Stoichiometry impact on the optimum efficiency of biomass conversion to biofuels

International audienceBiomass has the specific characteristic of being included in a short regeneration cycle that minimizes its ecological impact and should give it a preferential role in the energy transition. The scale up in the deployment of bioenergy requires an objective approach to processes. It is necessary to identify, according to a defined and available biomass, the most appropriate processes and products to extend their deployment This requires deep process analysis to identify achievable optimizations and opportunities of improvement. In order to provide criteria to identify the upper theoretical limits of biomass conversion, a theoretical approach to the conversion of two biomass (lignocellulosic and microalguae) into simple energy vector as alkanes, alcohols, carbon monoxide or hydrogen is carried out. Modelling highlights the importance of stoichiometry in the feasibility and efficiency of biomass conversions. The impact of hydrogen supply and its energy cost in improving conversion efficiency is also underlined. In terms of biomass conversion results, microalgae provide better conversion efficiency than lignocellulosic biomass. For these reactions, an optimal carbon conversion ratio is identified. The optimum conversion ratios are about 36% to 46% for short chains such as methane or methanol and 64% to 75% for long chains. • Stoichiometry plays a major role in the biomass conversion • Optimum limit for biomass conversions are identified for alkanes, alcohols, H 2 and CO production • Hydrogen supply source can improve conversion efficiency • Proposal of a methodology to calculate efficiency for biomass conversio

« De la régénération des objets techniques : flux de matière et mouvements de paysages »

International audienc

Scalable inference of ordinary differential equation models of biochemical processes.

Ordinary differential equation models have become a standard tool for the mechanistic description of biochemical processes. If parameters are inferred from experimental data, such mechanistic models can provide accurate predictions about the behavior of latent variables or the process under new experimental conditions. Complementarily, inference of model structure can be used to identify the most plausible model structure from a set of candidates, and, thus, gain novel biological insight. Several toolboxes can infer model parameters and structure for small- to medium-scale mechanistic models out of the box. However, models for highly multiplexed datasets can require hundreds to thousands of state variables and parameters. For the analysis of such large-scale models, most algorithms require intractably high computation times. This chapter provides an overview of the state-of-the-art methods for parameter and model inference, with an emphasis on scalability