Operational optimization of a cyclic gas pipeline network with consideration of thermal hydraulics

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F21%3APU143865" target="_blank" >RIV/00216305:26210/21:PU143865 - isvavai.cz</a>

Výsledek na webu

<a href="https://pubs-acs-org.ezproxy.lib.vutbr.cz/doi/abs/10.1021/acs.iecr.0c04007" target="_blank" >https://pubs-acs-org.ezproxy.lib.vutbr.cz/doi/abs/10.1021/acs.iecr.0c04007</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1021/acs.iecr.0c04007" target="_blank" >10.1021/acs.iecr.0c04007</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Operational optimization of a cyclic gas pipeline network with consideration of thermal hydraulics

Popis výsledku v původním jazyce

Following the rapidly increasing global demand for natural gas, many countries are launching projects to expand gas pipeline networks (GPNs). As a result, more cyclic GPNs are under construction with more rigorous physical constraints required, bringing new challenges to GPN optimization. This paper proposes a novel nonconvex mixed-integer nonlinear programming (MINLP) formulation for operational optimization of the cyclic GPN with simultaneous consideration of thermal hydraulics and flow direction reversibility, which has not been explored in the literature. To solve the proposed MINLP model, a three-level decomposition algorithm is proposed to generate an approximate solution, from which the flow direction is extracted and used to fix all discrete variables in the original MINLP model to construct two-stage NLP models. The NLP models are then solved to improve solution feasibility and quality. The computational results show that the proposed approach outweighs several state-of-the-art commercial MINLP solvers with better solutions and shorter computational time.

Název v anglickém jazyce

Operational optimization of a cyclic gas pipeline network with consideration of thermal hydraulics

Popis výsledku anglicky

Following the rapidly increasing global demand for natural gas, many countries are launching projects to expand gas pipeline networks (GPNs). As a result, more cyclic GPNs are under construction with more rigorous physical constraints required, bringing new challenges to GPN optimization. This paper proposes a novel nonconvex mixed-integer nonlinear programming (MINLP) formulation for operational optimization of the cyclic GPN with simultaneous consideration of thermal hydraulics and flow direction reversibility, which has not been explored in the literature. To solve the proposed MINLP model, a three-level decomposition algorithm is proposed to generate an approximate solution, from which the flow direction is extracted and used to fix all discrete variables in the original MINLP model to construct two-stage NLP models. The NLP models are then solved to improve solution feasibility and quality. The computational results show that the proposed approach outweighs several state-of-the-art commercial MINLP solvers with better solutions and shorter computational time.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20704 - Energy and fuels

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

ISSN

0888-5885

e-ISSN

1520-5045

Svazek periodika

6

Číslo periodika v rámci svazku

60

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

22

Strana od-do

2501-2522

Kód UT WoS článku

000621056900016

EID výsledku v databázi Scopus

2-s2.0-85101011602