Machine Learning Modelling for Predicting the Efficacy of Ionic Liquid-Aided Biomass Pretreatment

The influence of ionic liquid (IL) characteristics, lignocellulosic biomass (LCB) properties, and process conditions on LCB pretreatment is not well understood. In this study, a total of 129 experimental data on LCB (grass, agricultural, and forest residues) pretreatment using imidazolium, triethylamine, and choline-amino acid ILs were compiled to develop machine learning (ML) models for cellulose, hemicellulose, lignin, and solid recovery. Following data imputation, a bilayer artificial neural network (ANN) and random forest (RF) regression, the two most widely adopted ML models, were developed. The full-featured ANN following Bayesian hyperparameter (HP) optimisation offered excellent fit on training (R²: 0.936–0.994), though cross-validation (R₂CV) performance remained marginally poor, i.e. between 0.547 and 0.761. The fitness of HP-optimised RF models varied between 0.824 and 0.939 for regression, and between 0.383 and 0.831 in cross-validation. Temperature and pretreatment time had been the most important predictors, except for hemicellulose recovery. Bayesian predictor selection combined with HP optimisation improved the R²CV boundary for ANN (0.555–0.825), as well as for RF models (0.474–0.824). As predictive performance of the models varied depending on target response, use of a larger homogeneous dataset may be warranted. The predictive modelling framework for LCB pretreatment, developed in this study, can be extended to similar biochemical process systems.