Unlocking Lignin’s Potential: Compositional Insights and Kinetic Modeling of Its Catalytic Hydropyrolysis

Decarbonizing the chemical and energy industries is essential for achieving international climate goals. In this context, second-generation lignocellulosic biomass represents a sustainable alternative to fossil resources for the production of bio-based chemical intermediates. Among its constituents, lignin — an abundant aromatic biopolymer from the paper and biorefinery industries — is currently a largely under-exploited resource. However, its efficient conversion remains a significant scientific and industrial challenge. Indeed, the chemical complexity of lignins, their structural heterogeneity and the challenges associated with their characterization still limit the detailed understanding of the conversion mechanisms and hinder the development of optimized industrial processes.
This doctoral thesis aims to overcome these barriers by developing advanced numerical tools that provide a more accurate description of the composition, structure and reactivity of lignins during their conversion. The doctoral student will work on:
•    molecular reconstruction of lignins from experimental analytical data in order to accurately represent their structural diversity,
•    stochastic modeling of catalytic hydropyrolysis, a promising depolymerization process for the production of stabilized biooils and platform molecules.
This research will provide both innovative computational tools and fundamental knowledge to better understand and predict the behavior of different lignins during catalytic hydropyrolysis. This will facilitate the selection of optimal operating conditions for different lignins and enable the exploration of more efficient and more sustainable recovery pathways.
 

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 Chemical Engineering or Computational Science
Language requirements    English level B2 (CEFR), French (willingness to learn French)
Other requirements    Strong interest in modeling, good knowledge of one or more programming languages (C, C++, Python, Matlab), open to multiple scientific fields.