Modeling lignin extraction with ionic liquids using machine learning approach

Lignin, next to cellulose, is the second most common natural biopolymer on Earth, containing a third of the organic carbon in the biosphere. For many years, lignin was perceived as waste when obtaining cellulose and hemicellulose and used as a biofuel for the production of bioenergy. However, recently, lignin has been considered a renewable raw material for the production of chemicals and materials to replace petrochemical resources. In this context, an increasing demand for high-quality lignin is to be expected. It is, therefore, essential to optimize the technological processes of obtaining it from natural sources, such as biomass. In this work, an investigation of the use of machine learning-based quantitative structure-property relationship (QSPR) modeling for the preliminary processing of lignin recovery from herbaceous biomass using ionic liquids (ILs) is described. Training of the models using experimental data collected from original publications on the topic is assumed, and molecular descriptors of the ionic liquids are used to represent structural information. The study explores the impact of both ILs' chemical structure and process parameters on the efficiency of lignin recovery from different bio sources. The findings give an insight into the extraction process and could serve as a foundation for further design of efficient and selective processes for lignin recovery using ionic liquids, which can have significant implications for producing biofuels, chemicals, and materials.