rational addition of well-tailored additives is another challenge without answer up to now. Numerical simulations based on classical molecular dynamics are capable of following directly the dynamics of alkyl chains under thermodynamic conditions, and stablish a link between diesel’s viscosity, its analytical composition and the thermodynamic conditions.

Given the high number of parameters involved (molecular composition and distribution, temperature, pressure, presence of organic and/or inorganic contaminants, presence of additives…), such task demands the study of thousands of different systems in order to infer a possible structure-condition-property link. To circumvent this problem, in this project, the successful candidate will use Data Science/Machine Learning methods to deal with such number of systems in a semi-automatized way. In this way, s/he will be capable of extracting, by employing finely-tuned algorithms, the key molecular, analytical and thermodynamic conditions that have, in fine, an impact on the rheological properties of diesel fuels.

The successful candidate possesses a background in Engineering, Physics, Chemistry or Material’s Science. S/he has knowledge in thermodynamics and is capable of using programming languages (preferably python). Also, s/he needs to demonstrate a great interest for molecular simulations and having some experience on it as well as machine learning approaches is a considerable plus. Excellent oral and written communication skills are mandatory since the researcher will work in close contact with academic collaborators and our industrial partner. Fluency in English is mandatory, French would be a plus.

Duration: 36 fully-funded months

Salary: in accordance with French regulations (around 1750€/month gross before taxes)

Starting date: before March 1st 2019.

Location: IPREM, Pau, France

How to apply: a CV and a cover letter must be sent to Dr Hugo SANTOS SILVA ()

The University of Pau and Pays de l'Adour (L'Université de Pau et des Pays de l'Adour or UPPA) was founded in 1972. It is a multi-site establishment, based in Pau (département of Pyrénées-Atlantiques) but also in Bayonne, Tarbes and Mont-de-Marsan in the Adour river basin. Coming administratively under the Academy of Bordeaux, it is the third largest university in southwestern France (after Bordeaux and Toulouse), with some 12,000 students.

The Institute of Analytical Sciences and Physico-Chemistry for Environment and Materials (IPREM) is a Joint Research Unit CNRS / UPPA (UMR 5254). IPREM members are interested in the development of fundamental knowledge in physico-chemistry, analytical chemistry and microbiology, in relation to applications concerning the structure of the living, the management of the environment and the functional properties of different classes of materials. Their skills are based on analytical strategies, modeling, physico-chemical approaches, fine studies of structures and reactivity, development, characterization and implementation at different scales. They make it possible to display an original position in the field of applications in many industrial sectors both at national and international level.

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