Machine learning-based characterization of hydrochar from biomass:
  Implications for sustainable energy and material production
Alireza Shafizadeh Hossein Shahbeik Shahin Rafiee Aysooda Moradi Mohammadreza Shahbaz Meysam Madadi Cheng Li Wanxi Peng Meisam Tabatabaei Mortaza Aghbashlo
Hydrothermal carbonization (HTC) is a process that converts biomass into
versatile hydrochar without the need for prior drying. The physicochemical
properties of hydrochar are influenced by biomass properties and processing
parameters, making it challenging to optimize for specific applications through
trial-and-error experiments. To save time and money, machine learning can be
used to develop a model that characterizes hydrochar produced from different
biomass sources under varying reaction processing parameters. Thus, this study
aims to develop an inclusive model to characterize hydrochar using a database
covering a range of biomass types and reaction processing parameters. The
quality and quantity of hydrochar are predicted using two models (decision tree
regression and support vector regression). The decision tree regression model
outperforms the support vector regression model in terms of forecast accuracy
(R2 > 0.88, RMSE < 6.848, and MAE < 4.718). Using an evolutionary algorithm,
optimum inputs are identified based on cost functions provided by the selected
model to optimize hydrochar for energy production, soil amendment, and
pollutant adsorption, resulting in hydrochar yields of 84.31%, 84.91%, and
80.40%, respectively. The feature importance analysis reveals that biomass
ash/carbon content and operating temperature are the primary factors affecting
hydrochar production in the HTC process.