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.
 

IFP Energies nouvelles is a French public-sector research, innovation and training center. Its mission is to develop efficient, economical, clean and sustainable technologies in the fields of energy, transport and the environment. For more information, see our WEB site. 
IFPEN offers a stimulating research environment, with access to first in class laboratory infrastructures and computing facilities. IFPEN offers competitive salary and benefits packages. All PhD students have access to dedicated seminars and training sessions. 
 

Academic requirements    University Master degree in Analytical Chemistry, Data Science     
Language requirements    English level B2 (CEFR), French or willingness to learn French