Identifying descriptors of fast pyrolysis bio-oil thermal and storage stability through detailed multi-technique characterization and data analysis approaches

The urgency to combat global warming has led to extensive research into renewable energy sources. Among these, biofuel is emerging as a promising alternative to petroleum. One of the pathways of biofuel production is through the conversion of biomass, a high abundance feedstock contrary to vegetable oils/animal fats presently used for the production of drop-in biofuels. Conversion of biomass through fast pyrolysis is commercially viable however the obtained bio-oils are characterized by poor stability. Indeed, this thermal instability results in bio-oil polymerization causing catalyst deactivation and reactor plugging in conversion processes. Moreover, bio-oils storage at room temperature provokes progressive composition changes impacting product quality. There are different strategies to bolster bio-oil stability, however the choice of the most efficient strategy is not always clear as the present understanding is mostly based on model compounds behavior and bio-oils macroscopic physico-chemical properties evaluation. To design most efficient upgrading approaches and mitigate long-term storage effects, underlying detailed molecular composition changes of bio-oils need to be better understood.
The objective of this PhD work is to develop a multi-technique detailed characterization strategy for bio-oils with a goal to highlight compositional changes due to heating and storage. An evaluation of analytical strategy involving fractionation, derivatization and a combination of powerful analytical techniques such as GC×GC-HRMS, LC-HRMS, FT-ICR MS or NMR is envisioned to achieve comprehensive characterization of these complex samples. In the next stage a methodology for multi-technique data analysis will be developed based on chemometrics/machine learning approaches with a goal to highlight compositional changes and deliver descriptors of bio-oil stability, enabling evidence-based choices for stabilization strategies.

Keywords: biofuels, bio-oils, stability, descriptors, molecular analysis, data analysis

• Academic supervisor    Dr Marion LACOUE-NEGRE, IFPEN, ORCID: 0000-0002-1092-2223
• Doctoral School    ED STIC, Université Paris Saclay
• IFPEN supervisor    Dr Aleksandra LELEVIC, ORCID: 0000-0002-4783-2475
• PhD location    IFPEN, Solaize, France  
• Duration and start date    3 years, starting in the fourth quarter 2026 (Novembre 2)
• Employer    IFPEN
• Academic requirements    University Master degree in Analytical Chemistry, Data Science     
• Language requirements    English level B2 (CEFR), French or willingness to learn French    

To apply, please send your cover letter and CV to IFPEN supervisors below