Kinetic modeling of the hydrodeoxygenation of pyrolysis bio-oil and hybrid modeling methodology

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Kinetic modeling of the hydrodeoxygenation of pyrolysis bio-oil and hybrid modeling methodology

Pyrolysis of lignocellulosic biomass produces bio-oil, a renewable liquid that is highly oxygenated and unstable, and so must be upgraded before use as a fuel or refinery feedstock. Catalytic hydrotreatment (hydrodeoxygenation, HDO) is one of the main upgrading routes, and it should be optimized to obtain the desired oil quality with reasonable hydrogen consumption and catalyst lifetime. For this purpose, kinetic models can help us to understand the reaction network and to guide operating conditions. Therefore, it is important to develop predictive kinetic models for bio-oil hydrotreatment.

Description

Bio-oil hydrotreatment is difficult to model. Bio-oil contains hundreds of oxygenated compounds that react simultaneously, catalyst deactivation is strong, and the experimental data is limited and noisy. A first step is therefore to assess what the available data can support, and to compare it with published lumped kinetic models. In the longer term, hybrid models, which combine mechanistic kinetics with machine-learning components, could improve predictions as more data becomes available. In this regard, the hydrotreatment of vegetable oils (HVO), for which extensive data and validated kinetic models exist at [institution], is a well-defined problem on which to test the hybrid methodology.

The main goal of this internship is to build a simple kinetic model for the hydrotreatment of pine wood pyrolysis bio-oil from the experimental data available in the laboratory, and to establish what these data can and cannot tell us. In parallel, a hybrid modeling methodology will be tested on HVO hydrotreatment, as a guide for future hybrid modeling of bio-oil.

The work will be divided into the following tasks:

Conduct a literature review on bio-oil upgrading and on kinetic models for hydrotreatment of bio-oil and model compounds
Analyze the bio-oil experimental data and compare with published lumped kinetic models
Develop a simple lumped kinetic model for bio-oil hydrotreatment, estimate its parameters, and identify the dominant pathways and key parameters
Develop a hybrid model for HVO hydrotreatment, first on synthetic data generated from the existing kinetic model, then on real data, and compare it with the purely mechanistic model
Propose recommendations for future bio-oil experiments, including the data needed to apply the hybrid approach

This internship may be followed by a PhD project.

Required profile

Chemical Engineering with programming experience (python)

Additional information
- Duration of the internship : 6 months
- Workplace : IFPEN Lyon, Rond-point de l'échangeur de Solaize, 69360 Solaize
- Transport : public transportation / personal vehicle
- Paid internship 1150€/month (gross)