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@inproceedings{Xenos and Kahrs:2017:10.1109/ECC.2016.7810424,
author = {Xenos and Kahrs, O and Leira, FM and Thornhill, NF},
doi = {10.1109/ECC.2016.7810424},
pages = {1025--1030},
publisher = {IEEE Conference Publications},
title = {Challenges of the application of data-driven models for the real-time optimization of an industrial air separation plant},
url = {http://dx.doi.org/10.1109/ECC.2016.7810424},
year = {2017}
}

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TY  - CPAPER
AB  - The optimization of the operation of chemical plants may require the development of mathematical models of the process units of a plant. These mathematical models can be either first-principles or data-driven models. The former type of modeling may be complex for the use in optimization and especially for online applications such as real time optimization. Available measured process data can be used to develop the latter type of modeling. Although data-driven models offer several benefits for online applications, there are some very significant challenges related to their development in a practical industrial implementation. This paper discusses the important aspects of the building of data-driven models and demonstrates the effects of these types of models on the optimization results. The current work demonstrates the application of a real time optimization framework applied to an industrial air compressor station of an air separation plant when the models are based on operating data.
AU  - Xenos
AU  - Kahrs,O
AU  - Leira,FM
AU  - Thornhill,NF
DO  - 10.1109/ECC.2016.7810424
EP  - 1030
PB  - IEEE Conference Publications
PY  - 2017///
SP  - 1025
TI  - Challenges of the application of data-driven models for the real-time optimization of an industrial air separation plant
UR  - http://dx.doi.org/10.1109/ECC.2016.7810424
UR  - http://ieeexplore.ieee.org/document/7810424/
UR  - http://hdl.handle.net/10044/1/50032
ER  -

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