Research on optimization operation technology of qt oil pipeline based on the heuristic algorithm

Liu, Enbin, Lai, PinRong, Peng, Yong and Chen, Qikun 2022. Research on optimization operation technology of qt oil pipeline based on the heuristic algorithm. Energy Reports 8 , pp. 10134-10143. 10.1016/j.egyr.2022.08.010

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Abstract

Global environmental problems have become increasingly prominent, and China, as one of the world’s major powers, should take action. China promises to achieve a “carbon peak” by 2030, and carbon dioxide emissions will no longer increase, and will gradually decrease after reaching the peak. To achieve “carbon neutrality” by 2060, all the carbon dioxide emissions will be offset by tree planting, energy saving and emission reduction. The optimization of pipeline energy consumption is also associated with it. In recent years, the transportation mode of the QT oil pipeline has changed from normal temperature transportation to heating transportation. The energy consumption of this transportation method mainly comes from heating furnaces and pumps. In order to reduce energy consumption and find a suitable pipeline operation plan, this article optimizes and analyzes the transformed QT oil pipeline under the premise of ensuring safe production. Based on programming software, this article establishes a corresponding mathematical model of energy consumption of the QT oil pipeline, and uses artificial bee colony algorithm, invasive weed algorithm and optimization algorithm based on biogeography to solve the model. The article innovatively introduces the speed of the variable frequency pump as a variable to study the energy consumption optimization problem of the oil pipeline, analyze the practical application of the oil pipeline of the QT oil pipeline, and obtains the best plan for the optimized operation of the oil pipeline of the QT oil pipeline It provided research basis and played a role in promoting the country’s dual-carbon goals.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	2352-4847
Date of First Compliant Deposit:	14 October 2022
Date of Acceptance:	2 August 2022
Last Modified:	05 May 2023 12:44
URI:	https://orca.cardiff.ac.uk/id/eprint/153178

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