MINLP framework for systems analysis of the chemical manufacturing industry using network models

Skouteris, Alkiviadis; Giannikopoulos, Ioannis; Allen, David T.; Bâldea, Michael; Stadtherr, Mark A.

doi:10.1016/b978-0-323-95879-0.50158-2

Cited by 1 publication

(1 citation statement)

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“…We claim no guarantee that the SLP approach will find a global optimum to the nonlinear problem. However, in recent preliminary studies comparing the SLP approach to an MINLP problem formulation with standard solvers, we find empirical evidence that globally optimal solutions are found.…”

Section: Industry Network Modelingmentioning

confidence: 81%

Probing the Impact of an Energy and Transportation Paradigm Shift on the Petrochemicals Industry

Giannikopoulos

Skouteris

Edgar

et al. 2022

Ind. Eng. Chem. Res.

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A paradigm shift in energy and transportation, away from fossil fuels and toward renewables, has begun. Driven by climate and sustainability considerations, consumer preferences and corporate aspirations favoring electric vehicles, powered by electricity generated renewably, are accelerating. As a result, the long-term future outlook for fossil-based fuels and the petroleum refining industry is subject to significant uncertainty. Since the production of petrochemicals is closely connected to petroleum refining, it is also likely to be impacted by this paradigm shift. In this paper, these potential impacts are probed using an optimization-based, network superstructure model of the U.S. petrochemicals industry. The model is used to investigate the response of the industry under extreme-case scenarios involving complete loss of demand for liquid transportation fuels and/or complete loss of crude oil supply and petroleum refining capacity. The model is also used to perform an assessment of new, natural-gas-based technologies that might be implemented in the context of a residual, limited-demand market for liquid transportation fuels. The production of green hydrogen for use in this context is also considered.

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Section: Industry Network Modelingmentioning

confidence: 81%