Energy Consumption Optimization of Crude Oil Delivery System Based on Improved Genetic Algorithm

Xiao-qin XIONG , Xiao-kai XING , Bo WANG , Patigul YARMAMAT , Sheng-yu CAO , Ning XU

Science Technology and Engineering ›› 2026, Vol. 26 ›› Issue (2) : 582 -588.

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Science Technology and Engineering ›› 2026, Vol. 26 ›› Issue (2) : 582-588 DOI: 10.12404/j.issn.1671-1815.2500502
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Energy Consumption Optimization of Crude Oil Delivery System Based on Improved Genetic Algorithm
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Abstract

Aiming at the problem of high energy consumption and high cost caused by determining the pipeline operation scheme based on scheduling experience, the thermal and hydraulic models of the crude oil pipeline transportation system were first analyzed under the premise of knowing the basic overview of the crude oil system and the physical properties of the crude oil products. In order to achieve the goal of minimum energy consumption in pipeline operation, an energy consumption optimization model was established with pump combination, heating furnace combination, oil transfer pressure and oil transfer temperature as optimization variables. On this basis, the coding scheme of pump group and temperature was proposed, and the improved genetic algorithm was used to solve the energy consumption optimization model. Comparing with the actual working conditions, it is found that the optimized operation scheme of the two crude oil transmission pipelines B-W and K-W can reduce the energy consumption cost by 9% to 19%. In addition, the optimized operation scheme also analyzed the effects of different months of the year on the energy consumption of different pipelines. The results of the study can provide a theoretical basis for the development and implementation of energy-saving operation schemes for crude oil pipelines.

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energy consumption / crude oil pipeline / optimization / improve genetic algorithm / energy conservation

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Xiao-qin XIONG, Xiao-kai XING, Bo WANG, Patigul YARMAMAT, Sheng-yu CAO, Ning XU. Energy Consumption Optimization of Crude Oil Delivery System Based on Improved Genetic Algorithm. Science Technology and Engineering, 2026, 26(2): 582-588 DOI:10.12404/j.issn.1671-1815.2500502

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