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: 10.4121/83abb8a2-73f8-4f45-994c-a904268757ae

Schweidtmann, Artur M.; Gao, Qinghe; Kleinpaß, Marvin; Kondakov, Valentin (2024): KG-tool: Python package for accessing the Chemical Engineering Knowledge Graph (ChemEngKG). Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/83abb8a2-73f8-4f45-994c-a904268757ae.v1

Usage statistics

Categories

• Chemical Engineering
• Engineering

Keywords

Artificial intelligence chemical engineering chemical process development flowsheet simulations knowledge graph

Licence

MIT

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by Artur M. Schweidtmann, Qinghe Gao, Marvin Kleinpaß, Valentin Kondakov

This software is a Python package for accessing the ChemEngKG). ChemEngKG is a comprehensive knowledge graph that captures the vast and diverse information landscape of chemical engineering. It integrates information from various sources including research publications, patents, textbooks, and industry standards to provide a structured and interconnected representation of the knowledge domain. Our goal is to facilitate knowledge discovery and dissemination in chemical engineering and to support various applications such as process design, optimization, and innovation. Whether you are a student, researcher, or practitioner in chemical engineering, we hope that ChemEngKG will serve as a valuable resource to enhance your knowledge and productivity. 

History

• 2024-01-10 first online, published, posted

Publisher

4TU.ResearchData

Format

python scripts (.py), makedown files (.md)

Associated peer-reviewed publication

Digitization of chemical process flow diagrams using deep convolutional neural networks

Organizations

TU Delft, Faculty of Applied Sciences, Department of Chemical Engineering, Process Intelligence Research