PhD position in chemical reaction engineering (Kinetic modeling & thermal runaway)

At the lab scale, when we think about chemical production, the focus is on optimizing yield, distribution, or conversion by testing different operating conditions (pressure, concentration, temperature, etc.) and at isothermal conditions. Then, we develop kinetic models, but these models are reliable only for operating conditions quite close to the optimum production conditions and in isothermal conditions.
By working at isothermal conditions, one aspect could be overlooked: heat transfer and the risk of thermal runaway. Thermal runaway remains a significant safety risk in the chemical industry, accounting for nearly 25% of accidents [1].
The QUANTASTIC project (ANR-25-CE51-7405) aims to develop innovative methodologies for early detection and mitigation of thermal runaway by combining:
-Multifactorial kinetic modelling
-Calorimetry
-Quantum chemistry (DFT)
-Artificial intelligence
This thesis will evolve in the framework of QUANTASTIC project (ANR-25-CE51-7405).
Our previous research showed that biomass valorization involves exothermic reactions that could lead to thermal runaway scenarii [2-4].
The QUANTASTIC’s consortium fiercely believes in combining chemical reaction engineering and DFT calculation for a better understanding of thermal runaway reactions.

The recruited QUANTASTIC PhD student will:
-Contribute to the development of an experimental setup,
-Develop and evaluate different kinetic models, including runaway scenarios,
-Work in close collaboration with DFT QUANTASTIC researchers to increase model accuracy and with Automatic QUANTASTIC researchers to improve detection.

References:
1. A. Dakkoune, L. Vernieres-Hassimi, S. Leveneur, D. Lefebvre, L. Estel, Analysis of thermal runaway events in French chemical industry, Journal of Loss Prevention in the Process Industries, 62 (2019) 103938. https://doi.org/10.1016/j.jlp.2019.103938
2. Y. Wang, L. Vernières-Hassimi, V. Casson-Moreno, J.-P. Hébert, S. Leveneur, Thermal risk assessment of levulinic acid hydrogenation to γ-valerolactone, Organic Process Research & Development, 22(9) (2018) 1092-1100. https://doi.org/10.1021/acs.oprd.8b00122
3. W. Y. Pérez-Sena, T. Salmi, L. Estel, S. Leveneur, Thermal risk assessment for the epoxidation of linseed oil by classical Prisleschajew epoxidation and by direct epoxidation by H2O2 on alumina, Journal of Thermal Analysis and Calorimetry, 140 (2020) 673–684. https://doi.org/10.1007/s10973-019-08894-2
4. Yanjun Wang, Igor Plazl, Lamiae Vernières-Hassimi, Sébastien Leveneur, From calorimetry to thermal risk assessment: γ-valerolactone production from the hydrogenation of alkyl levulinates, Process Safety and Environmental Protection, 144 (2020) 32-41 https://doi.org/10.1016/j.psep.2020.07.017

The PhD candidate will be hosted at IRCELYON (UMR 5256 CNRS – University Claude Bernard Lyon 1), located on the LyonTech-La Doua campus in Villeurbanne. The laboratory brings together more than 80 permanent staff members and around 50 PhD students, and conducts cutting-edge research in catalysis, chemical reaction engineering, and environmental chemistry.

The candidate will join the ATARI team, specialized in kinetic modelling, calorimetry, and thermodynamic analysis of chemical reactions. The PhD will be supervised by Sébastien Leveneur (HDR) and carried out within the framework of the ANR QUANTASTIC project, in close interaction with partners in quantum chemistry (DFT) and control engineering.

-Master's thesis in chemical engineering (or provisional certificate) OR master’s thesis in chemistry but with good knowledge in chemical reaction engineering (or provisional certificate);
-Experience in experimental work and solid background in analysis;
-Experience in catalyst preparation and characterization will be appreciated.
-The candidate should have good knowledge in kinetic modeling;
-Fluent in English;
-This thesis will be embedded in the QUANTASTIC project. Thus, the Ph.D. student must also be open to other scientific activities, such as automatic and DFT methods.