Trajectory optimization of an oscillating industrial two-stage evaporator utilizing a Python-Aspen Plus Dynamics toolchain

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Bibtex

@article{c3634d7a564d4cdab291f91e0405ccf1,
title = "Trajectory optimization of an oscillating industrial two-stage evaporator utilizing a Python-Aspen Plus Dynamics toolchain",
abstract = "Evaporators are integral parts of many separation processes across production industries, and they need to be well understood in order to be operated well, thereby enabling high resource-efficiency and productivity. In a previous investigation, the effects of disturbing oscillations in a two-stage evaporator system were quantified. In the current study, these oscillations were reduced through trajectory optimization using steam consumption as a temporally discretized decision variable, taking advantage of a dynamic process flowsheet model in Aspen Plus Dynamics (APD) employed as if it were a black-box model. The optimization was performed utilizing a Python-APD toolchain with the SciPy implementation of COBYLA. The optimal trajectory was able to successfully reduce the objective function value (including the product stream mass flow variance and a bang-bang penalty on the trajectory itself) to slightly less than 0.3 {\%} of that of the nominal case, in which a time-invariant steam consumption was employed. This in turn grants opportunities to increase throughput of the process, leading to significant financial gains.",
keywords = "Aspen Plus Dynamics, Derivative-free optimization, Dynamic optimization, Evaporator system, Oscillations, Python",
author = "Mikael Yamanee-Nolin and Niklas Andersson and Bernt Nilsson and Mark Max-Hansen and Oleg Pajalic",
year = "2020",
month = "3",
day = "1",
doi = "10.1016/j.cherd.2019.12.015",
language = "English",
volume = "155",
pages = "12--17",
journal = "Chemical Engineering Research & Design",
issn = "0263-8762",
publisher = "IChemE",

}