Machine learning predicting wastewater properties of the aqueous phase derived from hydrothermal treatment of biomass

Abstract

Hydrothermal treatment (HTT) is a potential technology for producing biofuel from wet biomass. However, the aqueous phase (AP) is generated inevitably in the process of HTT, and studies are lacking on the detailed exploration of AP properties. Therefore, machine learning (ML) models were built for predicting the pH, total nitrogen (TN), total organic carbon (TOC), and total phosphorus (TP) of the AP based on biomass feedstock and HTT parameters. Results showed that the gradient boosting decision tree (average testing R2 0.85–0.96) can accurately predict the above wastewater properties for both single- and multi-target models. ML-based feature importance indicated that nitrogen content of biomass, solid content, and temperature were the top three critical features for pH, TN, and TP, while those for TOC were reaction time, lipid, and temperature. This ML approach provides new insights to understand the formation and properties of the HTT AP by ML rather than time-consuming experiments.