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

DOI:10.4121/834c3f22-f46a-4808-a0fe-46298be6a133.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/834c3f22-f46a-4808-a0fe-46298be6a133

Datacite citation style

Abdeldayem, Omar; Al-Sakkari, Eslam; Ortiz, Darwin; Dupont, Capucine; Ferras, David et. al. (2025): Database underlying the research for the development of machine learning models for predicting the physicochemical properties of hydrochar. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/834c3f22-f46a-4808-a0fe-46298be6a133.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

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.

History

  • 2025-07-09 first online, published, posted

Publisher

4TU.ResearchData

Format

xlsx

Funding

  • Bio4Africa (grant code 101000762) EU Horizon 2020

Organizations

IHE Delft Institute for Water Education, Department of Water Supply, Sanitation and Environmental Engineering
TU Delft, Faculty of Civil Engineering and Geosciences, Department of Water Management

DATA

Files (2)