Machine learning prediction of cellulose-rich materials from biomass pretreatment with ionic liquid solvents.

Ionic liquid solvents (ILSs) have been effectively utilized in biomass pretreatment to produce cellulose-rich materials (CRMs). Predicting CRM properties and evaluating multi-dimensional relationships in this system are necessary but complicated. In this work, machine learning algorithms were applied to predict CRM properties in terms of cellulose enrichment factor (CEF) and solid recovery (SR), using 23-feature datasets from biomass characteristics, operating conditions, ILSs identities, and catalyst. Random forest algorithm was found to have the highest prediction accuracy with RMSE and R2 of 0.22 and 0.94 for CEF, as well as 0.07 and 0.84 for SR, respectively. Highly influential features on making predictions were mainly from biomass characteristics andILS treatment'soperating conditions, totally contributed 80% on CEF and 60% on SR. One- and two-way partial dependence plots were used to explain/interpret the multi-dimensional relationships of the most important features. Our findings could be applied in designing new ILSs and optimizing the process conditions.

• Publication year

  2021

• Venue

  Bioresource Technology

• Publication date

  2021-01-02

• Fields of study

  Medicine, Materials Science, Chemistry, Environmental Science

• Identifiers
  DOI 10.1016/j.biortech.2020.124642PMID 33418349
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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