Database underlying the research for the development of machine learning models for predicting the physicochemical properties of hydrochar

This dataset was developed as part of research aimed at building machine learning models to predict the physicochemical properties of hydrochar produced via hydrothermal carbonization (HTC) of biomass. The study focuses on understanding how various process parameters and feedstock properties influence hydrochar characteristics. Data were collected through experimental HTC processes under controlled laboratory conditions, using a variety of biomass types and process settings. The dataset includes both input variables (such as reaction temperature, residence time, stirring rate, and feedstock composition) and output variables describing the elemental composition and yield of the resulting hydrochar. The data type is quantitative, comprising variables representing chemical and process parameters.