Sparse Regression Approach to Modelling the Effect of Ionic Liquid Acidity in Biomass Fractionation ⁎ ⁎ Suhaib Nisar is grateful to the Department of Chemical Engineering at Imperial College London for a PhD scholarship and to the EPSRC Centre for Doctoral Training in Next Generation Synthesis & Reaction Technology for the PhD studentship under grant EP/S023232/1.

Fractionation of lignocellulosic biomass is a crucial step to provide cellulose, lignin, and hemicellulose for further processing. This paper is concerned with modelling biomass fractionation using the ionoSolv process, which employs low-cost ionic liquid water mixtures, with a special focus on describing the effect of acid:base ratio of the mixture on process performance. We build on an existing semi-mechanistic modelling framework describing the solvent extraction of three main biopolymers from woody biomass for varying fractionation temperature, time, and solids loading. Since the effect of acidity is poorly understood from a mechanistic standpoint, we use sparse regression with lasso regularisation to incorporate it in the semi-mechanistic model. We investigate both polynomial and exponential functional forms and find that the latter yields more physically-consistent results. This enabled us to recalibrate the parameters of the combined semi-mechanistic and sparse data-driven models simultaneously to accurately predict the effect of varying acid:base ratio. This hybrid modelling framework opens new opportunities for further analysis and optimisation of ionic liquid-based biomass fractionation processes.