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

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Abstract

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.

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Data Availability

Data in this study are available on request to the corresponding author.

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Acknowledgements

D. H. and B. M. would like to acknowledge Karunya Institute of Technology and Sciences, Coimbatore for providing every essential support to perform the experiments and complete this research work.

Funding

This work is financially supported by Karunya Institute of Technology and Sciences, Coimbatore.

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Authors and Affiliations

  1. Division of Biotechnology, Karunya Institute of Technology and Sciences, Coimbatore, 641114, India

    Biswanath Mahanty & Dibyajyoti Haldar

  2. Division of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, 641114, India

    Munmun Gharami

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  1. Biswanath Mahanty
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Biswanath Mahanty: conceptualisation, software, writing — original draft, reviewing and editing. Munmun Gharami: data curation, reviewing and editing. Dibyajyoti Haldar: conceptualisation, data curation, reviewing and editing.

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Correspondence to Biswanath Mahanty or Dibyajyoti Haldar.

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Mahanty, B., Gharami, M. & Haldar, D. Machine Learning Modelling for Predicting the Efficacy of Ionic Liquid-Aided Biomass Pretreatment. Bioenerg. Res. (2024). https://doi.org/10.1007/s12155-024-10747-2

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