pyTEP: A Python package for interactive simulations of the Tennessee Eastman process. Reinartz, C. & Enevoldsen, T. T. SoftwareX, 18:101053, June, 2022. ![pyTEP: A Python package for interactive simulations of the Tennessee Eastman process [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex pyTEP is an open-source simulation API for the Tennessee Eastman process in Python. It facilitates the setup of complex simulation scenarios and provides the option of interactive simulation. The Tennessee Eastman process has been the go-to benchmark for statistical process monitoring and machine learning based fault-detection approaches for continuous chemical processes in recent years, but its potential outside these domains remains largely untapped. Existing simulators are tailored towards simulations of stationary operating conditions in the presence of faults, but further extensions for more complex simulation scenarios are time-consuming, which may discourage researchers from adopting the process. Through pyTEPs API, users can configure simulations, change operating conditions and store simulation data without being exposed to the underlying mechanics of the simulator. In addition to the newly introduced features, pyTEP promises more versatility and more straightforward usage than existing TEP simulators.
@article{reinartz_pytep_2022,
title = {{pyTEP}: {A} {Python} package for interactive simulations of the {Tennessee} {Eastman} process},
volume = {18},
issn = {2352-7110},
shorttitle = {{pyTEP}},
url = {https://www.sciencedirect.com/science/article/pii/S2352711022000449},
doi = {10.1016/j.softx.2022.101053},
abstract = {pyTEP is an open-source simulation API for the Tennessee Eastman process in Python. It facilitates the setup of complex simulation scenarios and provides the option of interactive simulation. The Tennessee Eastman process has been the go-to benchmark for statistical process monitoring and machine learning based fault-detection approaches for continuous chemical processes in recent years, but its potential outside these domains remains largely untapped. Existing simulators are tailored towards simulations of stationary operating conditions in the presence of faults, but further extensions for more complex simulation scenarios are time-consuming, which may discourage researchers from adopting the process. Through pyTEPs API, users can configure simulations, change operating conditions and store simulation data without being exposed to the underlying mechanics of the simulator. In addition to the newly introduced features, pyTEP promises more versatility and more straightforward usage than existing TEP simulators.},
language = {en},
urldate = {2022-05-04},
journal = {SoftwareX},
author = {Reinartz, Christopher and Enevoldsen, Thomas T.},
month = jun,
year = {2022},
keywords = {Chemical process simulation, Python, Simulation framework, Tennessee Eastman process},
pages = {101053},
}
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