Two-step machine learning-aided two-stage hydrothermal liquefaction of biomass for bio-oil upgrading to lower nitrogen content: Experimental verification and parameter optimization.

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    • Abstract:
      The two-stage hydrothermal liquefaction (HTL) is a promising process for converting biomass into low-nitrogen bio-oil. However, for two-stage HTL, there is a considerable lack of application of artificial intelligence tool. In this study, a two-step machine learning (ML) model was developed to optimizing bio-oil production of two-stage HTL. A total of 644 data points was collected, and a novel feature NH2-C (the mass ratio of amino to carbon) was used to enhancing ML models using Random Forest (RF), Gradient Boosting Regression (GBR), and Extreme Gradient Boosting (XGB) algorithms. The XGB multi-task model demonstrated the best performance (train R2 of 0.98, test R2 of 0.94 and 0.83). The nitrogen content in feedstock is the decisive factor for total nitrogen and nitrogen recovery rate of aqueous phase in HTL stage Ⅰ, while NH2-C for yield and nitrogen content of bio-oil in stage Ⅱ. The two-step ML model performed excellently in 22 validation experiments (with all errors ≤13.68%), and guided the bio-oil preparation with lower nitrogen content (5.46%) from Chlorella. This study provides a new approach for the application of multi-step ML in biomass HTL. [Display omitted] • Two-steps machine learning model is used to aided two-stage HTL bio-oil production. • A novel feature correlate to protein, NH2-C, is defined to enhance model performance. • N content of feedstock is the top important feature for TN_Ap and RN_Ap. • XGB algorithm show the best multi-task test R2 of 0.83–0.94. • The best models are verified in 22 sets and applied in Chlorella bio-oil preparation. [ABSTRACT FROM AUTHOR]
    • Abstract:
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