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@ARTICLE{Langiu:917584,
author = {Langiu, Marco and Dahmen, Manuel and Mitsos, Alexander},
title = {{S}imultaneous optimization of design and operation of an
air-cooled geothermal {ORC} under consideration of multiple
operating points},
journal = {Computers $\&$ chemical engineering},
volume = {161},
issn = {0098-1354},
address = {Amsterdam [u.a.]},
publisher = {Elsevier Science},
reportid = {FZJ-2023-00786},
pages = {107745 -},
year = {2022},
abstract = {We simultaneously optimize both the design and operation of
an air-cooled geothermal organic Rankine cycle, maximizing
total annual return (TAR), while considering multiple
operating scenarios based on different ambient temperatures.
In order to accurately capture realistic off-design behavior
of the heat exchangers and turbine, as well as for the
overall system, we incorporate component models that
consider performance variations with both size and operating
conditions. We employ a hybrid mechanistic data-driven
modeling approach, involving artificial neural networks
(ANNs) as surrogate models for accurate fluid properties, as
well as for intermediate expressions for which ANNs improve
tractability of the optimization problem. We demonstrate the
importance of considering multiple operating conditions
within design problems and propose a methodology for
formulating and solving such problems globally, using our
open-source solver MAiNGO.},
cin = {IEK-10},
ddc = {660},
cid = {I:(DE-Juel1)IEK-10-20170217},
pnm = {1121 - Digitalization and Systems Technology for
Flexibility Solutions (POF4-112)},
pid = {G:(DE-HGF)POF4-1121},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000806393100007},
doi = {10.1016/j.compchemeng.2022.107745},
url = {https://juser.fz-juelich.de/record/917584},
}