AMARETTO

Integration of analytical tools and machine learning to identify markers and predict reactivity of lignocellulosic biomass in enzymatic hydrolysis.

Enzymatic hydrolysis (EH) is a crucial method for converting second-generation lignocellulosic biomass into ethanol or platform molecules. Currently, due to a lack of understanding regarding the relationship between the physical and chemical properties of biomass and its reactivity, and the absence of predictive tools, costly experimental work under representative conditions is still necessary.

To improve the cost-effectiveness of biochemical biomass conversion processes, it is essential to establish a connection between the physical and chemical properties of lignocellulosic biomass and the observed sugar yield.

Amaretto - Schema projet
Amaretto targeted project organisation

 

Project objectives

Analytical methodology:

Develop and evaluate an analytical methodology:

  • Sample selection: choose a range of samples with sufficient variability.
  • Multi-technique analysis of hydrolysates: detailed molecular analysis of complex mixtures.
  • Multi-technique analysis of residues: analyze the solid residues (PT) at different scales.
  • Data structuring: format and organize multidimensional and heterogeneous data sets.
  • Link reactivity data: associate reactivity data with each sample.
Markers identification:

Identification of reactivity markers:

  • Inhibition markers in hydrolysates: identify substances in hydrolysates that inhibit enzymatic hydrolysis.
  • Key properties of solids: determine the properties of the solid residues that influence reactivity in EH.
 
Develop a predictive model for biomass reactivity.

 

 

Project lifetime:
 

June 2023 - May 2027

 

Scientific manager:
 

Agnès Le Masle (IFPEN)

 

The consortium:
 

Higher education establishment
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Research institutes
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